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  • Table of Content
       , Volume 34 Issue 2 Previous Issue    Next Issue
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    A review of remote sensing inversion methods for estimating soil water content based on hyperspectral characteristics
    YAN Hongbo, WEI Wanqiu, LU Xianjian, YANG Zhigao, LI Zhenbao
    Remote Sensing for Natural Resources. 2022, 34 (2): 1-9.   DOI: 10.6046/zrzyyg.2021126
    Abstract   HTML ( 1389 )   PDF (1215KB) ( 624 )

    The rapid and accurate estimation of soil water content at different spatial and temporal scales is key research content in the fields of hydrology, environment, geology, agriculture, and climate change. However, it is still a challenge to obtain accurate soil water content presently. In the past, the traditional point-based soil sampling and analysis methods were time-consuming and laborious. By contrast, retrieving soil water content using remote sensing images has the advantages of a wide range, high timeliness, low cost, and strong dynamic contrast. In hyperspectral remote sensing, soil water content is related to the wavelength range of soil reflectance. So far, many methods have been used to describe the relationships between soil water content and hyperspectral remote sensing. This paper summarized existing methods for estimating soil water content based on hyperspectral reflectance and divided them into four categories: spectral reflectance methods, function methods, model methods, and machine learning methods. Moreover, this paper compared and analyzed the potential and limitations of different methods in terms of accuracy, complexity, auxiliary data requirements, operability under different modes, and the dependence on soil types. Finally, this study put forward corresponding suggestions for future research on the relationships between soil water content and hyperspectral reflectance.

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    Research progress and development trend of remote sensing information extraction methods of vegetation
    HUANG Pei, PU Junwei, ZHAO Qiaoqiao, LI Zhongjie, SONG Haokun, ZHAO Xiaoqing
    Remote Sensing for Natural Resources. 2022, 34 (2): 10-19.   DOI: 10.6046/zrzyyg.2021137
    Abstract   HTML ( 220 )   PDF (749KB) ( 424 )

    The remote sensing information extraction of vegetation is the basis and key link for remote sensing investigation and dynamic monitoring of vegetation coverage, which is of great significance for regional ecological environment protection and sustainable development. For this purpose, the research progress on the remote sensing information extraction methods of vegetation was reviewed from prior knowledge, expert knowledge and related auxiliary information, extraction of vegetation phenological features, the fusion of multi-source remote sensing data, machine learning, and other methods. Then, the main problems and challenges existing at the present stage were pointed out, and the future development trend was put forward. The research shows that there are many methods to extract remote sensing information about vegetation, and different methods have their own advantages and disadvantages in the application. However, the research on remote sensing information extraction methods of vegetation is currently facing many challenges, such as the lack of openness of high-resolution remote sensing data, the poor stability of parameter settings in vegetation information extraction models, the prominent phenomenon of same objects with different spectra and different objects with the same spectrum, the difficulties in automatic extraction of vegetation remote sensing information based on an expert knowledge base, and the need in further research on the multiple-method fusion. Therefore, making more breakthroughs in integrating multi-source data, multiple methods and new features of multi-temporal remote sensing images will be necessary to promote the refined, automated, and intelligent development of remote sensing information extraction of vegetation.

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    Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model
    XU Zixing, JI Min, ZHANG Guo, CHEN Zhenwei
    Remote Sensing for Natural Resources. 2022, 34 (2): 20-29.   DOI: 10.6046/zrzyyg.2021354
    Abstract   HTML ( 233 )   PDF (6958KB) ( 408 )

    Predicting the subsequent subsidence in mining areas according to the law of mining subsidence is the key to assessing mining risks and adjusting mining planning. This study determined the available conditions of the logistic model for mining subsidence prediction through analysis and simulation experiments and proposed a method for the dynamic prediction of mining subsidence based on small baseline subset (SBAS)-interferometric synthetic aperture radar (InSAR) technology and the logistic model. Firstly, the time-series subsidence data of a mining area was obtained using the SBAS-InSAR technology. Then, taking the time series subsidence data as the data for fitting, the parameters of the logistic model were calculated pixel by pixel by using the trust region algorithm. Then, the pixel range in which the subsequent subsidence can be predicted was determined according to the available conditions of the logistic model. Finally, according to the Logistic model, the subsequent subsidence within the predictable range was predicted. This method was applied to a certain mining area in Erdos City, Inner Mongolia for tests, and the prediction results were verified using the InSAR monitoring results of corresponding dates. The predicted results after 36 days and 108 days of ming had the root mean square error (RMSE) of 0.010 1 m and 0.023 6 m, respectively, and their proportion with prediction errors of less than 0.03 m reached 98.9% and 89.3%, respectively. These results indicate that the method for dynamic prediction proposed in this study has high prediction accuracy.

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    A remote sensing method for judging the cross-border mining of oil and gas mines
    ZHAO Yuling, YANG Jinzhong, SUN Yaqin, CHEN Dong
    Remote Sensing for Natural Resources. 2022, 34 (2): 30-36.   DOI: 10.6046/zrzyyg.2021140
    Abstract   HTML ( 87 )   PDF (4710KB) ( 278 )

    Cross-border mining is a difficult and hot topic in the current supervision of oil and gas mines. Based on the judgement and interpretation of superficial and surface engineering, such as well sites, station sites, oil wells, gas wells, metering plants, gas gathering stations, gathering and transportation stations, patrol roads, and oil and gas pipelines within a single mining right, this study proposed for the first time that the combined information of superficial and surface engineering allow for quickly clarifying the accumulation and flow direction of oil and gas and accurately identifying and determining the production sites belonging to the same mining right and the cross-border sites. This method has been applied to a certain oil field as the test area and has been proved effective.

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    Principal component selection method for hyperspectral remote sensing images based on spatial statistics
    SUN Xiao, PENG Junhuan, ZHAO Feng, WANG Xiaoyang, LYU Jie, ZHANG Dengfeng
    Remote Sensing for Natural Resources. 2022, 34 (2): 37-46.   DOI: 10.6046/zrzyyg.2021214
    Abstract   HTML ( 81 )   PDF (10623KB) ( 424 )

    The principal component analysis is a widely used method for dimensionality reduction of hyperspectral remote sensing images. In task-oriented work, the principal component selection method based on cumulative variance contribution rate is not ideal. To address the problem of principal component selection after principal component analysis transformation, a method of principal component selection based on spatial statistics is proposed. The selection of principal components is performed by calculating the values of the semi-variogram parameter range and partial sill/sill of each principal component. The magnitude of a range is used to judge the range of spatial correlation of each principal component, and the partial sill/sill is used to judge the strength of spatial correlation of each principal component. The simulation proves that the variable range and partial sill/sill can effectively express the range and strength of spatial correlation of hyperspectral remote sensing images. Based on the experiment of real hyperspectral remote sensing images, the empirical threshold of principal component selection is determined from subjective and objective aspects, that is, the range is 2.5, and the partial sill/sill is 0.2. According to the classification results based on the support vector machine algorithm, compared with traditional methods, the principal components with better image quality can be screened by using variable range and partial sill/sill, which can not only achieve the purpose of dimensionality reduction, but also ensure high classification accuracy.

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    Knowledge-based remote sensing image fusion method
    KONG Ailing, ZHANG Chengming, LI Feng, HAN Yingjuan, SUN Huanying, DU Manfei
    Remote Sensing for Natural Resources. 2022, 34 (2): 47-55.   DOI: 10.6046/zrzyyg.2021179
    Abstract   HTML ( 76 )   PDF (3714KB) ( 305 )

    The remote sensing image fusion technology can combine multi-source images containing complementary information to obtain images with richer content and higher spectral quality, thus it is the key and foundation of remote sensing applications. Aiming at the problems of spectral distortion and spatial structure distortion that are prone to occur in the process of remote sensing image fusion, the knowledge-based remote sensing image FuseNet (RSFuseNet) was constructed based on the attention mechanism and using normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) as prior knowledge. Firstly, considering that the high-pass filtering can fully extract edge texture details, a high-pass filtering module was constructed to extract high-frequency details of panchromatic images. Secondly, NDVI and NDWI were extracted from multi-spectral images. Then, an adaptive squeeze-and-excitation (SE) module was constructed to recalibrate the input features. Finally, the adaptive SE module was combined with the convolution unit to perform fusion processing on the input features. The experiment was conducted using Gaofen 6 remote sensing image as the data source, and selecting Gram-Schmidt (GS) transformation, principal component analysis (PCA), a deep network architecture for pan-sharpening (PanNet), and pansharpening by convolutional neural networks (PNN) models as comparative models. The experimental results show that the peak signal to noise ratio (PSNR) index (40.5) and the structural similarity (SSIM) index (0.98) of the RSFuseNet model are better than those of comparative models, indicating that the method in this study has obvious advantages in remote sensing image fusion.

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    Construction of new vegetation water index based on PROSPECT-VISIR model
    WANG Jie, WANG Guanghui, LIU Yu, QI Jianwei, ZHANG Tao
    Remote Sensing for Natural Resources. 2022, 34 (2): 56-62.   DOI: 10.6046/zrzyyg.2021216
    Abstract   HTML ( 73 )   PDF (3541KB) ( 214 )

    In this paper, the leaf reflectance simulation data of visible to mid-infrared spectral range under the condition of different leaf parameters were obtained using the PROSPECT-VISIR leaf model. The spectral characteristic bands of vegetation leaves were analyzed to find the range of bands within which leaf reflectance is sensitive to changes in water content. On the basis of several common vegetation water indexes derived from visible and near-infrared bands, four new vegetation water index models were proposed by addition of the reflectance of the mid-infrared band, namely mid-infrared normalized difference infrared index (NDIIM), mid-infrared normalized difference water index (NDWIM), mid-infrared normalized multi-band drought index (NMDIM) and mid-infrared normalized difference vegetation index (NDVIM). Based on the leaf reflectance simulation data, the sensitivity of four new vegetation water indexes and that of common water index to leaf water content were compared, and the quantitative relationship model between, on one hand, new vegetation water index and, on the other hand, leaf water content and dry matter content, was established. The fitting degree of NMDIM was 0.972, showing the best performance. Finally, a leaf water content estimation model was developed based on two new vegetation water indexes NMDIM and NDIIM so that accurate estimation of leaf water content can be achieved even when the dry matter content is unknown (the root mean square error of the model was 0.002 1g/cm2).

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    Method to calibrate the coordinates of transmission towers based on satellite images
    MA Yutang, PAN Hao, ZHOU Fangrong, HUANG Ran, ZHAO Jianeng, LUO Jiqiang, LIU Jing, SUN Haoxuan, JIA Weijie, ZHANG Tao
    Remote Sensing for Natural Resources. 2022, 34 (2): 63-71.   DOI: 10.6046/zrzyyg.2021207
    Abstract   HTML ( 66 )   PDF (4996KB) ( 287 )

    In order to realize the refined line inspection management of transmission lines, improve its operation and maintenance efficiency, realize satellite intelligent inspection, and accurately find the defects and hidden dangers of towers and transmission lines, the paper took the coordinates of transmission line towers in Kunming City, Yunnan Province as an example and proposed a method to calibrate the coordinates of transmission towers using satellite images. The method first uses the reference base-map data as the basis to match the control points and uses the digital elevation model (DEM) to perform geometric correction on the original remote sensing image. Then combined with such technologies as shadow detection and edge detection and visual interpretation, the calibrated tower coordinates are obtained. The experiment verified the geometric correction accuracy of the SuperView-1 (SV1) and Gaofen-2(GF2) satellite images in the Kunming area, and the errors in the plane after correction were 0.931 and 1.387 m, respectively. In addition, the experiment verified the calibration accuracy of the old tower coordinates on the two lines. The results show that the plane accuracy of the tower has increased from 13.811 m and 8.256 m to 5.970 m and 5.104 m, respectively, which meets the basic power grid requirements. This method can realize the calibration of the tower coordinates, reduce the workload of manual inspection, and improve the efficiency of line inspection. With the explosive growth of remote sensing image data, multi-source images from the space and ground will continue to be combined, and the technology for the positioning of transmission towers based on satellite remote sensing images will have a broader development prospect.

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    A method for determining suitable scales for vegetation remote sensing based on the spatial distribution of leaves
    WU Haobo, WU Mengtong, YANG Siqi, FAN Wenjie, REN Huazhong
    Remote Sensing for Natural Resources. 2022, 34 (2): 72-79.   DOI: 10.6046/zrzyyg.2021148
    Abstract   HTML ( 75 )   PDF (2823KB) ( 241 )

    High spatial resolution remote sensing data serve as a new data source for quantitative remote sensing of vegetation, bringing in both new challenges and opportunities. The traditional leaf area index (LAI) inversion method based on the radiative transfer theory takes Beer-Lambert Law as the primary theoretical basis. The prerequisite for its application is that the leaf distribution in pixels follows a Poisson distribution. This study explored the appropriate scale in the case that the spatial distribution of continuous vegetation leaves in pixels follows a Poisson distribution. Focusing on the wheat canopy, this study used the LESS (LargE-Scale remote sensing data and image Simulation framework) software to simulate the remote sensing images of continuous wheat canopy. Based on this, this study analyzed the appropriate scale on which continuous wheat canopy leaves follow a Poisson distribution through the three-dimensional simulation of leaf canopy. Moreover, this study constructed a method for calculating the appropriate scale of the LAI inversion of continuous vegetation. The results show that the appropriate scale is influenced by the LAI value and the aggregation effect. The UAV hyperspectral data and the LAI inversion results from Luohe City, Henan Province validated the feasibility of this method.

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    A water body identification model for lakes in Hoh Xil based on GF-6 WFV satellite data
    WANG Renjun, LI Dongying, LIU Baokang
    Remote Sensing for Natural Resources. 2022, 34 (2): 80-87.   DOI: 10.6046/zrzyyg.2021125
    Abstract   HTML ( 83 )   PDF (3542KB) ( 249 )

    Exploration of the water body extraction model based on GF-6 satellite images can provide new data sources and technical support for water body monitoring. First, GF-6 WFV satellite data of Zonag Lake was used to analyze the reflectance difference between water and other ground objects in each band of GF-6 WFV satellite data. Based on this, a novel water index named red side water index (RSWI) was constructed. Then, the overall accuracy and Kappa coefficient generated by the confusion matrix were used to verify RSWI and the other three water extraction models, which include the single-band threshold method, normalized difference water index, and modified shade water index. Finally, six typical lakes with different types of areas larger than 100 km2 in Hoh Xil were selected for analysis of general applicability. The results showed that compared with other methods, the decision tree model composed of RSWI and NIR bands effectively eliminates the influence of lake bottom sediments on water bodies and extracts shallow water bodies more completely, with an overall accuracy of 93.78% and a Kappa coefficient of 0.87. Additionally, it has been found that the stability and general applicability of RSWI are better than those of other water body models with respect to different types of lakes.

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    Cloud detection algorithm of remote sensing image based on DenseNet and attention mechanism
    LIU Guangjin, WANG Guanghui, BI Weihua, LIU Huijie, YANG Huachao
    Remote Sensing for Natural Resources. 2022, 34 (2): 88-96.   DOI: 10.6046/zrzyyg.2021128
    Abstract   HTML ( 81 )   PDF (6594KB) ( 199 )

    The cloud detection of remote sensing images is the first step in the process of remote sensing image processing. To address the problem that the traditional cloud detection algorithm has a poor effect on the detection of small and thin clouds, this paper proposes a cloud detection method for densely connected network remote sensing images based on the attention mechanism. First, cloud vectors are manually checked from the images provided by the Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources and cloud labels are made, and the images are preprocessed by sequential clipping, color jitter, rotation, etc. to enlarge the sample size. Then, the pre-processed remote sensing images and their labels are fed into a neural network with DenseNet as the encoder and decoder, and a cascaded atrous convolution module is added between the encoder and decoder to increase the receptive field, and a dual attention mechanism and a global context modeling module are added to suppress some irrelevant detailed information. Finally, the experimental results showed that the accuracy rate could reach 95% and the intersection over union could reach 91%, which are big improvements over the traditional cloud detection algorithm, and this method performs well in extracting small and thin clouds.

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    High spatial resolution automatic detection of bridges with high spatial resolution remote sensing images based on random erasure and YOLOv4
    SUN Yu, HUANG Liang, ZHAO Junsan, CHANG Jun, CHEN Pengdi, CHENG Feifei
    Remote Sensing for Natural Resources. 2022, 34 (2): 97-104.   DOI: 10.6046/zrzyyg.2021130
    Abstract   HTML ( 70 )   PDF (7326KB) ( 304 )

    As a typical and important ground target, the bridge is the vital passage between transportation lines, so automatic detection of a bridge is of great social and economic significance. Deep learning has become a new way of bridge detection, but the detection accuracy for bridges obscured by cloud and mist is low. In order to solve this problem, an automatic bridge target detection method combining Random erase (RE) data enhancement and the YOLOv4 model is proposed: firstly, the scale range of the target in the data set is determined, and the candidate frame size is obtained by K-means clustering; secondly, the cloud obscuration is simulated by a combination of RE and mosaic data enhancement; thirdly, the enhanced data set is trained by YOLOv4 network; and finally, the mean Average Precision (mAP) is used to evaluate the experimental results. The experimental results show that the detection accuracy obtained by mAP is 97.06%, which is 2.99% higher than that of traditional YOLOv4, and the average detection accuracy of bridges obscured by a cloud is improved by 12%, which verifies the effectiveness and practicability of the proposed method.

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    Revision of solar radiation product ERA5 based on random forest algorithm
    WANG Xuejie, SHI Guoping, ZHOU Ziqin, ZHEN Yang
    Remote Sensing for Natural Resources. 2022, 34 (2): 105-111.   DOI: 10.6046/zrzyyg.2021151
    Abstract   HTML ( 88 )   PDF (3702KB) ( 363 )

    This study performed a multi-scale error analysis of the mean surface downward shortwave radiation flux product ERA5 (0.25° × 0.25°) of the European Centre for Medium-Range Weather Forecasts (ECMWF) using 93 pieces of solar radiation hourly data in 2013 of China. Subsequently, this study revised and analyzed the total radiation product ERA5 by training the random forest model using various relevant elements such as meteorological and geographic ones. Finally, the model was used to obtain the map of revised hourly radiation spatial distribution. As a result, the reanalyzed data can be better applied in industries such as agriculture, electric power, and urban construction. The results are as follows. ① The MAE, RMSE, and R values between the ERA5 solar radiation and the measured values of stations in 2013 were 27.60 W/m2, 29.87 W/m2, and 0.97 respectively. Moreover, the ERA5 values were higher than the measured values of stations. ② The accuracy was improved after the revision using the random forest model. After revision, the MAE, RMSE, and R values between the ERA5 solar radiation and the measured values of stations were 3.34 W/m2, 3.85 W/m2, and 1.00, respectively, indicating that correlation was significantly improved. ③ The spatial macroscopic distribution patterns of radiation before and after revision were consistent, but the ERA5 radiation value significantly decreased in local areas.

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    Residual dual regression network for super-resolution reconstruction of remote sensing images
    SHANG Xiaomei, LI Jiatian, LYU Shaoyun, YANG Ruchun, YANG Chao
    Remote Sensing for Natural Resources. 2022, 34 (2): 112-120.   DOI: 10.6046/zrzyyg.2021208
    Abstract   HTML ( 82 )   PDF (3859KB) ( 258 )

    In order to solve the problem of poor model generalizing ability in real super-resolution reconstruction of remote sensing images, which is easily caused by the use of artificial high-low resolution image pairs, combined with the residual in residual (RIR) module of residual channel attention network (RCAN), dual regression network (DRN) is improved, and residual dual regression network (RDRN) is proposed. Ten thousand 512 × 512 pixel images from LandCover.ai and DIOR aerial image data sets were selected to form the sample data set for training and testing the network, and the reconstruction results were compared with those of other super-resolution network models. The experimental results show that RDRN has an excellent performance in both reconstruction quality and model parameters. It can achieve a better super segmentation reconstruction effect with lower model complexity and has good generalization ability for different low-resolution remote sensing images.

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    Thick cloud removal of remote sensing images based on multi-reference image information fusion
    JIANG Sili, HUANG Wei, HUANG Rui
    Remote Sensing for Natural Resources. 2022, 34 (2): 121-127.   DOI: 10.6046/zrzyyg.2021209
    Abstract   HTML ( 69 )   PDF (3557KB) ( 264 )

    The cloud removal of remote sensing images is very important in the processing and analysis of remote sensing images and plays a crucial role in the subsequent image information extraction and other operations. Aiming at the high-quality requirements and low applicability of the reconstructed images in the cloud removal of multi-temporal remote sensing image fusion, a thick cloud removal algorithm based on one or more reference images was proposed, mainly including a selection of reference image, radiometric normalization, multi-temporal image fusion, and Poisson image editing. Firstly, the reference image was selected according to the image masking and the principal component information, and the radiometric normalization of the multi-source remote sensing image was carried out to preserve the change of ground feature information. Then, the image was fused based on the selective multi-source total variation model, and the boundary gradient discontinuity after image fusion was reduced by Poisson image editing. The experimental results show that the proposed method can effectively remove clouds from multi-source remote sensing images with thick clouds and different quality, and obtain higher image detail precision than traditional methods.

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    Lightweight DeepLabv3+ building extraction method from remote sensing images
    WANG Huajun, GE Xiaosan
    Remote Sensing for Natural Resources. 2022, 34 (2): 128-135.   DOI: 10.6046/zrzyyg.2021219
    Abstract   HTML ( 97 )   PDF (5417KB) ( 329 )

    Fast extraction of buildings with high accuracy from remote sensing images is an important research of remote sensing intelligent application services. To address the problems of imprecise segmentation of building edge in remote sensing images, holes in large-scale target segmentation, and a large amount of network parameters in the DeepLab model, a lightweight DeepLabv3+ model for building extraction from remote sensing images is proposed. In this method, the lightweight network MobileNetv2 is used to replace Xception, the backbone network of DeepLabv3+, so as to reduce the number of parameters and improve the training speed; The hole rate of hole convolution in ASPP is optimized to improve the effect of multi-scale semantic feature extraction. The improved model has been tested on WHU and Massachusetts data sets. The results show that the IOU and F1 score in WHU dataset are 82.37% and 92.89%, respectively, 2.71 percentage points and 2.14 percentage points higher than those of DeepLabv3+, 2.04 percentage points, and 2.32 percentage points higher than those of DeepLabv3+ in Massachusetts data set. The number of training parameters and training time is reduced, and the accuracy of the building extraction is effectively improved, which can meet the requirements of fast extraction of high-precision buildings.

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    Spatiotemporal evolution of impervious surface and the driving factors in Chenggong District,Kunming City
    LI Yimin, YANG Shuting, WU Bowen, LIANG Yuxi, MENG Yueyue
    Remote Sensing for Natural Resources. 2022, 34 (2): 136-143.   DOI: 10.6046/zrzyyg.2020187
    Abstract   HTML ( 86 )   PDF (5604KB) ( 363 )

    Impervious surface is a key factor to measure the urban ecological environment. It is of great significance for urban development planning to grasp the dynamic changes of impervious surfaces timely and accurately. Taking the Chenggong District of Kunming City as an example, based on the Landsat images in 2007, 2011, 2015, and 2019, the comparative study of normalized difference impervious surface index (NDISI) and modified soil adjusted vegetation index(MSAVI) was carried out to analyze the spatial and temporal evolution characteristics of impervious surface. The results showed that: ①As the extraction accuracy and Kappa coefficient of NDISI were 87.01% and 0.81, respectively, which were better than MSAVI’s 81.78% and 0.75, this paper selected the NDISI method to extract impervious surfaces in the Chenggong District;② the impervious surface area extracted in this paper increased from 46.12 km2 in 2007 to 72.64 km2 in 2011, 146.94 km2 in 2015 and 164.42 km2 in 2019, especially from 2011 to 2015, the impervious surface area had the fastest growth rate and nearly doubled. The changes to the impervious surface in Chenggong District are mainly influenced by such factors as national policies, urban planning, topographic factors, and traffic development. The impervious surface area along the Dianchi Lake in the west of Chenggong District and several administrative regions in the middle of Chenggong District developed rapidly, which brings certain pressure on the prevention and control of waterlogging in urban areas and the Dianchi Lake area. In the process of future urban planning, the expansion scope and speed of impervious surfaces should be well controlled to avoid ecological and environmental problems caused by the unreasonable spatial patterns of impervious surfaces.

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    Application of label clustering loss in the classification of remote sensing images
    SU Fu, YU Haipeng, ZHU Weixi
    Remote Sensing for Natural Resources. 2022, 34 (2): 144-151.   DOI: 10.6046/zrzyyg.2021147
    Abstract   HTML ( 101 )   PDF (3748KB) ( 208 )

    Scene information of remote sensing images has important application value in image interpretation and actual production and life in various fields. In view of the characteristics of remote sensing images with large intra-class differences and small inter-class differences, this paper further studies the center loss function and proposes a new label clustering loss function. Firstly, the class center is initialized by using the class label center initialization method. Secondly, the sinusoidal attenuation learning rate is used to keep the stability of the class center in the preheating stage. Then, Euclidean distance and cosine distance are used to gather the intra-class features and keep them away from the class center. Furthermore, two network models, VGG16 and ResNet50, are used to verify on NWPU-RESISC45 data set, and the accuracy is improved by 2.3% and 5.7% respectively. Experiments show that the method proposed in this paper can effectively cluster the features and separate class centers, and improve the accuracy of the network model, which has a certain development prospect in the classification of remote sensing images.

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    Classification of tea garden based on multi-source high-resolution satellite images using multi-dimensional convolutional neural network
    LIAO Kuo, NIE Lei, YANG Zeyu, ZHANG Hongyan, WANG Yanjie, PENG Jida, DANG Haofei, LENG Wei
    Remote Sensing for Natural Resources. 2022, 34 (2): 152-161.   DOI: 10.6046/zrzyyg.2021202
    Abstract   HTML ( 93 )   PDF (6846KB) ( 286 )

    The terrain conditions and tea plantation structure of Wuyishan City are complex, with cloudy and rainy weather, so it is difficult to obtain satellite images here. To address the problem of difficult extraction of tea gardens from a single image source, we investigated the spectral information of Sentinel-2 images and the texture features of Google images in Xintian Town, Wuyishan City, coupled with which a tea garden classification method based on multi-source high-resolution satellite images and multidimensional convolutional neural networks (MM-CNN) was established. In this method, tea gardens and suspected tea gardens were extracted, respectively, with two models developed with images with different spatial resolutions, based on one- and two-dimensional CNN. Results obtained with the two CNN models were combined, and the high-accuracy distribution of tea gardens in the study area was generated in a relatively economical and efficient way. The results showed that the spatial distribution accuracy of the tea gardens identified by MM-CNN is better than that of the single image source method. The MM-CNN method is highly universal and robust and provides a reference method for efficiently monitoring the distribution of tea gardens in large-scale hilly areas of South China.

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    Application of mining collapse recognition technology based on multi-source remote sensing
    YANG Xianhua, WEI Peng, LYU Jun, HAN Lei, SHI Haolin, LIU Zhi
    Remote Sensing for Natural Resources. 2022, 34 (2): 162-167.   DOI: 10.6046/zrzyyg.2021195
    Abstract   HTML ( 80 )   PDF (4617KB) ( 260 )

    Mining collapse has caused damage to soil, vegetation, and water resources. With the implementation of the national ecological restoration strategy, it is significant to effectively identify and monitor collapse areas. For this purpose, based on multi-source high-resolution remote sensing images and Sentinel-1 SAR radar images, this study identified and monitored the mining collapses of a coal mine in Baiyin City, Gansu Province using the two technologies, namely the Stacking-InSAR method for extracting ground subsidence data and the human-computer interactive interpretation of optical images of mining collapse. Moreover, this study comprehensively compared the characteristics of both techniques and explored the application prospects of both techniques in the deployment of ecological restoration engineering. The results are as follows: ① The Stacking-InSAR radar monitoring technology can better reflect the deformation during the monitoring period and can effectively identify the mining collapse areas in shallow, middle, and deep coal seams. ② The high-resolution optical image technology can better identify the mining collapse areas in shallow and middle coal seams, more accurately identify the damaged land, and can well identify the historically formed mining collapse areas and damaged land whose collapse deformation has stopped. ③ The collapse deformation and land damage of various stages can be obtained by combining the InSAR monitoring technology and the recognition method base on high-resolution remote sensing images, thus providing detailed and reliable basic data for ecological restoration engineering.

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    Remote sensing-based green space evolution in Tangshan and its influence on heat island effect
    WANG Siyao, ZHAO Chunlei, CHEN Xia, LIU Dan
    Remote Sensing for Natural Resources. 2022, 34 (2): 168-175.   DOI: 10.6046/zrzyyg.2021198
    Abstract   HTML ( 82 )   PDF (3435KB) ( 277 )

    The urban environment is an important issue in the whole world, and the urban heat island (UHI) effect is one of the important research topics. Owing to the expansion of the urban area and the increase in population, the urban heat island effect has also significantly changed. With the Landsat imageries as the data source and the central urban area of Tangshan City, Hebei Province as the main study area, this study analyzed the impacts of green space evolution on urban temperature change using the methods such as the radiative transfer equation algorithm, supervised classification, gravity center shift, and random sampling. The results are as follows. ① During the study period, the development direction and area of UHIs were roughly consistent with the scale and direction of rapid urban development. Moreover, the migration directions of the gravity centers of the UCI/UHIs were similar to those of the green space and urban area, with the migration distance of the gravity centers of UCIs greater than that of the UHIs. ② The urban green space (UGS) has been continuously lost during the study period, with the largest loss area of approximately 55.79 km2 occurring in agricultural land. Moreover, the largest increased area occurred in urban land and was approximately 47.85 km2. ③ The evolutionary trends of UCIs/UHIs were inconsistent with those of the UGS in different periods. This result may be related to the stock of green space. ④ The cooling effect on the urban surface (-0.16 ℃) induced by green space expansion was much smaller than the warming effect on the urban surface (6.37 ℃) caused by green space loss. The research results will provide a reference for urban planning in order to rationally arrange green space, retain sufficient green space, and effectively reduce the development speed of UHIs.

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    Application of the software development kit of GXL in the processing of domestic satellite data
    ZHANG Wei, ZHANG Tao, ZHENG Xiongwei, QI Jianwei, WANG Guanghui
    Remote Sensing for Natural Resources. 2022, 34 (2): 176-183.   DOI: 10.6046/zrzyyg.2021206
    Abstract   HTML ( 94 )   PDF (3974KB) ( 176 )

    The GXL (GeoImaging Accelerator) is a new generation of distributed processing platform for remote sensing data. It is fast, efficient, and flexible and plays an important role in the processing of domestic satellite data. This study investigated the software development kit (SDK) of GXL from the aspects of view, controller, and model based on the MVC (Model View Controller) framework of GXL. Furthermore, it developed a new algorithm processing module and employed distributed program deployment to enhance the function and algorithms of satellite data processing. An experiment was carried out to process domestic satellite (GF-1, GF-2, and ZY1-02C) data. The experiment results show that the SDK of GXL allows for flexibly expanding the processes for satellite data processing and improving the productivity of domestic satellite products. Therefore, the SDK of GXL can better satisfy the demands of various industries.

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    Analysis on water conservation function using remote sensing method in the Three Gorges Reservoir area (Chongqing section)
    YE Qinyu, YANG Shiqi, ZHANG Qiang, WANG Shu, HE Zeneng, ZHENG Yinghui
    Remote Sensing for Natural Resources. 2022, 34 (2): 184-193.   DOI: 10.6046/zrzyyg.2021182
    Abstract   HTML ( 89 )   PDF (5545KB) ( 278 )

    Water conservation is one of the most important functions of an ecosystem and can maintain and provide water resources for the ecosystem and humans. According to the physical meaning of water conservation, this study used leaf area index, vegetation coverage, and evapotranspiration to represent the water conservation of the vegetation layer and used surface temperature, soil moisture content, and slope to represent the water conservation capacity of the soil layer. Then, this study developed a remote sensing monitoring and evaluation model for water conservation through principal component analysis to explore the spatial-temporal distribution characteristics of the water conservation capacity in the Three Gorges reservoir area. The results show that the water conservation index (WCI) contained the objective information of various indices, could be used to quickly and conveniently assess the water conservation function in the Three Gorges Reservoir area, and properly represented the water conservation capacity there. In 2019, the water conservation capacity was unevenly distributed in the Three Gorges reservoir area and was high downstream and low upstream. The northeastern part of Chongqing was dominated by forest ecosystems and had the strongest water conservation function. From 2013 to 2019, the WCI slightly increased in most areas, especially in some parts of Fengdu, Kaizhou, and Yunyang areas.

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    Remote sensing evaluation of mine geological environment of Hainan Island in 2018 and ecological restoration countermeasures
    YIN Yaqiu, JIANG Cunhao, JU Xing, CHEN Keyang, WANG Jie, XING Yu
    Remote Sensing for Natural Resources. 2022, 34 (2): 194-202.   DOI: 10.6046/zrzyyg.2021136
    Abstract   HTML ( 96 )   PDF (6837KB) ( 400 )

    The exploitation of rich and unique mineral resources in Hainan Island has promoted economic growth but has also caused serious ecological environment problems. Analyzing the impacts of mining in Hainan Island and proposing suggestions on ecological restoration facilitate the protection and management of the ecological environment in Hainan Island. To this end, this study obtained the information on land destruction and ecological restoration of mines in Hainan Island using 2018 remote sensing images with high spatial resolution through image preprocessing, establishing interpretation indicators, and man-machine interactive interpretation. Specifically, with the information on land destruction and ecological restoration of mines as input, the assessment indicator system for mine geological environment was established based on 13 assessment factors of four categories, namely physical geography, basic geology, resource damage, and geological environment. Then, this study analyzed and assessed the effects of the geological environment of mines based on the analytic hierarchy process, obtaining the following results. The severely affected areas account for 0.22% of the total land area of Hainan Province and are mainly distributed in Wenchang City, Ledong Li Autonomous County, Xiuying District of Haikou City, Chengmai County, Lin’gao County, and Changjiang Li Autonomous County. The mine geological environment problems in these areas mainly include secondary geological disasters such as mining collapse of goaves and landslides caused by the mining of large-scale iron ore mines, as well as soil erosion and ecosystem degradation caused by the mining of coastal zirconium-titanium placers. The moderately severely affected areas account for 1.68% of the total land area of Hainan Province and are mainly distributed in Wenchang City, Danzhou City, Chengmai County, Qionghai City, Lin’gao County, Haikou City, and Dongfang City. The mine geological environment problems mainly include land damage caused by landslides induced by the mining of small- and medium-sized iron ore mines, as well as severe impacts on original terrain and landforms and the natural ecological environment caused by mining. The generally affected areas account for 4.93% of the total land area of Hainan Province and are mainly distributed in the coastal areas in the eastern part, the economically developed areas in the middle and northern parts, and the area with rich metallic minerals in the western part in Hainan Province. The mine geological environment problems in these areas mainly include the destruction of the surface landforms and natural vegetation caused by the mining of the scattered small nonmetal mines of building materials. This study proposed ecological restoration countermeasures targeting the different geological environment problems. For metal mines, it is suggested to primarily restore the ecosystem by natural restoration methods, supplemented by artificial restoration methods based on the elimination of geological hazards, soil improvement, and water environment management. For zirconium-titanium placers and nonmetal mines of building materials, it is recommended to restore vegetation to prevent water and soil erosion. For the coastal mine areas with severe desertification, it is recommended to gradually restore the ecosystem of the mining areas by growing crops such as watermelons and peanuts to improve soil and planting trees such as casuarina and Vatican hainanensis.

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    MODIS-based comprehensive assessment and spatial-temporal change monitoring of ecological quality in Beijing-Tianjin-Hebei region
    ZUO Lu, SUN Leigang, LU Junjing, XU Quanhong, LIU Jianfeng, MA Xiaoqian
    Remote Sensing for Natural Resources. 2022, 34 (2): 203-214.   DOI: 10.6046/zrzyyg.2021224
    Abstract   HTML ( 76 )   PDF (4467KB) ( 233 )

    Ecological quality assessment is an important prerequisite for guaranteeing the harmony and stability of the production and life of human beings and the ecological environment and for achieving the sustainable development of regional social economy. It has become a new trend to quickly, accurately, and objectively assess the regional ecological quality using use remote sensing technology. This study used the MODIS data of the Beijing-Tianjin-Hebei region in 2001, 2010, and 2019 to extract four important indices, namely, NDVI (greenness), LSM (humidity), NDBSI (dryness), and LST (heat). Then, this study obtained the MODIS remote sensing ecological index (RSEIM) using the principal component analysis method to conduct a comprehensive assessment and change monitoring of the ecological quality in the Beijing-Tianjin-Hebei region over the past 20 years. The results are as follows. ① The ecological quality of the Beijing-Tianjin-Hebei region shows distinct regional differences. The Yanshan Mountain in the north and the Taihang Mountain in the west have high ecological quality, while the Zhangjiakou area in the northwestern part of Hebei Province and the urban center in the southeastern part of Hebei Province suffer low ecological quality. ② In 2001, 2010, and 2019, the average RSEIM of the Beijing-Tianjin-Hebei region was 0.556, 0.583, and 0.527, respectively, with the overall ecological quality showing a downward trend. ③ From 2001 to 2019, the area with improved and degraded ecological quality in the Beijing-Tianjin-Hebei region accounted for 20.18% and 35.69% respectively, and the ecological quality in this region showed a pattern of improvement in the northwest and degradation in the southeast. The main reasons for the ecological improvement in the northwestern part of the region are the changes in water and heat conditions, such as an increase in precipitation and temperature, and a series of man-made protection measures. The reasons for ecological degradation in the southeastern part of the Beijing-Tianjin-Hebei region mainly include the rapid advancement of urbanization and the enhancement of social and economic activities. The comprehensive assessment of regional ecological quality can be effectively achieved based on MODIS data, thus providing a reference for the green and high-quality development of regional social economy.

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    Classification of tropical natural forests in Hainan Island based on multi-temporal Landsat8 remote sensing images
    ZHU Qi, GUO Huadong, ZHANG Lu, LIANG Dong, LIU Xuting, WAN Xiangxing
    Remote Sensing for Natural Resources. 2022, 34 (2): 215-223.   DOI: 10.6046/zrzyyg.2021156
    Abstract   HTML ( 77 )   PDF (4055KB) ( 311 )

    Tropical forests play a vital role in biodiversity conservation and research on global climate change. However, the complexity and diversity of vegetation types pose challenges to the fine remote sensing-based classification of tropical forests. The classification of tropical forests in the Jianfengling area, Hainan Province was analyzed using the multi-temporal Landsat8 data of the Google Earth Engine (GEE) platform. Based on the analysis of the impacts of the size and combination of multi-temporal data on the classification accuracy, this study proposed a classification method based on multi-temporal Landsat8 images for the vegetation type groups of tropical natural forests, such as typical tropical rain forest, tropical monsoon forest, and evergreen broad-leaved forests. The results are as follows. ① The classification accuracy of tropical natural forests was significantly improved as the size of multi-temporal data increased. The classification accuracy of the vegetation type groups of natural forests in Hainan Island reached 91%. ② When the multi-temporal data reached a certain size, the classification accuracy tended to be stable. Different combinations of multi-temporal data can improve the classification accuracy of tropical forests, especially when the classification accuracy of individual data involved was low. This finding reflects the broadness of the selection of temporal data. The proposed method, taking advantage of the temporal changes in remote sensing data, provides an effective reference for the remote sensing-based classification of tropical natural forests in Hainan Island.

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    Landslide identification using remote sensing images and DEM based on convolutional neural network: A case study of loess landslide
    YANG Zhaoying, HAN Lingyi, ZHENG Xiangxiang, LI Wenji, FENG Lei, WANG Yi, YANG Yongpeng
    Remote Sensing for Natural Resources. 2022, 34 (2): 224-230.   DOI: 10.6046/zrzyyg.2021204
    Abstract   HTML ( 98 )   PDF (4049KB) ( 343 )

    China is one of the countries with frequent landslide disasters. In recent years. In recent years, more than 70% of the catastrophic geological hazards have occurred not within the scope of known hidden danger points of geological hazards in China. Therefore, there is an urgent need for investigating large-scale landslide disasters using automatic and efficient technologies and methods for landslide identification. To quickly identify the location of landslides from massive remote sensing images, it is necessary to determine the key areas of landslides to support subsequent interpretation and research. This study investigated loess landslide identification based on GF-1 images and digital elevation model (DEM) data. First, a database of remote sensing images and DEM landslide samples was constructed. Second, the landslide samples were classified using the channel fusion convolutional neural network model. Finally, the classification results were restored to the remote sensing images according to the location information. Experimental results showed that the model yielded landslide identification accuracy of 95.7% and a recall rate of 100.0%. The model used in this study has a small number of network layers, a high convergence speed, and higher efficiency and identification accuracy. As a result, it allows for the quick identification of key landslide areas from remote sensing images in the case of a limited number of samples, thus supporting the investigation of large-scale landslide disasters.

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    Research on urban development and security in border areas of China based on deep learning
    MA Xiaoyu, ZHANG Xin, LIU Jilei, ZHOU Nan, LIU Kejian, WEI Chunshan, YANG Peng
    Remote Sensing for Natural Resources. 2022, 34 (2): 231-241.   DOI: 10.6046/zrzyyg.2021157
    Abstract   HTML ( 79 )   PDF (8546KB) ( 297 )

    In order to explore the development trend of border cities in China and assess the city’s border defense capability, the D-LinkNet34 deep learning algorithm is used to automate the extraction of buildings and roads in Tuolin, Shiquanhe and Pulan towns in Tibet Autonomous Region, and to analyze the development trend and border defense capability of border towns based on landscape index and population size. Analysis results show that: ① The extraction method based on D-LinkNet deep learning network can effectively further classify urban construction land, with average total progress of more than 80% and IOU above 70%.② The distribution of plaques in the towns of Pulan and Shiquanhe shows a trend of aggregation, and the trend of urban expansion weakened. The distribution of plaques in Tuolin Town shows a scattered trend, and the trend of urban expansion is obvious. ③ The building area is linearly related to the resident population, and the building area of Tuolin Town increased by about 68.75%from 2002 to 2018, and the resident population increased by about 39.00%. The building area of Shiquanhe Town increased by about 70.75% from 2004 to 2020, while the resident population increased by about 68.44%. The building area of Pulan Town increased by about 68.36% from 2005 to 2018, while the resident population increased by about 25.04%. This study provides a new method for quantitative evaluation of the expansion characteristics and border defense capability of border cities, as well as a reference for building China’s border defense capability.

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    Recognition of cotton distribution based on GF-2 images and Unet model
    ERPAN Anwar, MAMAT Sawut, MAIHEMUTI Balati
    Remote Sensing for Natural Resources. 2022, 34 (2): 242-250.   DOI: 10.6046/zrzyyg.2021135
    Abstract   HTML ( 96 )   PDF (5774KB) ( 331 )

    The typical crop cotton in the Ugan-Kuqa River Delta Oasis was used as the research object to study the applicability and optimization process of the deep learning method in the identification of cotton distribution in arid areas. Based on the domestic GF-2 images and the field survey data, the Unet deep learning method was adopted, in which the characteristics of the Unet network’s multiple convolution operations were fully utilized to explore the deep-level characteristics of cotton in remote sensing images, thereby improving the precision of cotton extraction. The results show that the recognition effect of the Unet model to extract cotton, corn, and peppers in the study area is better than the classification results of the object-oriented method and the traditional machine learning algorithms. The overall precision is 84.22%, and the Kappa coefficient is 0.804 7. Compared with the object-oriented method and the traditional machine learning algorithms SVM and RF, the overall precision has increased by 7.94 percentage points,11.93 percentage points, and 11.73 percentage points, respectively, and the Kappa coefficient has increased by 10.13%, 14.72%, and 14.60%, respectively. In the classification results of the Unet model, both the mapping precision and the user precision of cotton are higher than those of the other three methods, which are 94.95% and 89.07%, respectively. Therefore, it is feasible and reliable to use the Unet model to extract high-precision cotton spatial distribution information of arid areas on GF-2 high-resolution remote sensing images.

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    Monitoring of spatial-temporal dynamic changes in water surface in marshes based on multi-temporal Sentinel-1A data
    WEI Chang, FU Bolin, QIN Jiaoling, WANG Yanan, CHEN Zhihan, LIU Bing
    Remote Sensing for Natural Resources. 2022, 34 (2): 251-260.   DOI: 10.6046/zrzyyg.2021205
    Abstract   HTML ( 67 )   PDF (5944KB) ( 297 )

    Water is an important factor in the formation and maintenance of wetland ecosystems. Monitoring the changes in the water area of wetlands is of great significance for wetland conservation. Taking Sentinel-1A data from 2018 to 2019 as the data source, this study calculated the intra- and inter-annual synthetic aperture radar (SAR) backscattering coefficient (σ0) and coherence coefficient (μ0) images of the Zhalong Wetland. Then, this study assigned weights according to the proximity to water bodies of color optical images and extracted the weighted images of σ0 and μ0. Finally, this study extracted the wetland water bodies using the threshold segmentation method and random forest algorithm. The purpose is to monitor the dynamic variations in the wetland water area and explore the intra- and inter-annual variation rules of the wetland water body. The results are as follows. The random forest algorithm yielded the highest extraction accuracy of water bodies, with an absolute value of the mean difference of representative months was 6.69 km2. The threshold segmentation method based on μ0 images yielded the lowest classification accuracy of water bodies, with an absolute value of the mean difference of 13.07 km2. Overall, the intra-annual water area of the Zhalong Wetland showed significant seasonal variations during 2018—2019. The water area fluctuated in the ranges of 1 300~1 600 km2 during late spring and early summer and 700~900 km2 during late summer and early autumn. The inter-annual water area varied with conditions such as climate and temperature. In particular, the wetland water area in October and November 2019 was approximately 1 050 km2 greater than that in 2018 due to large amounts of rainfall. As shown by the calculation based on effective data, the water area in 2019 was about 550 km2 greater than that in 2018 in the Zhalong Wetland.

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    Spatiotemporal analysis of drought in sugarcane planting areas of Guangxi by remote sensing
    QIN Wen, HUANG Qiuyan, QIN Zhihao, LIU Jianhong, WEI Gaoyang
    Remote Sensing for Natural Resources. 2022, 34 (2): 261-270.   DOI: 10.6046/zrzyyg.2021191
    Abstract   HTML ( 81 )   PDF (5588KB) ( 236 )

    Guangxi is the most important sugarcane producing area in China. Remote sensing of drought in sugarcane planting areas is of great practical significance to decision-making for the anti-drought campaign in Guangxi where sugarcane planting is mainly on the farmland without irrigation guarantee. This study intends to examine the issue of remote sensing monitoring of drought in sugarcane planning areas in Guangxi. MODIS data product MOD16/MYD16 was used to calculate the drought intensity index (DSI) in Guangxi sugarcane planting areas for a period from 2002 to 2018. On the basis of this calculation, the spatiotemporal characteristics of different drought grades in Guangxi sugarcane planting areas were analyzed. In order to reveal the changing trend of the drought severity in the sugarcane planning areas, three methods, i.e. unary linear regression, Mann-Kendall trend test and Hurst index, were used in the study to analyze the change trend of drought in the sugarcane planting areas of Guangxi. The results showed that the drought in Guangxi during the period from 2002 to 2018 mainly happened in the center, the southeast, the southwest, and the northwest sugarcane areas. The annual average of DSI was -0.59 during the period. Very high DSI was observed in two years, i.e. 2010 and 2002, implying that sugarcane planting in Guangxi experienced the most severe drought in the two years. Very low DSI was seen in 2016, indicating the minimal impact of drought on the planting areas this year. The change of DSI in the sugarcane planting areas of Guangxi showed a trend of a slight decrease from 2002 to 2018, with an annual rate of -0.07%. In terms of the spatial center of gravity of drought, the center of gravity of drought areas in different growth periods showed a trend of expansion from the center to the northwest, and the path of gravity center shift was as follows: central planting area > southwest planting area > northwest planting area. From the perspective of years, DSI in each year revealed a remarkable fluctuation during the period in question. Sharp changes in DSI might also be seen in some years, implying that drought had variability among years in Guangxi. The spatiotemporal variation of drought in Guangxi is obviously related to the climate change in the areas. Therefore we believe that sugarcane planting in Guangxi might continuously face the challenges from the frequent occurrence of the drought of various grades as a result of future climate change. Remote sensing monitoring of the drought can provide useful information on drought events and their dynamics for anti-drought campaigns to reduce the impact of drought on sugarcane planting in Guangxi.

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    Time series calculation of remote sensing ecological index based on GEE
    LUO Hongjian, MING Dongping, XU Lu
    Remote Sensing for Natural Resources. 2022, 34 (2): 271-277.   DOI: 10.6046/zrzyyg.2021150
    Abstract   HTML ( 85 )   PDF (2842KB) ( 279 )

    Ecological evaluation plays an important role in supporting urban development planning and using a remote sensing index to carry out ecological evaluation is a feasible method. Today, with the development of cloud computing, this paper explores a time-series calculation method of remote sensing ecological index suitable for Google Earth Engine, to address the problem that the calculation results of different sensors differ greatly in the process of big data calculation. Firstly, by taking Kuitun City, Xinjiang Uygur Autonomous Region, as the study area, this paper performs the de-clouded fusion process on Landsat images from 1989 to 2019. Secondly, this paper calculates the four major components of the fused images and makes preferences in the calculation of the humidity component and temperature component. Finally, this paper proposes the normalization method of the overall optimum and calculates the remotely sensed ecological index for each year on this basis. The analysis of the obtained results shows that the first principal component under the calculation by this method has a higher contribution rate, and the time series results on this basis have a higher polynomial fitting effect. It indicates that the method can specify uniform standards for different sensors, enhance the comparability of calculated results between different sensors, optimize the calculated results of remote sensing ecological indices, and ensure the interpretability of ecological evaluation grading results.

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    Response mechanism of ecosystem service value to urban and rural construction land expansion in the three outlets of the southern Jingjiang River
    LI Xia, LI Jingzhi
    Remote Sensing for Natural Resources. 2022, 34 (2): 278-288.   DOI: 10.6046/zrzyyg.2021180
    Abstract   HTML ( 129 )   PDF (5734KB) ( 209 )

    In view of the shortcomings of the current research on the response mechanism of ecosystem services value to urban and rural construction land expansion, By taking the three outlets of southern Jingjiang River as the research object, this study calculates the ecosystem service value of the three outlets of southern Jingjiang River by equivalent factor method, analyzes the temporal and spatial variation characteristics of ecosystem service value, and analyzes the response mechanism of ecosystem service value to urban and rural construction land expansion by linear regression method. The results show that from 1990 to 2018, ESV in the three outlets of southern Jingjiang River increased from 44.356 billion yuan to 47.103 billion yuan, which experienced a gentle growth process as a whole. The spatial difference of ESV was obvious. Specifically, the area of the high- and medium-value areas decreased rapidly, and the area of the low-value areas increased rapidly. The expansion of construction land was more significant, with a total expansion of 27.74 km2, which experienced the fluctuating expansion process of "slow-negative-slow-rapid- decelerated expansion". Construction land occupied different types of land, which has different degrees of impact on the loss and gain values of all kinds of ecosystem services, and the greater the proportion of occupation high ecological value land (water area, wetland), the higher the damage to the ecology. In the future use of the land of the three outlets of southern Jingjiang River, the traditional expansion mode of construction land should be changed, the boundary of urban land use should be reasonably planned, the red line of arable land should be strictly observed, and the level of intensive land use should be improved. At the same time, we should improve the management system of rivers and lakes, make more efforts on the ecological protection and restoration of rivers and lakes, and improve the ecological service function for rivers and lakes.

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    Monitoring of nighttime light pollution in Nanjing City based on Luojia 1-01 remote sensing data
    LI Jiayi, XU Yongming, CUI Weiping, WU Yuyang, WANG Jing, SU Boyang, JI Meng
    Remote Sensing for Natural Resources. 2022, 34 (2): 289-295.   DOI: 10.6046/zrzyyg.2020380
    Abstract   HTML ( 132 )   PDF (4159KB) ( 387 )

    To obtain the distribution of nighttime light pollution on a city scale, this study monitors the nighttime light pollution in Nanjing City based on Luojia 1-01 nighttime light remote sensing images. The apparent radiance of the remote sensing images was converted into the surface incident luminance according to surface reflectance and building coverage ratio. Based on this and the illuminance values observed in the field, various empirical models were established to calculate the nighttime illuminance of Nanjing City. Finally, the distribution of nighttime light pollution in Nanjing City was analyzed according to the calculated nighttime illuminance. The results show that the third-order polynomial regression model had the highest accuracy, with a determination coefficient of 0.87 and a mean absolute error (MAE) of 4.71 lx. The nighttime illuminance in Nanjing City varied in the range of 0~55 lx, with obvious spatial distribution differences. In general, the areas with high illuminance were mainly concentrated in the main urban area and the illuminance showed a decreasing trend from the main urban area to the surrounding area. Light pollution was the most serious in Gulou and Qinhuai districts, where light pollution covered more than 70% in terms of area. The light pollution in the suburb was relatively weak, and the three districts with the weakest light pollution included Gaochun, Lishui, and Liuhe districts successively, where light pollution covered less than 4% in terms of area. Some areas in Nanjing City showed extremely high illuminance (> 30 lx), including large shopping malls, large factories, traffic hubs, roads, and some residential areas. It should be noted that there are many residential areas near these places except for traffic hubs and large factories. This study explored a method of monitoring urban light pollution at night based on Luojia 1-01 remote sensing data. It will provide data support for the light pollution control and management in Nanjing City and a scientific reference for the light pollution monitoring in other areas.

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