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Progress in research on the joint inversion for soil moisture using multi-source satellite remote sensing data
JIANG Ruirui, GAN Fuping, GUO Yi, YAN Bokun
Remote Sensing for Natural Resources    2024, 36 (1): 1-13.   DOI: 10.6046/zrzyyg.2022408
Abstract300)   HTML15)    PDF (3160KB)(248)      

Soil moisture is closely associated with global climate change, the carbon cycle, and the water cycle, as well as agricultural production and ecological conservation and restoration. The detection of soil moisture has shifted from ground survey to remote sensing detection, achieving global- and regional-scale survey and monitoring. Given differences in data spectrum segments, radiative transfer mechanisms, and inversion algorithms, it is necessary to comprehensively analyze the mechanisms, advantages, and limitations of algorithms, with the purpose of laying a foundation for accuracy and algorithm improvement. From the aspects of optical remote sensing, microwave remote sensing, and optic-microwave cooperation, this study systematically analyzed the features and challenges of the following inversion techniques: inversion based on the Ts-VI spatial and Ts-NSSR temporal characteristics of optical remote sensing data, inversion using passive and active microwave data, joint inversion using active and passive microwave data and remote sensing data, and optical-microwave cooperative inversion based on accuracy improvement and spatio-temporal transformation. At present, the joint inversion of soil moisture using multi-source remote sensing data faces the following challenges: ① The data suffer missing and spatio-temporal mismatching; ② Different data sources exhibit varying degrees of surface penetration; ③ The joint inversion model relies on empirical parameters and numerous auxiliary parameters. These challenges can be addressed with the improvement in the satellite monitoring network, the increase in the surface detection depths of data sources, the clarification of the physical mechanisms of joint inversion, and the establishment of spatio-temporal continuous datasets of auxiliary parameters.

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Remote sensing ecological index (RSEI) model and its applications: A review
CHEN Yixin, NING Xiaogang, ZHANG Hanchao, LAN Xiaoqiang, CHANG Zhongbing
Remote Sensing for Natural Resources    2024, 36 (3): 28-40.   DOI: 10.6046/zrzyyg.2023128
Abstract250)   HTML3)    PDF (2192KB)(224)      

In the context of achieving peak carbon dioxide emissions and carbon neutrality, conducting a remote sensing-based ecological assessment and monitoring analysis is greatly significant for ascertaining the ecological condition in time and formulating scientific and reasonable ecological protection policies. The early remote sensing-based ecological assessment indices, simple and involving complex processes, are difficult to find wide applications. In contrast, the remote sensing ecological index (RSEI), contributing to elevated assessment efficiency, has been extensively used. To gain a deeper understanding of RSEI, this study describes its background, calculation method, and research status and provides a summary of the current issues and regional adjustments. Furthermore, it analyzes the main application directions of RSEI, namely the in-depth analyses of regional ecological assessment and change monitoring. Finally, the study proposes that despite a broad space for RSEI development, it is necessary to conduct research into the spatiotemporal scales of images, storage and batch processing capabilities, model adaption, and intelligentization.

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Advances in research on the dynamic monitoring of global vegetation based on the vegetation optical depth
YANG Ni, DENG Shulin, FAN Yanhong, XIE Guoxue
Remote Sensing for Natural Resources    2024, 36 (2): 1-9.   DOI: 10.6046/zrzyyg.2023059
Abstract236)   HTML14)    PDF (1206KB)(198)      

The vegetation optical depth (VOD) serves as a microwave-based method for estimating vegetation water content and biomass. Compared to optical remote sensing, the satellite-based VOD, exhibiting a lower sensitivity to atmospheric disturbances, can measure the characteristics and information of vegetation in various aspects, thus providing an independent and complementary data source for global vegetation monitoring. It has been extensively applied to investigate the effects of global climate and environmental changes on vegetation. Discerning the research advances of VOD application in the dynamic monitoring of global vegetation is critical for VOD’s further development and application. Hence, this study first presented the primary methods for obtaining the VOD through inversion of passive and active microwave data, comparatively analyzing the principal characteristics of various sensor VOD products. Then, this study generalized the current research advances of VOD in the dynamic monitoring of vegetation in terms of vegetation characteristic monitoring (like vegetation water content and biomass), carbon balance analysis, drought monitoring, and phenological analysis. Finally, this study expounded the advantages, limitations, and improvement approaches of VOD products, envisioning the application prospect of VOD in the dynamic monitoring of vegetation.

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Deep learning-based cloud detection method for multi-source satellite remote sensing images
DENG Dingzhu
Remote Sensing for Natural Resources    2023, 35 (4): 9-16.   DOI: 10.6046/zrzyyg.2022317
Abstract221)   HTML90)    PDF (4339KB)(230)      

Cloud detection, as a crucial step in preprocessing optical satellite images, plays a significant role in the subsequent application analysis. The increasingly enriched optical satellite remote sensing images pose a challenge in achieving quick cloud detection of numerous multi-source satellite remote sensing images. Given that conventional cloud detection exhibits low accuracy and limited universality, this study proposed a multi-scale feature fusion neural network model, i.e., the multi-source remote sensing cloud detection network (MCDNet). The MCDNet comprises a U-shaped architecture and a lightweight backbone network, and its decoder integrates multi-scale feature fusion and a channel attention mechanism to enhance model performance. The MCDNet model was trained using tens of thousands of globally distributed multi-source satellite images, covering commonly used satellite data like Google and Landsat data and domestic satellite data like GF-1, GF-2, and GF-5 data. Several classic semantic segmentation models were used for comparison with the MCDNet model in the experiment. The experimental results indicate that the MCDNet model exhibited superior performance in cloud detection, achieving detection accuracy of over 90% for all types of satellite data. Additionally, the MCDNet model was tested on the Sentinel data that were not used in training, yielding satisfactory cloud detection effects. This demonstrates the MCDNet model’s robustness and potential for use as a general model for cloud detection of medium- to high-resolution satellite images.

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Application of high-resolution InSAR technique in monitoring deformations in the Beijing Daxing International Airport
ZHAO Xia, MA Xinyan, YU Qian, WANG Zhaobing
Remote Sensing for Natural Resources    2024, 36 (1): 49-57.   DOI: 10.6046/zrzyyg.2022381
Abstract213)   HTML3)    PDF (24086KB)(152)      

The Beijing Daxing International Airport, located in the Yufa—Lixian area of Daxing District, is one of Beijing’s five major land subsidence areas. Differential deformations pose risks to the airport’s safe and stable operation. By applying the time-series interferometric synthetic aperture Radar (InSAR) technique, this study obtained the spatio-temporal characteristics of the airport’s deformations from 39 scenes of high-resolution COSMO-SkyMed (CSK) SAR images taken from September 2019 to November 2021. The monitoring results, with high accuracy, are roughly consistent with level monitoring results. Findings indicate that the airport’s subsidence lasted from 2019 to 2021, with the highest subsidence rate measured at -47.5 mm/a and a maximum cumulative subsidence amount of -103.84 mm. Notably, all four runways exhibited varying degrees of differential subsidence. Furthermore, this study delved into the spatio-temporal characteristics of deformations in the runways, as well as deformations in other high-deformation zones such as terminal buildings, maintenance aprons, oil tank areas, and the business jet apron. By combining the foundation treatment, this study analyzed the factors influencing the airport’s subsidence, providing a reference for the airport’s safe and stable operation.

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A review of the estimation methods for daily mean temperatures using remote sensing data
WANG Yan, WANG Licheng, WU Jinwen
Remote Sensing for Natural Resources    2023, 35 (4): 1-8.   DOI: 10.6046/zrzyyg.2022338
Abstract190)   HTML129)    PDF (864KB)(274)      

Daily mean temperatures, as a primary indicator reflecting climatic characteristics, play a decisive role in monitoring urban heat island effects and agroecological environments. However, daily mean temperatures measured at meteorological stations lack spatial representativeness in regional-scale models. By contrast, the inversion results of daily mean temperatures using remote sensing data can better accommodate the large-scale monitoring needs, but with insufficient accuracy and quality. This study presented several common estimation methods for daily mean temperatures using remote sensing data, including multiple linear regression, machine learning, and feature space-based extrapolation. Then, based on the principle and process for estimation of daily mean temperatures using remote sensing data, this study systematically analyzed the effects of uncertainties such as clouds and aerosols and offered corresponding solutions. Finally, this study predicted the development trend of such estimation methods. Additionally, this study posited that image fusion and multi-source data fusion at different transit times can significantly improve the estimation accuracy under cloud interference.

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Identifying and monitoring tailings ponds by integrating multi-source geographic data and high-resolution remote sensing images: A case study of Gejiu City, Yunnan Province
LIU Xiaoliang, WANG Zhihua, XING Jianghe, ZHOU Rui, YANG Xiaomei, LIU Yueming, ZHANG Junyao, MENG Dan
Remote Sensing for Natural Resources    2024, 36 (1): 103-109.   DOI: 10.6046/zrzyyg.2022480
Abstract188)   HTML1)    PDF (7058KB)(190)      

Tailings ponds are considerable hazard sources with high potential energy. Ascertaining the number and distribution of tailings ponds in a timely manner through rapid identification and monitoring of their spatial extents is critical for the environmental supervision and governance of tailings ponds in China. Due to the lack of pertinence for potential targets, identifying tailings ponds based on solely remote sensing images is prone to produce confusion between tailings ponds and exposed surfaces, resulting in significant errors in practical applications. This study proposed an extraction method for tailings ponds, which integrated enterprise directory, multi-source geographic data (e.g., data from spatial distribution points, digital elevation model (DEM), and road networks), and high-resolution remote sensing images. The application of this method in Gejiu City, Yunnan Province indicates that integrating multi-source geographic data can effectively exclude the interferential areas without tailings ponds, with the precision and recall rates of the extraction results reaching 83.9% and 72.4%, respectively. The method proposed in this study boasts significant application prospects in high-frequency and automated monitoring of tailings ponds nationwide.

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Post-flood recovery assessment based on multi-source remote sensing data:A case study of the “7·20” rainstorm in Henan
LI Mengqi, LI Gongquan, XIE Zhihui
Remote Sensing for Natural Resources    2024, 36 (1): 250-266.   DOI: 10.6046/zrzyyg.2022389
Abstract183)   HTML2)    PDF (32274KB)(173)      

Quantitative post-flood recovery assessment based on vegetation and lighting indices is critical for assessing economic reconstruction and ecological restoration in afflicted areas. This study investigated the “7.20” rainstorm disaster area in Henan. Based on the daily and monthly NPP-VIIRS data, Sentinel-NDVI and MODIS-EVI data, and statistical yearbook data, this study characterized the spatial intricacies within urban areas by constructing a normalized difference urban index (NDUI). Then, it simulated the population and GDP distributions by employing a regression model. Finally, this study assessed the post-flood recovery from two distinct aspects: nighttime light data and vegetation cover data. The results are as follows: ① High- and medium-risk zones covered an area of 1 429.04 km2, accounting for 6.06% of the total study area. High-risk zones were primarily distributed in western Zhengzhou, eastern Xinxiang, eastern Anyang, and northern Hebi, with Zhengzhou suffering the most severe impact; ② In terms of the vegetation cover recovery rate (VCRR), low overall vegetation recovery was observed in Weihui and Linzhou cities and Qixian and Huaxian counties, with VCRRs mostly below 0. This indicates a deteriorating vegetation cover trend; ③ The fitting between NDUI and socio-economic statistical data yielded accuracy exceeding 0.8, suggesting that the NDUI can be applied to precise location-based rescue and targeted post-disaster reconstruction in the aftermath of floods. Additionally, the assessment results based on NPP-VIIRS and MODIS-EVI data were highly complementary, implying that the flood research based on the integration of the two types of data enjoys high application value for post-disaster rescue and recovery assessment.

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Crops identification based on Sentinel-2 data with multi-feature optimization
CHEN Jian, LI Hu, LIU Yufeng, CHANG Zhu, HAN Weijie, LIU Saisai
Remote Sensing for Natural Resources    2023, 35 (4): 292-300.   DOI: 10.6046/zrzyyg.2022272
Abstract173)   HTML12)    PDF (3736KB)(235)      

Focusing on Quanjiao County in Chuzhou City, this study determined 90 features, including spectral, traditional vegetation index, red-edge vegetation index, and texture features, from Sentinel-2 satellite data on the GEE platform. This study examined the effects of diverse feature optimization algorithms combined with a random forest classifier on identifying crop planting types in the study area. These algorithms included the random forest-recursive feature elimination (RF_RFE) algorithm, the Relief F algorithm based on Relief expansion, and the correlation-based feature selection (CFS) algorithm. On this basis, this study further analyzed the classification effects of the optimal feature optimization algorithm in various machine learning classification approaches. The study demonstrates that: ① Spectral features proved to be the most crucial for crop identification, followed by red-edge index features, and texture features manifested minimal effects; ② RF_RFE-based remote sensing identification results exhibited the highest accuracy, with overall accuracy of 92% and a Kappa coefficient of 0.89; ③ Under the RF_RFE feature optimization method, the RF’s Kappa coefficient was 0.01 and 0.41 higher than that of the support vector machine (SVM) and the minimum distance classification (MDC), respectively. This indicates that the RF_RFE feature optimization method based on multiple features, combined with the RF algorithm, can effectively enhance the accuracy and efficiency of remote sensing identification of crops.

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A remote sensing-based study on change in land use and vegetation cover in Xiong’an New Area from 1991 to 2021
CUI Dunyue, WANG Shidong, ZHANG Xuejun
Remote Sensing for Natural Resources    2023, 35 (4): 214-225.   DOI: 10.6046/zrzyyg.2022311
Abstract163)   HTML11)    PDF (7174KB)(162)      

This study aims to analyze the changes in the land use and vegetation cover in the Xiong'an New Area from 1991 to 2021. To this end, this study explored the characteristics of the land use changes in the area over the 30 years based on the Landsat TM\OLI data of five periods using the GIS technology and map fusion method. Then, it extracted the vegetation cover information using the dimidiate pixel model and analyzed the changes in the vegetation cover. Furthermore, this study explored the potential factors driving the vegetation cover change in the area using the geographic detector model and analyzed the impact of land use change on vegetation cover change by referencing the existing map fusion method. The results show that: ① From 1991 to 2021, the construction land in Xiong’an New Area increased by 108.09 km2, primarily transformed from farmland and other types of land; other types of land reduced by 108.17 km2, predominantly transformed to farmland; forestland and grassland increased by 11.56 km2, mainly transformed from water areas and other types of land; the water area decreased by 38.76 km2, mainly transformed to farmland and other types of land; and the area of farmland roughly remained unchanged; ② Over the 30 years, the Xiong’an New Area generally exhibited high vegetation coverage, and the area with moderate and high vegetation coverage and above accounted for more than 50.00%. The vegetation coverage in the Xiong’an New Area presented an overall spatial distribution pattern characterized by high in Anxin County, moderate in Rongcheng County, and low in Xiong County. Regarding the phased changes, this area showed a degradation trend from 1991 to 2001, and the area with degraded vegetation cover accounted for 39.15%. From 2001 to 2021, this area exhibited an improvement trend, the area with improved vegetation cover accounted for up to 47.55%; ③ The vegetation cover change showed spatial differentiation, significantly affected by the population density, GDP, soil type, and soil quality but slightly affected by the elevation and slope. The transformation of construction land and other types of land to farmland acted as an important reason for the improvement in vegetation cover, while the transformation of farmland to construction land and other types of land served as an important reason for vegetation degradation. The results of this study can, to some extent, provide a scientific basis and suggestions for the sustainable development of Xiong’an New Area.

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Deformation monitoring and analysis of mining areas based on the DT-SDFPT combined time-series InSAR
YU Bing, WANG Bing, LIU Guoxiang, ZHANG Guo, HU Yunliang, HU Jinlong
Remote Sensing for Natural Resources    2024, 36 (1): 14-25.   DOI: 10.6046/zrzyyg.2022378
Abstract160)   HTML5)    PDF (20899KB)(194)      

High-intensity coal mining leads to significant surface deformation and secondary geological disasters. Synthetic aperture Radar interferometry (InSAR), exhibiting high deformation monitoring capability, fails to detect enough target pixels in the mining core and surrounding low-coherence areas. This study intends to increase the density and coverage of deformation monitoring points in mining areas by combining distributed targets (DTs) and slowly-decorrelating filtered phase targets (SDFPTs). First, DT and SDFPT candidate pixels were selected using the fast statistically homogenous pixel selection (FaSHPS) method and the amplitude dispersion index method, respectively for phase optimization and stability analysis. Then, qualified DT and SDFPT pixels were screened out to constitute a fused pixel set, which was subjected to three-dimensional phase unwrapping, phase time series recovery, and spatio-temporal filtering. Consequently, the deformation time series and the annual average deformation rate were determined based on the fused pixel set. Finally, the method proposed in this study was applied to monitor the deformation in the Buertai coal mine using 60 scenes of Sentinel-1 images covering the coal mine from April 2018 to April 2020. The results reveal a significant increase in the density and coverage of deformation points through the integration of DT and SDFPT, thus allowing for the monitoring of higher levels of maximum deformation. Within the experimental area, five deformation cones were identified, with the maximum cumulative deformation amplitude reaching -309.76 mm. The influencing range of the deformations and the difference in the deformation amplitude of the time series in different years are closely related to mining activities.

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InSAR-based monitoring and analysis of Menyuan earthquake-induced surface deformations
JIN Xintian, WANG Shijie, ZHANG Lanjun, GAO Xingyue
Remote Sensing for Natural Resources    2024, 36 (1): 26-34.   DOI: 10.6046/zrzyyg.2022497
Abstract159)   HTML1)    PDF (13990KB)(165)      

Earthquake-induced surface deformations are characterized by large scales and extensive coverage, and the resultant secondary geological disasters significantly impact local infrastructure and engineering construction. Investigating the surface deformations caused by the Menyuan earthquake is critical for understanding the seismic deformation movement and identifying potential geological disasters. This study obtained the coseismic deformation field of the Menyuan earthquake using the differential interferometric synthetic aperture Radar (D-InSAR) technique. Based on the geometric relationships between the ascending descending passes, this study extracted the two-dimensional information of surface deformations induced by the Menyuan earthquake. The results show that the coseismic deformations occurred primarily at the intersection of Lenglongling and Tuolaishan faults. The line-of-sight (LOS) surface deformations from ascending and descending passes exhibited uplift of 0.40 m and 0.80 m and subsidence of -0.65 m and -0.70 m, respectively. As indicated by the analysis of two-dimensional deformation based on the ascending and descending LOS surface deformation results, the maximum amplitude of vertical deformations dominated by subsidence was -0.32 m and the maximum amplitude of horizontal deformation dominated by eastward motion was 0.87 m, suggesting significant horizontal seismic deformations and fault activity dominated by left-lateral strike-slip process. Based on the 21 scenes of Sentinel-1A SAR images covering the study area taken from the ascending pass, this study extracted the information on the surface deformations after the Mengyuan earthquake using the small baseline subset-interferometric synthetic aperture Radar (SBAS-InSAR) technique, determining the LOS time series and average deformation rates. The results show that from January 17, 2022 to September 26, 2022, the study area experienced relatively stable overall deformations and significant local deformations. The fault activity was identified as the primary factor affecting the surface deformations, with a maximum average deformation rate of 53 mm/a and a maximum deformation amplitude of 77 mm. The results of this study will provide technical support for earthquake disaster mitigation, emergency management, and sustainable socio-economic development.

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A review of water body extraction from remote sensing images based on deep learning
WEN Quan, LI Lu, XIONG Li, DU Lei, LIU Qingjie, WEN Qi
Remote Sensing for Natural Resources    2024, 36 (3): 57-71.   DOI: 10.6046/zrzyyg.2023106
Abstract148)   HTML3)    PDF (8040KB)(90)      

Timely and accurate detection and statistical analysis of the spatial distributions and time-series variations of water bodies like rivers and lakes holds critical significance and application value. It has become a significant interest in current remote sensing surface observation research. Conventional water body extraction methods rely on empirically designed index models for threshold-based segmentation or classification of water bodies. They are susceptible to shadows of surface features like vegetation and buildings, and physicochemical characteristics like sediment content and saline-alkali concentration in water bodies, thus failing to maintain robustness under different spatio-temporal scales. With the rapid acquisition of massive multi-source and multi-resolution remote sensing images, deep learning algorithms have gradually exhibited prominent advantages in water body extraction, garnering considerable attention both domestically and internationally. Thanks to the powerful learning abilities and flexible convolutional structure design schemes of deep neural network models, researchers have successively proposed various models and learning strategies to enhance the robustness and accuracy of water body extraction. However, there lacks a comprehensive review and problem analysis of research advances in this regard. Therefore, this study summarized the relevant research results published domestically and internationally in recent years, especially the advantages, limitations, and existing problems of different algorithms in the water body extraction from remote sensing images. Moreover, this study proposed suggestions and prospects for the advancement of deep learning-based methods for extracting water bodies from remote sensing images.

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Multi-class change detection using a multi-task Siamese network of remote sensing images
MA Hui, LIU Bo, DU Shihong
Remote Sensing for Natural Resources    2024, 36 (1): 77-85.   DOI: 10.6046/zrzyyg.2022446
Abstract145)   HTML1)    PDF (8701KB)(197)      

The accurate acquisition of land cover/use changes and their types is critical to territorial space planning, ecological environment monitoring, and disaster assessment. However, most current studies on the change detection focus on binary change detection. This study proposed a multi-class change detection method using a multi-task Siamese network of remote sensing images. First, an object-oriented unsupervised change detection method was employed to select areas that were most/least prone to change in the new and old temporal images. These areas were used as samples for the multi-task Siamese network. Subsequently, the multi-task Siamese network model was used to learn and predict the new and old temporal land-use maps and binary change maps. Finally, the final multi-class change detection results were derived from these maps. The multi-task Siamese network was tested based on the images from the Third National Land Survey and corresponding land-use maps. The results demonstrate that the method proposed in this study is applicable to the change detection cases where changed and unchanged samples lack but there are available historical thematic maps.

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Identifying predominant tree species based on airborne hyperspectral images using machine learning algorithms
YU Hang, TAN Bingxiang, SHEN Mingtan, HE Chenrui, HUANG Yifei
Remote Sensing for Natural Resources    2024, 36 (1): 118-127.   DOI: 10.6046/zrzyyg.2022383
Abstract140)   HTML2)    PDF (12795KB)(118)      

Identifying forest tree species can provide a valuable scientific reference for ascertaining forest resources. However, it is difficult to achieve accurate tree species classification even using hyperspectral data with high spatial resolution. Hence, there is an urgent need to meet this challenge. This study investigated the Genhe Forest Reserve in the Great Xing’an Range within Inner Mongolia. At spatial resolutions of 1 m and 3 m, two sample value scales were employed: sample points (i.e., the spectral values of pixels corresponding to sample plots) and sample planes (i.e., the average spectral values of pixels in a 3×3 window corresponding to sample plots). Then, this study explored the identification effects of predominant tree species using airborne hyperspectral images based on three machine learning algorithms: neural network (NN), three-dimensional convolution neural network (3DCNN), and support vector machine (SVM). Key findings include: ① Regardless of spatial resolution and sample value scales, the 3DCNN exhibited the highest classification accuracy, yielding the highest overall accuracy and Kappa coefficient of 95.42% and 0.94, respectively; ② Compared to a low spatial resolution (3 m), a high spatial resolution was more favorable to the identification of predominant tree species, with overall accuracy and Kappa coefficient increased by 30.97% and 54.24% at most, respectively; ③ In the case of NN/SVM-based classification, sample points outperformed sample planes in improving the accuracy of tree species identification. In contrast, sample planes outperformed sample points for 3DCNN-based classification at a spatial resolution of 3 m. Overall, spatial resolution, sample value scales, and classification algorithms manifested varying degrees of effects on the identification accuracy of predominant tree species. High-spatial-resolution images, small-sample data, and deep-learning algorithms can be combined to enhance the accuracy of predominant tree species identification using airborne hyperspectral images.

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Dynamic analysis of landslide hazards in the Three Gorges Reservoir area based on Google Earth Engine
SONG Yingxu, ZOU Yujia, YE Runqing, HE Zhixia, WANG Ningtao
Remote Sensing for Natural Resources    2024, 36 (1): 154-161.   DOI: 10.6046/zrzyyg.2022464
Abstract139)   HTML0)    PDF (7247KB)(135)      

Conventional remote sensing monitoring techniques, constrained by data availability and computational capacity, often fall short of the research requirements of extensive landslide disaster monitoring. This study established a dynamic assessment model for landslide hazards in the Three Gorges Reservoir area based on cloud computing platform Google Earth Engine (GEE), achieving dynamic assessment of landslide hazards in the area under the support of the massive data storage and robust computational capabilities of GEE. First, based on factors such as slope, slope aspect, normalized difference vegetation index (NDVI), normalized differential water index (NDWI), and geological structures, a landslide susceptibility zone map was established using a weighted gradient boosting decision tree (WGBDT) model. Then, the rainfall threshold inducing landslides in the Three Gorges Reservoir area was determined based on the Global Precipitation Measurement (GPM) data from the National Aeronautics and Space Administration (NASA). Subsequently, the rainfall classification criteria and a landslide hazard assessment model were established by combining rainfall and landslide susceptibility. Finally, focusing on the rainfall on August 31 in the Three Gorges Reservoir area, the daily distribution maps of landslide hazards in the Three Gorges Reservoir area were plotted, yielding the spatio-temporal variation trend of landslide hazards. In sum, the data processing and analysis tools of GEE allow for the analysis of landslide-related data of the Three Gorges Reservoir area, thus providing nearly real-time monitoring and early warning information for landslide hazards and offering a basis for the formulation of disaster prevention and mitigation policies.

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A method for sugarcane information extraction based on multi-feature optimal selection of Sentinel-1/2 image data
LU Xianjian, ZHANG Huanling, YAN Hongbo, LI Zhenbao, GUO Ziyang
Remote Sensing for Natural Resources    2024, 36 (1): 86-94.   DOI: 10.6046/zrzyyg.2022489
Abstract138)   HTML0)    PDF (12067KB)(142)      

The integration of multi-source remote sensing images and multi-feature parameters is effective in the accurate identification of target ground objects. However, excess feature parameters can cause data redundancy, reducing classification accuracy. Focusing on a sugarcane planting area with Karst landforms, this study extracted the spectral, index, texture, topographic, and polarization features of the ground objects in the study area from Sentinel-1/2 images and SRTM digital elevation data. The index features involved the red edge index calculated based on the red-edge band, which was scarce in data derived from remote sensing sensors, and the texture features included the Radar image textures. In the experiment, six schemes were designed to explore the effects of different image features and the random forest-based optimal feature association on sugarcane information extraction. The results show that for the classification of ground objects in the study area using spectral features combined with other feature types, the importance of the feature types ranked in descending order of spectral features, index features, texture features, topographic features, and polarization features. Among the six schemes, the scheme based on the random forest algorithm, integrating different feature variables, yielded the optimal information extraction effect for sugarcane, with both user and producer accuracy higher than 97%, overall accuracy of 95.49%, and a Kappa coefficient of 0.94.

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Distributions and existing problems of mining land of abandoned open-pit mines in China
XING Yu, WANG Jingya, YANG Jinzhong, CHEN Dong, DU Xiaomin, GUO Jingkai, SONG Licong
Remote Sensing for Natural Resources    2024, 36 (2): 21-26.   DOI: 10.6046/zrzyyg.2022440
Abstract137)   HTML6)    PDF (2255KB)(106)      

To obtain the fundamental data of mine environments objectively, this study monitored the damaged mining land and the ecological restoration land in abandoned open-pit mines in China by combining remote sensing data with multi-source data, computer automated information extraction with human-computer interactive interpretation, and comprehensive laboratory research with field investigation. The remote sensing monitoring in 2022 shows that the mining land of abandoned open-pit mines in China covered an area of 82.74×104 hm2, representing 0.86‰ of the national land area, primarily distributed in Inner Mongolia and Xinjiang Uygur autonomous regions as well as Hebei, Shandong, and Heilongjiang provinces. Among them, the damaged mining land and the ecological restoration land accounted for 50.74×104 hm2 and 32.00×104 hm2, respectively, with an ecological restoration rate of 38.68%. The mining land of abandoned open-pit mines occupied primary farmland of 2.63×104 hm2, representing 3.18% of the total mining area. The mining land of nationwide abandoned open-pit mines within the ecological red line accounted for 8.09×104 hm2, representing 9.77% of the total mining area. The mining land of nationwide abandoned open-pit mines, coinciding with the result of the third national land resource survey (mining land), totaled 30.13×104 hm2, representing 36.42% of the total mining area. This study preliminarily analyzed the present situation and existing problems of remote sensing work involving the mining land of nationwide abandoned open-pit mines, the occupation of primary farmland, the mining land of such mines within the ecological red line, and corresponding environmental restoration and governance. Finally, this study proposed countermeasures and suggestions in this regard.

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Monitoring and analyzing land subsidence in Qinfang, Guangxi based on Sentinel-1A data
MING Xiaoyong, TIAN Yichao, ZHANG Qiang, TAO Jin, ZHANG Yali, LIN Junliang
Remote Sensing for Natural Resources    2024, 36 (1): 35-48.   DOI: 10.6046/zrzyyg.2022370
Abstract136)   HTML0)    PDF (38732KB)(202)      

This study aims to lay the scientific foundation for regional disaster prediction, prevention, and control, as well as urban planning, by analyzing the spatio-temporal distribution, evolutionary patterns, and driving factors of land subsidence in the Qinfang area, Guangxi Province, China. Using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique, this study extracted information on land subsidence in the study area during 2018—2021 from 45 scenes of Sentinel-1A SAR images. By combining the geological setting, precipitation, land use, and road data and using methods such as GIS spatial analysis, mathematical statistics, remote sensing image classification, and change detection, this study conducted visual and quantitative analyses of the overall characteristics, spatio-temporal evolutionary trends, and influencing factors of land subsidence in the study area. The results show that: ① In the spatial dimension, the ground deformations, at rates ranging from -114.37 to 58.55 mm/a within the study area, exhibited extensive but significantly nonuniform distributions during 2018—2021. Consequently, three primary subsidence centers emerged in the central and southern urban areas of Qinnan District, Qinzhou Port, and the port area, with subsidence areas expanding southward annually; ② In the temporal dimension, the subsidence centers displayed an overall uneven subsidence trend over time. Besides, they exhibited periodic rebounds, with a maximum rebound amplitude of 18.4 mm; ③ In terms of influencing factors, primary factors causing land subsidence in the study area included urbanization, road density, tectonic movement, stratigraphy, precipitation, and sea level rise, which play a predominant role in the expansion and intensification of land subsidence.

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Integrated remote sensing-based hazard identification and disaster-causing mechanisms of landslides in Zayu County
CAI Jian’ao, MING Dongping, ZHAO Wenyi, LING Xiao, ZHANG Yu, ZHANG Xingxing
Remote Sensing for Natural Resources    2024, 36 (1): 128-136.   DOI: 10.6046/zrzyyg.2023313
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Zayu County, located in the southeastern portion of the Qinghai-Tibet Plateau, is characterized by a large area, significantly varying topographic and climatic characteristics, and frequent landslides. The hazard identification and early warning of landslides are critical to disaster prevention and mitigation in the county. Based on the data acquired from January 2020 to November 2022, including 162 scenes of Sentinel-1A Radar remote sensing images taken on ascending and descending passes and high-resolution optical remote sensing images, this study conducted hazard identification, cataloging, mapping, analysis, and assessment of active landslides in Zayu County using the integrated remote sensing (IRS) technique on the Google Earth platform. A total of 237 active landslide hazards were identified primarily along the Gongrigabuqu River (the western tributary of the Zayu River), Zayu River, both sides of the Nujiang River, and the eastern Zayu River to the western Nujiang River. As revealed by the statistical analysis of the interpretation results combined with quantitative factors such as topography (elevation, slope, lithology) and natural environment (rainfall, temperature), the landslides in Zuobu and Azha villages pose high disaster risks, necessitating further mitigation measures. With relatively accurate results, this study can serve as a reference for disaster prevention and mitigation in Zayu County.

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Remote sensing information extraction for mangrove forests based on multi-feature parameters: A case study of Guangdong Province
WANG Yumiao, LI Sheng, DONG Chunyu, YANG Gang
Remote Sensing for Natural Resources    2024, 36 (1): 95-102.   DOI: 10.6046/zrzyyg.2022482
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Accurate mangrove forest distribution information is critical to the conservation and management of mangrove forests. Despite extensive studies on the remote sensing mapping of mangrove forests, it is necessary to improve their mapping accuracy by effectively utilizing multi-source remote sensing features. First, this study designed 15 feature associations using temporal features, including spectral, scattering, texture, and terrain features, which were extracted from multi-source remote sensing data. Then, using a random forest model, it analyzed the accuracy of different feature associations in mangrove forest identification, obtaining the optimal feature association. Finally, this study mapped the 10-m-resolution mangrove forest distribution of Guangdong Province in 2021 based on platform Google Earth Engine (GEE). The results show that spectral features in winter exhibited the highest importance, with richer feature types corresponding to higher mapping accuracy. The optimal feature association yielded overall accuracy of 92.25% and a Kappa value of 0.91. Overall, this study extracted information on mangrove forests in Guangdong based on multi-feature parameters and the optimal feature association. The results of this study will provide a scientific reference for accurate mapping of mangrove forests on a large scale.

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Research advances and challenges in multi-label classification of remote sensing images
LIN Dan, LI Qiucen, CHEN Zhikui, ZHONG Fangming, LI Lifang
Remote Sensing for Natural Resources    2024, 36 (2): 10-20.   DOI: 10.6046/zrzyyg.2023027
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Multi-label classification of remote sensing images plays a fundamental role in remote sensing analysis. Parsing given remote sensing images to identify semantic labels can provide a significant technical basis for downstream computer vision tasks. With the continuously improved spatial resolution of remote sensing images, many remote sensing objects with different scales, colors, and shapes are distributed in various zones of images, posing high challenges to the multi-label classification task of remote sensing images. This study focuses on the multi-label classification of images in the field of remote sensing, summarizing and analyzing the frontier research advances in this regard. First of all, this study expounded the problem definition for the multi-label classification task of remote sensing images while generalizing the commonly used multi-label image datasets and model evaluation indicators. Furthermore, by systematically presenting the frontier progress in this field, this study delved into two key tasks in the multi-label classification of remote sensing images: feature extraction of remote sensing images and label feature extraction. Finally, based on the characteristics of remote sensing images, this study analyzed the current challenges of multi-label classification as well as subsequent research orientation.

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Monitoring of dynamic changes in water bodies of Henan Province based on time-series Sentinel-2 data
WEI Xin, REN Yu, CHEN Xidong, HU Qingfeng, LIU Hui, ZHOU Jing, SONG Dongwei, ZHANG Peipei, HUANG Zhiquan
Remote Sensing for Natural Resources    2024, 36 (2): 268-278.   DOI: 10.6046/zrzyyg.2022445
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Inland water bodies, as irreplaceable resources in ecosystems, play a vital role in climate change and regional water circulation. Scientifically and accurately monitoring the distribution and dynamic changes of water bodies is critical for ecosystem balance maintenance, sustainable human development, and early warning of floods and droughts. However, current research primarily focuses on the static monitoring of inland water bodies, lacking high-resolution monitoring of dynamic changes in water bodies. Hence, relying on the Google Earth Engine (GEE) cloud computing platform, this study monitored the dynamic changes of water bodies at a spatial resolution of 10 m, with the Sentinel-2 surface reflectance data in 2020 as the data source. First, the optimal water body monitoring features were selected by examining the features of typical land cover types in Sentinel-2 spectral bands and water indices. Then, an automatic extraction method for water body training datasets was proposed in conjunction with priori water body products, obtaining high-confidence water body training samples. Furthermore, the spectral angle (SA) and Euclidean distance (ED) methods were integrated based on the Dempster-Shafer (D-S) evidence theory model, and a SA-ED dynamic monitoring model for water bodies was developed combined with the extracted optimal water body monitoring features. Finally, the stability of the SA-ED model was tested with Henan Province as a study area, demonstrating that the SA-ED model can effectively monitor the dynamic changes in water bodies. The SA-ED model yielded an overall monitoring accuracy of 97.03% for water bodies in Henan Province, with user accuracy of 95.85% and producer accuracy of 95.17% for permanent water bodies, user and producer accuracies of 96.21% and 93.82% for seasonal water bodies, respectively. The results of this study provide a novel approach for the fine-resolution monitoring of dynamic changes in water bodies.

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Urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration and its urban heat island effect from 2000 to 2018
YAO Lingyun, WANG Li, NIU Zheng, YIN Ziqi, FU Yuwen
Remote Sensing for Natural Resources    2024, 36 (1): 162-168.   DOI: 10.6046/zrzyyg.2022490
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The urban heat island effect is closely linked with the well-being of urban residents. Rapid urbanization has further accelerated urban expansion. This is accompanied by an increasingly significant urban heat island effect, especially in cities of central and western China in the past 20 years. To explore the relationship between the expansion of cities and urban agglomerations and the changes in the urban heat island effect, this study analyzed the expansion and spatial form variation of cities in the Changsha-Zhuzhou-Xiangtan urban agglomeration using the Boyce-Clark shape index. The land surface temperatures were derived through inversion using the practical single-channel algorithm based on the Google Earth Engine (GEE) platform. The temperature zones with different grades were determined using the mean-standard deviation method, followed by the definition and extraction of the range of the urban heat island effect. The urban center and heat island center were extracted, and the variation trends of the relationship between urban expansion and urban heat island effect were analyzed using the center shift method. The results show that the changes in the urban heat island effect were consistent with the expansion of the urban agglomeration and its cities. The results lead to the following conclusions: ① After 2015, the Changsha-Zhuzhou-Xiangtan urban agglomeration entered a critical period of rapid development; ② Urban expansion is the primary cause of the increase in the area of urban heat island effect; ③ The urban heat island center roughly shares the same variation trend with the urban center, and the urban heat island range increases in the direction roughly consistent with the urban expansion direction.

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Spatio-temporal evolution and influencing factors of ecological environment quality in the Changsha-Zhuzhou-Xiangtan urban agglomeration
LI Guangzhe, WANG Hao, CAO Yinxuan, ZHANG Xiaoyu, NING Xiaogang
Remote Sensing for Natural Resources    2023, 35 (4): 244-254.   DOI: 10.6046/zrzyyg.2022371
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Accurately identifying the evolutionary trend and influencing factors of ecological environment quality in new urban agglomerations holds crucial significance for scientifically guiding urbanization and achieving sustainable development. Existing research on the spatio-temporal evolutionary characteristics of ecological environment quality in new urban agglomerations ignored the interactions of multiple factors on ecological environment quality. Based on the Google Earth Engine (GEE) platform, and long-time-series Landsat TM/OLI remote sensing images as the fundamental data source, this study delved into the spatio-temporal variations of ecological environment quality in the Changsha-Zhuzhou-Xiangtan urban agglomeration from 1990 to 2020 using methods including the remote sensing ecological index (RSEI), Sen’s slope estimator, and Mann-Kendall test. Moreover, the geographical detector was employed to quantitatively measure the effects of various factors on the urban agglomeration’s spatial heterogeneity. The results indicate that the Changsha-Zhuzhou-Xiangtan urban agglomeration exhibited generally high ecological environment quality, with a spatial distribution pattern of higher quality in marginal areas and lower quality in core areas. The average proportion of areas with ecological environment quality graded as “excellent” and “good” exceeds 60% in the urban agglomeration. The sustainable development strategy altered the urban sprawl in this urban agglomeration, leading to a decline followed by an increase in RSEI, with an inflection point in 2000. From 1990 to 2020, the ecological environment quality significantly deteriorated in central urban areas while improvement was observed in non-central urban areas. Physical and geographical conditions significantly influenced the ecological environment quality of the urban agglomeration in the early stages. With socio-economic progression, the influence of socio-economic factors like nighttime lighting on ecological environment quality gradually intensified, assuming a dominant role over time. Besides, the interactions among factors significantly enhanced the effects of individual factors on ecological environment quality. Before 2010, the interactions between human and natural factors exerted considerable influences on the ecological environment. After 2015, the interactions among human factors yielded more pronounced effects on ecological environment quality. These findings serve as a foundational guide for the integrated high-quality development of the Changsha-Zhuzhou-Xiangtan urban agglomeration and a reference for the advancement of other comparable urban agglomerations.

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Changes and spatial conflict measurement of land use in Urumqi City
TIAN Liulan, LYU Siyu, WU Zhaopeng, WANG Juanjuan, SHI Xinpeng
Remote Sensing for Natural Resources    2023, 35 (4): 282-291.   DOI: 10.6046/zrzyyg.2022341
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Identifying land use conflicts holds critical significance for sustainable socio-economic development and ecological civilization construction. Since Urumqi City is situated in the core region of the Silk Road Economic Belt, investigating the causes and manifestations of its land use conflicts arising from urban development, oasis agriculture, and ecological environment becomes an urgent and necessary task. With Urumqi as the study area, this study analyzed its land use characteristics in 2000, 2010, and 2020, as well as those in 2030 simulated from the FLUS model. Based on this analysis and the pressure-state-response (PSR) model, a land use conflict intensity measurement model was constructed to evaluate the land use conflicts over the four periods. Finally, a geographic detector was employed to quantitatively analyze the factors driving land use conflicts in the study area. The findings indicate that: ① The land use between 2000 and 2030 exhibited significant spatial differentiation, showing increased construction land, forest land, and water areas, but decreased grassland, arable land, and unused land; ② The comprehensive indices of land use indicate low to medium utilization degrees but an overall rising trend, suggesting land use in a development stage; ③ Significant spatial changes occurred in land use conflicts between 2000 and 2030. The conflict-free and mild conflict zones occupied the largest proportions, the moderate conflict zones showed normal distributions, and severe and high-level conflict zones increased annually, with the highest increase observed in high-level conflict zones; ④ From 2000 to 2010, the hotspots of land use conflicts were distributed primarily in the north and southwest of the central urban area. From 2010 to 2020, they spread to the periphery of forest land in the southern and northern mountainous areas, and the areas near the alluvial fans on both sides of the salt lake in the Dabancheng District. From 2020 to 2030, the hotspots are still mainly located around the land for construction and near the forest land in mountainous areas but significantly decreased in the mountainous areas; ⑤ As demonstrated by one-way influence analysis of spatial differentiation drivers on land use conflicts, the influences of factors are in the order of patch density > population density > GDP > slope > elevation > distance from districts and counties > distance from rivers > distance from roads. Additionally, the interaction detection analysis indicates (patch density ∩ elevation) > (patch density ∩ average land population)>(patch density ∩ distance from roads). This study serves as a reference for effectively managing the conflicting demands between economic development and ecological conservation in Urumqi and enhancing the future land use composition.

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Information extraction of inland surface water bodies based on optical remote sensing:A review
FENG Siwei, YANG Qinghua, JIA Weijie, WANG Mengfei, LIU Lei
Remote Sensing for Natural Resources    2024, 36 (3): 41-56.   DOI: 10.6046/zrzyyg.2023123
Abstract118)   HTML2)    PDF (1303KB)(126)      

Inland surface water bodies, including rivers, lakes, and reservoirs, are significant freshwater resources for human beings and ecology, and their monitoring and control are greatly significant. Optical remote sensing provides great convenience for the monitoring of surface water resources, proving to be an important means for the information extraction and dynamic monitoring of inland surface water bodies. This study reviews the basic principles, remote sensing data sources, methods, existing issues, and prospects of the information extraction of water bodies. Owing to the unique characteristics of the remote sensing images of inland surface water bodies, their information can be extracted in an accurate, scientific, and effective manner using remote sensing. Multiple remote sensing data resources can be applied to the information extraction, and the optical remote sensing-based extraction methods include the threshold value method, classifier method, object orientation method, and deep learning method. Given that different methods have unique advantages, disadvantages, and applicable conditions, selecting appropriate multi-source data and varying methods based on the conditions of study areas tend to improve the information extraction accuracy. Nevertheless, there still exist some issues in the optical remote sensing-based water body information extraction, such as the balance of spatiotemporal resolution of remote sensing data, the information mining of water body characteristics, the generalization ability of water body models, and the uniformity of criteria for accuracy evaluation.

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Suitability of photovoltaic development in the Western Sichuan Plateau based on remote sensing data
YUAN Hong, YI Guihua, ZHANG Tingbin, BIE Xiaojuan, LI Jingji, WANG Guoyan, XU Yonghao
Remote Sensing for Natural Resources    2023, 35 (4): 301-311.   DOI: 10.6046/zrzyyg.2022269
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The rapid growth of China’s photovoltaic (PV) industry is accompanied by unplanned construction of PV power plants. Ascertaining the regional PV development suitability, power generation potential, and emission reduction effects holds critical significance for the sound development of the PV industry. Based on remote sensing, meteorological, and fundamental geographic data, this study constructed an evaluation index system for PV development suitability. Using this system, it assessed the zones suitable for PV development in the Western Sichuan Plateau and estimated the PV power generation potential and emission reduction effects. The results are as follows: ① The zones suitable for PV development account for 57.43% of the entire plateau, with highly suitable zones covering an area of approximately 2.07×104 km2, which are distributed primarily in the southwestern and northwestern portions of the plateau; ② The plateau exhibits significant power generation potential, reaching 17 197.97×108 KWh in highly suitable zones under a full development scenario, which is equivalent to 6.52-fold Sichuan Province’s total electricity consumption in 2019 before the COVID-19 outbreak; ③ Contrasting with conventional thermal power generation, PV power generation in highly suitable zones can achieve annual CO2 emission reduction of 12.45×108 t, which is about 12.71% of China’s total CO2 emissions in 2019 and 3.95-fold Sichuan Province’s CO2 emissions. Moreover, PV power generation can diminish the emissions of coal and conventional pollutants as well as heavy metals. The findings offer a scientific reference and guidance for selecting sites for PV power plants in the Western Sichuan Plateau and promoting the sustainable growth of the PV industry.

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Remote sensing-based monitoring and analysis of residential carbon emissions
TIAN Zhao, LIANG Ailin
Remote Sensing for Natural Resources    2023, 35 (4): 43-52.   DOI: 10.6046/zrzyyg.2022310
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In recent years, the research on residents’ carbon emissions has mostly focused on the economic level and direct energy consumption, and less involved in the area of residential areas, and most of the research has relied on traditional surface measured data. In order to improve data accuracy and make more targeted policies, this paper selected China as the research object by taking advantage of the features of strong timeliness, wide coverage and small constraints of remote sensing images, and analyzed the correlation between residential area and residential carbon emissions in China in 2019. After determining the significance of the two, combined with the influencing factor of GDP, a multiple linear regression model was established between residents’ carbon emissions and residential area and GDP. The results show that there is a linear correlation between residents’ carbon emissions and the area of residential areas and GDP. With the development of economic level, the expansion of residential area is the main driving force for the increase of residential carbon emissions, and the driving effect of GDP on the increase of residential carbon emissions has decreased. Therefore, it is necessary to reasonably control the expansion of residential areas while considering economic development, so as to make more refined emission reduction policies and achieve the country's future green and low-carbon goals.

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Remote sensing-based exploration of coalbed methane enrichment areas:Advances in research and prospects
QIN Qiming, WU Zihua, YE Xin, WANG Nan, HAN Guhuai
Remote Sensing for Natural Resources    2024, 36 (3): 1-12.   DOI: 10.6046/zrzyyg.2023193
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Coalbed methane (CBM), a type of self-sourced unconventional clean energy, occurs in coal seams and their surrounding rocks. Conventional exploration methods for CBM enrichment areas are laborious, while remote sensing provides a new approach to the rapid exploration of such areas. The basic principle behind the remote sensing-based exploration of CBM enrichment areas is as follows: ① The extraction and comprehensive analysis of multi-source data are conducted based on the comparison between the spectral features of typical surface features and those of surface feature anomalies, including rock and mineral alterations, vegetation anomalies, and thermal anomalies, caused by hydrocarbon micro-seepage in CBM enrichment areas, along with data obtained using geophysical prospecting methods like geological, seismic, and magnetotelluric methods; ② The distribution range and gas-bearing properties of CBM enrichment areas are gradually delineated. This paper reviews the hydrocarbon seepage in the CBM enrichment areas and the response mechanisms of spectral anomalies of surface rocks, minerals, and vegetation. It covers the applications of various methods based on the inversion of spectral parameters of surface rocks, minerals, and vegetation, together with the inversion of the spectral anomalies of surface features, in the exploration of potential CBM enrichment areas. Additionally, this paper elucidates the different explanations for surface thermal anomalies caused by CBM-bearing strata, as well as major methods to improve the accuracy of surface temperature inversion and their applications. In the future, the main approach to achieving low-cost, rapid exploration of CBM enrichment areas will be the analysis and information extraction of three-dimensional, multi-source information based on the combination of remote sensing technology with the geological data, seismic exploration, and geomagnetic prospecting of coalfields.

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Ecological environment in the Dongting Lake basin over the past decade: Spatio-temporal dynamic characteristics and their influencing factors from 2010 to 2019
LI Shijie, FENG Huihui, WANG Zhen, YANG Zhuolin, WANG Shu
Remote Sensing for Natural Resources    2024, 36 (1): 179-188.   DOI: 10.6046/zrzyyg.2022375
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Since the Dongting Lake basin is a significant ecological zone in the middle and lower reaches of the Yangtze River, quantitative monitoring and evaluation of its ecological environment serve as a prerequisite for regional ecological conservation, restoration, and governance. Using MODIS products involving 2010—2019 remote sensing data, this study constructed the remote sensing ecological index (RSEI) for the Dongting Lake basin based on four ecological indices: greenness, humidity, dryness, and heat. Furthermore, this study explored the spatio-temporal dynamic characteristics of the ecological environment in the basin and their influencing factors. The results show that: ① From 2010 to 2019, the Dongting Lake basin exhibited an elevated greenness index, a reduced humidity index, and relatively stable dryness and heat indices; ② The ecological environment of the Dongting Lake basin was generally satisfactory, with a mean annual RSEI of 0.58, indicating a fluctuating growth. In terms of spatial distribution, the ecological environment in the western and surrounding areas was superior to that in the eastern and central areas; ③ There were strong correlations between RSEI and precipitation, air temperature, elevation, and land cover. The RSEI was the highest (0.65) for forest land and the lowest (0.31) for construction land. As for the two primary land conversion types (grassland → forest land, arable land → grassland) in the basin, the former type could improve the regional ecological environment (ΔRSEI=0.002 5, a contribution rate of 46.3%), whereas the latter type might lead to ecological environment deterioration (ΔRSEI=-0.000 4, contribution rate: 44.44%). The results of this study, assisting in deeply understanding the spatio-temporal characteristics of the ecological environment in the basin and their internal driving mechanisms and facilitating scientific land planning and ecological environment governance, hold critical theoretical and practical significance.

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Analyzing the spatio-temporal evolution of urban expansion in the Central Plains urban agglomeration and its driving force based on DMSP/OLS and NPP/VIIRS nighttime light images
HU Miaomiao, YAN Qingwu, LI Jianhui
Remote Sensing for Natural Resources    2024, 36 (1): 189-199.   DOI: 10.6046/zrzyyg.2022410
Abstract113)   HTML1)    PDF (11869KB)(131)      

Discerning the spatial pattern and driving mechanism of urban expansion will contribute to the sustainable development of the Central Plains urban agglomeration (CPUA). Based on the DMSP/OLS and NPP/VIIRS nighttime light images, this study extracted the built-up area of the CPUA from 1993 to 2018 through statistical data comparison. Furthermore, this study delved into the spatial-temporal evolutionary characteristics of the urban expansion in this period on scales of the entire urban agglomeration and prefecture-level cities. Accordingly, this study investigated the driving force behind the spatial-temporal expansion using a driving force model. The results show that: ① In terms of the spatial evolution, with Zhengzhou as the center and the northeast to southwest as the reference direction, the built-up areas and expansion scales of cities in the CPUA were generally large in the central part but small on both sides. With 2010 as the point of division, the expansion type shifted from edge expansion to exclave expansion, and the expansion mode transitioned from planar expansion to multi-center dotted expansion and linear expansion along main traffic routes; ② Regarding the temporal evolution, different cities exhibited significantly distinct expansion area, speed, and intensity. The expansion speed and intensity were both positive, roughly manifesting W-shaped fluctuations. The center of the built-up areas shifted from southwest to northeast, then northeast, then west, then northwest, and finally southeast, wandering between Zhengzhou and Kaifeng cities; ③ The main driving force behind the urban expansion resulting from economic factors, followed by social, transportation, and environmental factors. The top five driving force indicators affecting the urban expansion comprised general public budget revenue, GDP, actually utilized foreign capital, education expenditure, and population density.

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Comparative study on atmospheric correction methods for ZY-1 02D hyperspectral data for geological applications
LI Na, DONG Xinfeng, WANG Jinglan, CHEN Li, GAN Fuping, LI Tongtong, ZHANG Shifan
Remote Sensing for Natural Resources    2023, 35 (4): 17-24.   DOI: 10.6046/zrzyyg.2022349
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Hyperspectral data, exhibiting technical advantages in the spectral dimension, have been extensively used for accurately identifying surface features, particularly mineral information. Mineral identification relies on hyperspectral reflectance products, necessitating the application of proper atmospheric correction methods to obtain high-precision surface reflectance products that meet application requirements. Hence, three commonly used atmospheric correction models, ATCOR, FLAASH, and QUAC, were utilized to correct the hyperspectral data acquired by the ZY-1 02D satellite. Moreover, a comparative analysis was conducted on their visual effects, spectral analysis of typical surface features, and extraction of mineral information. The results are as follows: ① All three atmospheric correction models can effectively enhance image clarity in terms of visual effects. Specifically, the ATCOR model slightly outperformed the FLAASH and QUAC models; ② The correlation coefficients (R2) between the typical surface feature spectra of the three models and the ASD-measured spectra showed average values exceeding 0.7, suggesting high consistency and accuracy. Especially, the imaging spectra derived from the inversion results of the ATCOR model were more similar to the ASD-measured spectra; ③ The three models yielded relatively consistent results in chlorite identification but divergent results in sericite identification. Comparatively, the FLAASH and QUAC models exhibited high omission rates in surface regions with low sericite content. Overall, all three models can achieve satisfactory atmospheric correction effects, but the ATCOR model is superior to the other two models in mineral identification.

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Impact of soil salinization on the eco-environment quality of coastal wetlands:A case study of Yellow River Delta
ZHANG Zhimei, FAN Yanguo, JIAO Zhijun, GUAN Qingchun
Remote Sensing for Natural Resources    2023, 35 (4): 226-235.   DOI: 10.6046/zrzyyg.2022284
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Soil salinization is an important reason for land degradation and desertification and has a huge impact on the eco-environment. Coastal wetlands are typical areas subjected to a weak eco-environment and severe salinization, and there is an urgent need to investigate the impact of soil salinization on their eco-environment. This study proposed the baseline-based soil salinity index (BSSI), which can effectively suppress the influence of complex features on surface salinization monitoring and improve the accuracy of saline soil extraction by 10% compared to other salinity index models. Furthermore, this study proposed the optimized water benefit-based ecological index (OWBEI) by optimizing the water benefit-based ecological index (WBEI), which can effectively increase the accuracy of eco-environment quality assessment to 87%. Finally, this study explored the mechanical processes of the influence of soil salinization on the eco-environment quality based on the distribution of soil salinization and eco-environment quality obtained from the Yellow River Delta. The results show that the deterioration of soil salinization has led to an increase in the soil vulnerability of coastal wetlands, indirectly resulting in a continuous decrease in eco-environment quality. Although eco-environment protection measures have been continuously proposed, few of them are tailored to the solving of salinization. This leads to the deterioration of the ecological quality, which then yields negative feedback to the soil and eventually forms a vicious circle. This adversely affects local production, life, and social development.

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Sea ice in Bohai Sea:Spatio-temporal distribution and the forcing effects of atmospheric circulation
GUO Yudi, WANG Tie, CHENG Shanjun, ZUO Tao
Remote Sensing for Natural Resources    2024, 36 (1): 242-249.   DOI: 10.6046/zrzyyg.2022441
Abstract111)   HTML1)    PDF (14382KB)(101)      

Sea ice disasters, significant marine disasters in the Bohai Sea in winter, severely threaten oil exploitation, marine transportation, and fishery. Hence, it is particularly critical to monitor and predict the formation and melting of sea ice. Based on MODIS data, reanalysis grid data, and meteorological observation data from 2001 to 2020, this study derived the daily sea ice area of the Bohai Sea through the inversion of satellite data. Then, this study conducted a statistical analysis of changes in the sea ice area, including inter-annual variations and the variations during the freezing, severe ice, and melting periods in different years with sea ice disasters. Moreover, this study delved into the differences in the atmospheric circulation of the Bohai Sea between years with severe and mild sea ice disasters. The results show that: ① Over the past 20 years, higher sea ice grades in the Bohai Sea corresponded to longer ice periods, and the sea ice area experienced a decrease-increase-decrease process, which was opposite to the changes in accumulated temperature; ② Intra-annual sea ice formation and melting processes exhibited a single peak or multiple peaks, with the multi-peak type corresponding to a longer ice period and large-scale ice-bound time; ③ During the initial ice formation period, the sea ice in the Bohai Sea was primarily distributed in the Liaodong Bay, with the sea ice area in a year with severe sea ice disasters more than twice that in a year with mild sea ice disasters. During the ice period, the sea ice covered almost all three bays, completely covering the three bays in years with severe sea ice disasters. During the ice melting period, the sea ice still spread primarily in the Liaodong Bay, manifesting an EN-WS-directed distribution; ④ Compared to years with mild sea ice disasters, years with severe sea ice disasters showed more favorable atmospheric circulation for sea ice formation, accompanied by stronger and cooler cold air behind the upper-level trough along the area from Lake Baikal to Northeastern China. Furthermore, there is a strong negative correlation between the sea ice situation in the Bohai Sea and the 500 hPa geopotential height, with the latter determining the former.

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A deep learning-based study on downscaling of GPM products in Fujian-Zhejiang-Jiangxi area
LI Xintong, SHI Lan, CHEN Duoyan
Remote Sensing for Natural Resources    2023, 35 (4): 105-113.   DOI: 10.6046/zrzyyg.2022270
Abstract110)   HTML10)    PDF (3843KB)(180)      

A timely and accurate assessment of the spatial precipitation distribution holds great significance to the development of the national economy. At present, most remote sensing-based precipitation products improve their accuracy using multiple regression models and physical models rather than deep learning models. This study improved a long short-term memory neural network (LSTM) deep learning model, yielding an optimized LSTM deep learning model. With the Fujian-Zhejiang-Jiangxi area as the study area, this study conducted downscaling for an integrated multi-satellite retrievals for global precipitation measurement (IMERG) product based on the daily precipitation data of 69 meteorological stations from 2015 to 2019 by introducing multiple factors controlling precipitation such as vegetation, slope aspect, slope gradient. Finally, this study assessed the reliability of the optimized model through verifications based on high-density meteorological stations and individual years. The results show that the downscaling results are consistent with the spatio-temporal distribution of precipitation measured at meteorological stations and, thus, can better reflect the spatial distribution of precipitation in the study area than the original IMERG. Furthermore, underestimated and overestimated precipitation data of the study area from the GPM product were corrected. As indicated by the verification based on high-density meteorological stations, the downscaled model yielded correlation coefficients of 0.9 or above for July and October, which were followed by April. The correlation coefficient was the lowest of 0.7 in January. As shown by the verification based on individual year data, the correlation coefficient between the daily precipitation downscaling results and the measurement results in 2020 was above 0.8, with a root mean square error of 5.23 mm and an average relative error of 9.43%. Therefore, the deep learning-based downscaling model enjoys high accuracy on both daily and monthly scales and can be widely applied in the assessment of both spatial and temporal precipitation distributions.

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Application of GF-5 hyperspectral data in uranium deposit exploration
ZHANG Yuantao, PAN Wei, YU Changfa
Remote Sensing for Natural Resources    2023, 35 (4): 61-70.   DOI: 10.6046/zrzyyg.2022250
Abstract108)   HTML12)    PDF (8290KB)(192)      

However, since GF-5 launch in 2018, few studies regarding the application of GF-5 AHSI data for uranium deposit exploration have been reported. In this study, with the Weijing area of Inner Mongolia as the study area, the spectral hourglass technology was applied to extract alteration anomalies of goethite and low-, medium-, and high-aluminum sericite from corresponding GF-5 AHSI data. Then, the principal component analysis (PCA) and the LINE module in PCI Geomatica software were employed for the automatic extraction of linear structures in the study area, with a linear structure density map created. Finally, a uranium mineralization potential map of the study area was generated by integrating all proof layers based on the ArcGIS software. The results indicate that the extraction of alteration information and linear structures, and the integration of multiple proof layers are feasible, and the obtained uranium mineralization potential map exhibits high reliability. One uranium deposit prediction zone was identified based on the study results and geological data. The study results will guide the subsequent uranium deposit exploration in the study area while providing a reference for the geological application of GF-5 AHSI data.

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Advances in research and application of remote sensing-based snow monitoring products
SUN Xiyong, LIU Jiafeng, FAN Jinghui, ZHANG Wenkai, SHI Lijuan, QIU Yubao, ZHU Farong
Remote Sensing for Natural Resources    2024, 36 (3): 13-27.   DOI: 10.6046/zrzyyg.2023065
Abstract108)   HTML2)    PDF (1281KB)(79)      

Snow proves to be both an important factor in characterizing the surface cryosphere and a critical parameter for weather and hydrological phenomena. Employing remote sensing to conduct long-term and large-scale monitoring of snow morphologies and their changes plays a vital role in research into global climate change, investigations into hydrology and water resources, and geological disaster prevention. After decades of development, significant progress has been made in the field of remote sensing-based snow monitoring technology both in China and abroad. Accordingly, the products for remote sensing-based snow monitoring have become increasingly abundant, and the snow-orientated inversion algorithms have been continuously improved. This paper provides a summary of the existing, widely applied products after categorizing them into three types: snow-cover extent (SEC), snow coverage, and snow depth/snow water equivalent (SWE) products. Furthermore, this study organizes the commercialized remote sensing inversion algorithms used in existing, typical SEC and SWE products. The review of advances in the relevant scientific research reveals that, with the constant presence of sensors with high temporal and spatial resolutions in China and abroad and the support of both novel optical and microwave data sources and new technologies, researchers have gradually improved the accuracy of snow-orientated inversion algorithms by optimizing these algorithms based on regional characteristics. This will provide more support for continuously improving remote sensing-based snow monitoring products in the future.

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Monitoring of the area of Poyang Lake based on Landsat images and its relationship with the water level
ZHAO Hui, CHEN Zhen, FENG Chaofan, ZHANG Tong, ZHAO Xuejing, ZHANG Zhaoxu
Remote Sensing for Natural Resources    2024, 36 (2): 198-206.   DOI: 10.6046/zrzyyg.2023061
Abstract108)   HTML5)    PDF (9602KB)(75)      

Lakes constitute a crucial part of terrestrial ecosystems. Changes in the water areas of lakes significantly influence environments and human production activities. Poyang Lake, the largest freshwater lake in China, has experienced many floods and droughts in recent years, thus necessitating its dynamic monitoring. With 175-phase Landsat images of Poyang Lake from 2000 to 2021 as the data source, this study comparatively analyzed four water body extraction methods: the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI), the automated water extraction index (AWEI), and the spectrum photometric method (SPM), determining the optimal water body extraction index for Poyang Lake. Moreover, based on the 175-phase area data, this study delved into the inter-annual area variation trend from 2000 to 2021 as well as the intra-annual seasonal variations. Furthermore, it established the area - water level model by combining 50 sets of water level data from 2009 to 2013 and 2017 to 2018. The results show that: ① The AWEI model, outperforming the other three models in the extraction accuracy, was employed for the water body extraction of Poyang Lake; ② The area of Poyang Lake exhibited significant seasonal variations, large inter-annual fluctuations in the wet season, and relatively gentle inter-annual fluctuations in the dry season; ③ The area - water level piecewise linear model of the Tangyin gauging station proved optimal, which can predict the water coverage area based on real-time water level observations in Poyang Lake, compensating for the limitation of visible spectral remote sensing methods in monitoring the lake water coverage during cloudy and rainy weather.

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Information extraction of landslides based on high-resolution remote sensing images and an improved U-Net model: A case study of Wenchuan, Sichuan
BAI Shi, TANG Panpan, MIAO Zhao, JIN Caifeng, ZHAO Bo, WAN Haoming
Remote Sensing for Natural Resources    2024, 36 (3): 96-107.   DOI: 10.6046/zrzyyg.2023132
Abstract108)   HTML0)    PDF (7655KB)(65)      

Rapid identification and detection of landslides can both meet the requirement of timely responses to disasters and hold great significance for loss assessment and rescue post-disaster. This study proposed a deep learning-based automatic information extraction method for landslides to improve their detection accuracy. Specifically, the model input of this method includes the remote sensing images of the target areas, data from digital elevation models, and variation characteristics extracted using robust change vector analysis (RCVA). Furthermore, a U-Net model integrating dense upsampling and asymmetric convolution is designed to improve the identification accuracy. Taking Wenchuan, Sichuan Province as the study area, this study designed experiments to test the pixel-level image segmentation accuracy of landslides using different data combinations and methods. The results indicate that the improved U-Net model proposed in the study can produce the optimal image segmentation results of landslides.

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Spatial distribution prediction of soil pH in arable land of Jiangxi Province based on multi-source environmental variables and the random forest model
ZHONG Xiaoyong, LI Hongyi, GUO Dongyan, XIE Modian, ZHAO Wanru, HU Bifeng
Remote Sensing for Natural Resources    2023, 35 (4): 178-185.   DOI: 10.6046/zrzyyg.2022294
Abstract107)   HTML11)    PDF (4067KB)(166)      

This study aims to compare the accuracy of random forest(RF) and ordinary kriging(OK) model for predicting spatial distribution of soil pH in arable land of Jiangxi Province using different covariates combination, and assess the feasibility and potential of RF method for improving the prediction accuracy of soil pH value. The RF algorithm is used to predict the pH value of cultivated soil in Jiangxi Province based on environmental covariate such as climate, topography and vegetation, combined with soil properties and cultivated land use conditions, identify the main influencing factors. The results produced by the RF was compared with the classical OK interpolation model. Our results showed that the accuracy of RF-A model with soil properties and cultivated land use conditions as environmental variables is better than that of RF-B model which only including terrain, climate and vegetation attributes as environmental variables. Climatic condition is the dominate factor which control the spatial variation of soil pH. the topographic factors and anthropogenic factors also have essential effect on spatial variability of soil pH. Thus, this study proved RF method has theoretical and practical significance for improving the accuracy of soil pH prediction at large-scale.

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Estimation of soil organic carbon content in farmland based on UAV hyperspectral images: A case study of farmland in the Huangshui River basin
SONG Qi, GAO Xiaohong, SONG Yuting, LI Qiaoli, CHEN Zhen, LI Runxiang, ZHANG Hao, CAI Sangjie
Remote Sensing for Natural Resources    2024, 36 (2): 160-172.   DOI: 10.6046/zrzyyg.2023005
Abstract106)   HTML6)    PDF (17204KB)(83)      

Rapid and accurate estimation and spatial distribution mapping of soil organic carbon content in farmland facilitate the refined management of soil and the development of smart agriculture. This study investigated three typical farmland areas in the Huangshui River basin of Qinghai Province using 296 soil samples and corresponding field in situ spectra collected synchronously. The unmanned aerial vehicle (UAV) with a hyperspectral camera was employed for image acquisition, and the soil samples were tested for spectral acquisition and organic carbon content in the laboratory. The spectral reflectance was transformed into seven different forms, and the main characteristic bands were screened out through correlation analysis. Using multiple linear regression, partial least squares regression, and random forest, the experimental spectra, field in situ spectra, and UAV spectra were modeled, with the accuracy of the models compared. The UAV spectra were corrected using the direct spectral conversion method, and the optimal model of corrected UAV spectra was used for modeling. The model was substituted into the UAV hyperspectral images for the organic carbon content mapping. Finally, the farmland areas meeting the mapping accuracy requirements were analyzed and discussed. The results show that: ① The multiple linear regression after logarithmic transformation of UAV hyperspectra failed to estimate the organic carbon content, with a relative percent deviation (RPD) of 1.375. Except for it, the experimental spectra, field in situ spectra, and original spectra of UAV hyperspectra as well as all conversion methods could estimate the organic carbon content, with coefficients of determination (R2) ranging from 0.562 to 0.942, root mean square errors (RMSEs) ranging from 1.713 to 5.211. and RPDs between 1.445 and 4.182; ② Among all spectral transformation methods, multiple scatter correction and first-order differential transformation exhibited the highest correlation with the organic carbon content, presenting characteristic bands of 429~449 nm, 498~527 nm, 830~861 nm, and 869 nm; ③ As revealed by the modeling results, the random forest model manifested the highest accuracy, followed by the partial least squares model and the multiple linear regression model in turn. The corrected UAV spectra yielded improved modeling accuracy; ④ The inversion accuracy of the three farmland areas all met the mapping requirements, with R2 values above 0.88. Farmland A exhibited the highest average organic carbon content of 28.88 g·kg-1 and an overall uniform spatial distribution. Farmland B manifested average organic carbon content of 13.52 g·kg-1 and a significantly varying spatial distribution. Farmland C displayed the lowest average organic carbon content of 8.54 g·kg-1 and significant differentiation between high and low values. This study can be referenced for the application of UAV hyperspectral remote sensing technology to the field-scale estimation and digital mapping of soil organic carbon content.

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Spatio-temporal differentiation of vegetation net primary productivity in Henan Province as well as its driving factors
ZHI Lu, HU Tao, ZOU Bin, LI Haosheng, ZHAO Yongqiang
Remote Sensing for Natural Resources    2023, 35 (4): 169-177.   DOI: 10.6046/zrzyyg.2022347
Abstract105)   HTML14)    PDF (3872KB)(151)      

The net primary productivity (NPP) of vegetation, exhibiting regional differentiation, serves as a crucial parameter for determining the carbon source/sink of ecosystems. Based on the MOD17A3HGF, topography, and human activity data, this study delved into the spatio-temporal differentiation of vegetation NPP in Henan Province from 2010 to 2020 and its response to driving factors using methods like the gravity center model, trend analysis, and the geographical detector model. Moreover, it revealed the explanatory power and interactions of the driving factors. The results are as follows: ① Temporally, the vegetation NPP from 2010 to 2020 displayed a slightly fluctuating upward trend, averaging 424.89 gC·m-2·a-1. Its gravity center exhibited significant temporal differentiation, with the average center of gravity closer to the geometric center. ② Spatially, the vegetation NPP values increased from the northeast to the southwest and were dominated by median values (300~600 gC·m-2·a-1). ③ In terms of influencing factors, the vegetation NPP showed a higher correlation with precipitation compared to temperature. Moreover, it first increased and then decreased with an increase in altitude and slope. The areas with altitudes below 200 m and slopes less than 2° contributed the most to NPP in the study area. The vegetation NPP on sunny slopes was higher than that on shady slopes. In the case of land use changes, the shift to arable land plays a significant role in the increase of total NPP. ④ The geographical detection results indicate that precipitation exhibited the highest explanatory power for changes in vegetation NPP. The two-factor interactions all showed an enhanced relationship, with the q value of precipitation ∩ longitude being the highest. These findings provide data support for ecological protection and high-quality development of Henan Province.

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A YOLOv5-based target detection method using high-resolution remote sensing images
SONG Shuangshuang, XIAO Kaifei, LIU Zhaohua, ZENG Zhaoliang
Remote Sensing for Natural Resources    2024, 36 (2): 50-59.   DOI: 10.6046/zrzyyg.2023052
Abstract104)   HTML4)    PDF (19607KB)(110)      

High-resolution remote sensing images contain rich data and information, which reduce the difference between the target and the background, resulting in substandard detection accuracy and reduced target detection performance. Based on the deep learning algorithm You Only Look Once (YOLO), this study designed a lightweight network model GC-YOLOv5 by combining end-to-end coordinate attention (CA) and the lightweight network module GhostConv. The CA was employed to encode channels along the horizontal and vertical directions, enabling the attention mechanism module to simultaneously capture remote spatial interactions with precise location information and helping the network locate targets of interest more accurately. The original ordinary convolutional module convolutional-batchnormal-SiLu (CBS) was replaced by the GhostConv module, reducing the number of parameters in the feature channel fusion process and the size of the optimal model. Experiments were conducted on the GC-YOLOv5 using the publicly available NWPU-VHR-10 dataset, with the robustness of the model verified on the RSOD dataset. The results show that GC-YOLOv5 yielded a detection accuracy of 96.5% on the NWPU-VHR-10 dataset, with a recall rate of 96.4% and mAP of 97.7%. Moreover, GC-YOLOv5 achieved satisfactory results on the RSOD dataset.

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Forest stock volume inversion based on ICESat-2 and Sentinel-2A data
LIU Meiyan, NIE Sheng, WANG Cheng, XI Xiaohuan, CHENG Feng, FENG Baokun
Remote Sensing for Natural Resources    2024, 36 (1): 210-216.   DOI: 10.6046/zrzyyg.2022478
Abstract102)   HTML1)    PDF (6259KB)(108)      

Forest stock volume (FSV), a critical indicator in forestry surveys, plays a significant role in evaluating the health and carbon sequestration capacity of forests. Cooperative inversion using active and passive remote sensing data is an essential method for FSV inversion of large areas. Focusing on forests in Shangri-La, Yunnan Province, this study extracted feature variables from ICESat-2/ATLAS and Sentinel-2A images and then screened them through correlation analysis and collinearity diagnostics. Using the selected feature variables, this study constructed a Sentinel-2A variable set, an ICESat-2/ATLAS variable set, and a combined variable set. Based on the measured data of sample sites and the three feature variable sets, this study built linear and nonlinear regression models for FSV inversion using stepwise linear regression and the random forest method, respectively. Finally, this study performed accuracy verification and comparative analysis of the results: ① For the three variable sets, the random forest method yielded higher accuracy than the stepwise linear regression; ② The ICESat-2/ATLAS variable set exhibited higher inversion accuracy than the Sentinel-2A variable set under both regression methods; ③ Combining Sentinel-2A and ICESat-2/ATLAS variable sets, the random forest method yielded the highest inversion accuracy, with its coefficient of determination (R2), root mean square error (RMSE), and relative root mean square error (rRMSE) of 0.7034, 84.78 m3/hm2, and 36.46%, respectively. Overall, compared to Sentinel-2A data, the inversion models based on ICESat-2/ATLAS data and multi-source remote sensing data can effectively improve the accuracy of FSV inversion and model stability.

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Tectonic alteration information extraction and prospecting prediction for the Delong area of Qinghai Province based on GF-2 and ASTER data
WANG Yilong, WANG Ran, YAN Ziqing, ZHANG Xinming, LI Xiaolong, XU Chongwen
Remote Sensing for Natural Resources    2024, 36 (1): 217-226.   DOI: 10.6046/zrzyyg.2022444
Abstract102)   HTML3)    PDF (19046KB)(135)      

The Delong area, located in the eastern segment of the Eastern Kunlun gold-polymetallic metallogenic belt in Qinghai, is recognized as an area with significant exploration potential. However, its remote geographical location and rugged terrain pose challenges to large-scale geochemical explorations and conventional geological surveys. Based on ASTER and GF-2 data, this study identified the linear and circular structures by analyzing the color tones, geometric structures, and textures of remote sensing images with varying resolutions. Through the spectral analysis of primary alteration minerals, this study extracted the information on ferrugination, Al-OH, and Mg-OH alterations from visible light to near-infrared bands and short-wave infrared bands of ASTER using the mask + principal component analysis technique. Then, this study established a remote sensing-based prospecting prediction model for gold deposits in the study area by combining geoscience information and field survey results and comprehensively analyzing the association between the tectonic alteration information derived from remote sensing image interpretation and the gold mineralization of the study area. Using the prediction model, this study delineated three prospective areas for mineral exploration. The field verification revealed several new gold ore bodies in the Delong prospective area. The results show that the integration of remote sensing data and GIS technology can effectively identify surface hydrothermal alterations and tectonic spatial structures. The integration can serve as a guide for subsequent prospecting prediction of the study area.

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Coupled assessment and spatio-temporal evolution analysis of ecosystem health in Fujian Province
CAO Delong, TANG Tingyuan, LIN Zhen, XU Zheng, YAN Xu
Remote Sensing for Natural Resources    2024, 36 (1): 137-145.   DOI: 10.6046/zrzyyg.2023081
Abstract102)   HTML0)    PDF (7370KB)(151)      

This study aims to explore the origin of the excellent ecology in Fujian Province in the past 15 years. First, a land use intensity system with a five-year time interval was constructed using the 2005—2020 MODIS images and land use data of Fujian as data sources. Then, the coupling relationship between the remote sensing ecological index (RSEI) and land use intensity was analyzed based on a coupled coordination model. Finally, the spatio-temporal evolution analysis was conducted for the ecological health of Fujian from 2005 to 2020. The results show that: ① The ecological environment of Fujian manifested an improvement-degradation-degradation trend, with an average RSEI value of 0.704 8 in 2020, suggesting a sound ecological environment; ② The land use intensity of Fujian displayed an increasing trend, with a growth rate of 26.00%. Most especially, Sanming City demonstrated a maximum increase of 160.91% in land use intensity; ③ The coupled coordination degree of Fujian increased by 0.729 0, suggesting high coordination. All cities in Fujian exhibited increased coupled coordination degrees, except for Xiamen City, where the coupled coordination degree decreased by 0.131 0, implying a slight imbalance. This study fills the gap in the research on the interactions between ecosystem health and land use intensity. It also provides a new perspective for ecological civilization construction and ecosystem health assessment in Fujian and even China.

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Extracting granite pegmatite information based on GF-2 images and the random forest algorithm
DU Xiaochuan, LOU Debo, XU Lingang, FAN Yinglin, ZHANG Lin, LI Wanyue
Remote Sensing for Natural Resources    2023, 35 (4): 53-60.   DOI: 10.6046/zrzyyg.2022280
Abstract100)   HTML16)    PDF (3629KB)(180)      

Granite pegmatites serve as a significant carrier and prospecting marker of granite pegmatite-type lithium deposits. The southeastern Zhaka area in Tianjun County, Qinghai Province demonstrates considerable prospecting potential for lithium deposits. Nevertheless, its high altitudes and deep cross-cutting characteristics pose challenges in surface surveys. Hence, this study extracted the granite pegmatite information within the study area from remote sensing images using the random forest algorithm. With high-spatial-resolution GF-2 remote sensing images as the primary data source, it extracted the spectral, texture, exponential, topographic, and edge features from various ground objects within the study area. These features, together with the newly introduced contrast limited adaptive histogram equalization (CLAHE) features, constituted 25 feature variables, forming a feature subset. Then, feature variables in the subset were evaluated for their feature importance, and their importance scores were used for feature selection, determining the optimal feature combination for extracting granite pegmatite information. Ultimately, 16 feature variables were chosen for random forest classification, with the accuracy of the classification results assessed. The study indicates that: ①The CLAHE feature variables emphasize the tonal variations among ground objects, thereby enhancing the classification accuracy, with the overall accuracy increased by 2.7 percentage points and the Kappa coefficient increased by 0.035; ②The classification results for granite pegmatites based on GF-2 images and the random forest algorithm exhibited overall accuracy of 93.1%, with a Kappa coefficient of 0.902, user accuracy of 94.24%, and producer accuracy of 98.00%, confirming the effectiveness of the method used in this study. Moreover, this study provides reliable data for future research in the study area.

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Exploring the spatio-temporal variations and influencing factors of vegetation cover in Yunnan Province
LI Yimin, FENG Xianjie, LI Yuanting, YANG Xue, XIANG Qianying, JI Peikun
Remote Sensing for Natural Resources    2024, 36 (2): 116-125.   DOI: 10.6046/zrzyyg.2023037
Abstract100)   HTML5)    PDF (15550KB)(133)      

Yunnan Province has abundant species resources but fragile ecosystems, and the ecological vulnerability is closely related to vegetation cover. Hence, based on the normalized difference vegetation index (NDVI) from the MOD13Q1 dataset for 2000—2022, this study dynamically monitored the spatio-temporal variations of vegetation using the maximum value composite (MVC), Theil-Sen median trend analysis, and Mann-Kendall significance test. Moreover, this study delved into the response of vegetation to factors like topography, climate change, and land cover through correlation analysis. The results show that: ① From 2000 to 2022, the overall vegetation coverage of Yunnan Province was relatively high, with average annual NDVI values ranging from 0.74 to 0.90, showing a fluctuating upward trend. Of the whole area, 91.17% exhibited an increasing vegetation coverage trend, with the fastest growth rate seen in northeastern Yunnan; ② Regional differences were observed in vegetation cover, which was higher in southeastern and southwestern Yunnan compared to northwestern, central, and northeastern Yunnan; ③ The NDVI values of Yunnan Province were relatively stable below the altitude of 3 900 m, and decreased with increasing altitude in the case of over 3 900 m; ④ The NDVI values were the lowest with slopes below 3°, and with an increase in slope, they increased first and then decreased; ⑤ The planar slope aspect displayed the lowest NDVI values, and other slope aspects showed minimal impact on vegetation growth; ⑥ From 2000 to 2022, the vegetation cover in central, southeastern, and northeastern Yunnan was positively correlated with precipitation, suggesting that precipitation in these areas was favorable for vegetation growth. However, the vegetation cover in southwestern and northwestern Yunnan showed a negative correlation with precipitation. Additionally, the vegetation cover in the whole region and various areas was positively correlated with temperature, suggesting that temperature is beneficial to vegetation growth. The results of this study will provide a scientific basis for strengthening ecological environment construction and ecological management in Yunnan Province.

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Early identification of potential landslides for the Sichuan-Chongqing power grid based on optical remote sensing and SBAS-InSAR
ZHAO Huawei, ZHOU Lin, TAN Minglun, TANG Minggao, TONG Qinggang, QIN Jiajun, PENG Yuhui
Remote Sensing for Natural Resources    2023, 35 (4): 264-272.   DOI: 10.6046/zrzyyg.2022321
Abstract99)   HTML14)    PDF (7185KB)(154)      

Power grid projects in mountainous regions have encountered numerous landslides in recent years, leading to collapsed transmission towers and power outages. Hence, early identification of potential landslides is crucial for ensuring the safety of power engineering. For this purpose, this study conducted early identification of potential landslides along the Sichuan-Chongqing power grid based on optical remote sensing and the small baseline subset (SBAS) - interferometric synthetic aperture radar (InSAR) technology. The interpretation of high-resolution optical remote sensing images revealed 28 potential landslide sites near the transmission towers along the power grid. Based on this, this study detected the study area’s surface deformation using the SBAS-InSAR technology, identifying 27 potential landslide sites. Except for 15 repeated results, the above two methods identified a total of 40 potential landslide sites. Finally, through field check and the qualitative analysis of deformation signs and stability, this study determined that seven potential landslide sites threaten the safety of transmission towers, with two of them presenting higher risks. These findings provide valuable guidance and references for the prevention and control of landslides along the Sichuan-Chongqing power grid.

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