Please wait a minute...
 
Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 160-169     DOI: 10.6046/zrzyyg.2022173
|
Optimizing an ICESat2-based remote sensing estimation model for the leaf area index of mountain forests in southwestern China
XI Lei(), SHU Qingtai(), SUN Yang, HUANG Jinjun, SONG Hanyue
College of Forestry, Southwest Forestry University, Kunming 650224, China
Download: PDF(4456 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The leaf area index (LAI) is a critical parameter for the forest ecosystem. Improving the remote sensing estimation accuracy of the regional LAI of mountain forests at a low cost is of great significance for accurately determining the LAIs of forests and for further understanding the forest ecosystem. With spaceborne LiDAR ICESat-2/ATLAS data as a primary information source, this study investigated Shangri-La City in mountainous areas in southwestern China. Based on the remote sensing estimation model using random forest (RF) regression, RF hyperparameter optimization, and the data of 51 measured sample plots of LAI, this study analyzed the estimation effects of the model using accuracy evaluation indicators such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MedAE). The results are as follows: The hyperparameter optimization of the RF regression model using a random surface search algorithm can significantly improve the estimation accuracy of LAI. The extracted characteristic parameters of ground spots showed high contribution and excellent effects in the LAI estimation of mountain forests. Therefore, they can be applied to the estimation of regional LAI of mountain forests. The RF regression model optimized using the random surface search algorithm yielded higher estimation accuracy. The estimation results were consistent with the forest distribution in the study area, indicating certain generality. Finally, this study determined that it is feasible to employ ICESat-2/ATLAS data products for LAI estimation, providing a reference for medium- to large-scale LAI estimation based on spaceborne LiDAR.
Keywords ICESat-2/ATLAS      leaf area index      hyperparameter optimization      random forest      Shangri-La     
ZTFLH:  TP79  
  S771.8  
Issue Date: 19 September 2023
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Lei XI
Qingtai SHU
Yang SUN
Jinjun HUANG
Hanyue SONG
Cite this article:   
Lei XI,Qingtai SHU,Yang SUN, et al. Optimizing an ICESat2-based remote sensing estimation model for the leaf area index of mountain forests in southwestern China[J]. Remote Sensing for Natural Resources, 2023, 35(3): 160-169.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022173     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/160
Fig.1  Location and plot of the study area
样地
数量		 均值 均值标
准差		 标准差 最大值 最小值 中位数
51 0.468 1 0.038 8 0.265 8 0.967 0 0.012 1 0.509 0
Tab.1  Summary of LAI statistics for sample sites
Fig.2  Study flowcharts
Fig.3  Flow chart of noise elimination algorithm
Fig.4  Flow chart of photon classification algorithm
Fig.5  Effective spot diagram of the study area
参数名 描述 类型
n_estimators 决策树的数量 整数型
min_samples_split 节点可分的最小样本数 整数或浮点型
min_samples_leaf 叶子节点含有的最少样本 整数或浮点型
max_features 构建决策树最优模型时考虑的最大特征数 整数或浮点型
max_depth 决策树最大深度 整数型
bootstrap 样本集是否放回抽样 布尔型
Tab.2  Description of the parameters of the random forest algorithm
Fig.6  Proportion of importance contribution of modelling parameters
参数名 描述 数值/%
photon_rate_can 计算后每100 m段内冠层光子的光子率 19.38
n_toc_photons 区段内冠层顶部光子数 7.81
n_ca_photons 区段内冠层光子数 5.66
asr 表观反射率 4.20
h_median_canopy 区段内个体相对冠层高的中位数 3.62
solar_azimuth 太阳方位角 3.34
solar_elevation 太阳高度角 2.92
toc_roughness 区段内冠层顶光子相对高度的标准偏差 2.73
h_min_canopy 区段内冠层高度的最小值 2.56
dem_h 地理定位点处的最佳可用DEM值 2.55
Tab.3  Statistics on the contribution of modeling parameters of the unoptimized random forest model
Fig.7  Point line diagram of the random forest model fit
Fig.8  Proportion of importance contribution of modelling parameters after optimization
参数名 描述 数值/%
photon_rate_can 计算后每100 m段内冠层光子的光子率 32.22
toc_roughness 区段内冠层顶光子相对高度的标准偏差 13.39
asr 表观反射率 6.41
snr 定位光子的信噪比 6.19
h_te_median 在WGS84椭球体上方的光子高度的中值(分类为地形区段内) 4.86
h_te_max 在WGS84椭球体上方的光子高度的最大值(分类为地形区段内) 4.73
h_te_min 在WGS84椭球体上方的光子高度的最小值(分类为地形区段内) 4.61
canopy_openness 区段内冠层光子与段内所有光子的标准差(可推断冠层开放度) 4.41
h_te_rh25 25%分位数高度处的地形高度值 4.25
segment_landcover IGBP地表覆盖类型 4.17
Tab.4  Statistics on the contribution of modeling parameters to the random forest model after hyperparameter optimization
Fig.9  Point line diagram of the random forest model fit after optimization
Fig.10  Scatterplot of random forest model fit
Fig.11  Spatial distribution of ICESat-2 spot LAI in the study area
[1] Chen J M, Menges C H, Leblanc S G. Global mapping of foliage clumping index using multi-angular satellite data[J]. Remote Sensing of Environment, 2005, 97(4):447-457.
doi: 10.1016/j.rse.2005.05.003 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425705001550
[2] Friedl M A, Sulla-Menashe D, Tan B, et al. MODIS collection 5 global land cover:Algorithm refinements and characterization of new datasets[J]. Remote Sensing of Environment, 2010, 114(1):168-182.
doi: 10.1016/j.rse.2009.08.016 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425709002673
[3] 林晓娟. 基于ICESat-2和GEDI森林冠层高度和森林地上生物量遥感诊断[D]. 北京: 中国科学院大学(中国科学院空天信息创新研究院), 2021.
[3] Lin X J. Remote sensing diagnosis of forest canopy height and forest aboveground biomass based on ICESat-2 and GEDI[D]. Beijing: University of Chinese Academy of Sciences (Aerospace Information Research Institute,Chinese Academy of Sciences), 2021.
[4] Moussavi M S, Abdalati W, Scambos T, et al. Applicability of an automatic surface detection approach to micro-pulse photon-counting LiDAR altimetry data:Implications for canopy height retrieval from future ICESat-2 data[J]. International Journal of Remote Sensing, 2014, 35(13):5263-5279.
doi: 10.1080/01431161.2014.939780 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2014.939780
[5] Liu M S, Xing Y Q, Wu H B, et al. Study on mean forest canopy height estimation based on ICESat-GLAS waveforms[J]. Forest Research, 2014, 27(3):309-315.
[6] García M, Popescu S, Riaño D, et al. Characterization of canopy fuels using ICESat/GLAS data[J]. Remote Sensing of Environment, 2012, 123:81-89.
doi: 10.1016/j.rse.2012.03.018 url: https://linkinghub.elsevier.com/retrieve/pii/S003442571200137X
[7] Luo S, Wang C, Li G, et al. Retrieving leaf area index using ICESat/GLAS full-waveform data[J]. Remote Sensing Letters, 2013, 4(8):745-753.
doi: 10.1080/2150704X.2013.790573 url: http://www.tandfonline.com/doi/abs/10.1080/2150704X.2013.790573
[8] Neumann T A, Martino A J, Markus T, et al. The ice,cloud,and land elevation satellite-2 mission:A global geolocated photon product derived from the advanced topographic laser altimeter system[J]. Remote Sensing of Environment, 2019, 233:111325.
doi: 10.1016/j.rse.2019.111325 url: https://linkinghub.elsevier.com/retrieve/pii/S003442571930344X
[9] Huang X, Xie H, Liang T, et al. Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau[J]. International Journal of Remote Sensing, 2011, 32(18):5177-5196.
doi: 10.1080/01431161.2010.495092 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2010.495092
[10] 董佳臣, 倪文俭, 张志玉, 等. ICESat-2植被冠层高度和地表高程数据产品用于森林高度提取的效果评价[J]. 遥感学报, 2021, 25(6):1294-307.
[10] Dong J C, Ni W J, Zhang Z Y, et al. Performance of ICESat-2 ATL08 product on the estimation of forest height by referencing to small footprint LiDAR data[J]. National Remote Sensing Bulletin, 2021, 25(6):1294-1307.
doi: 10.11834/jrs.20219449 url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20219449/
[11] Huang J P, Xing Y Q, Qin L. Review of noise filtering algorithm for photon data[J]. The International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences, 2020, 42:105-110.
[12] Gwenzi D, Lefsky M A, Suchdeo V P, et al. Prospects of the ICESat-2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 118:68-82.
doi: 10.1016/j.isprsjprs.2016.04.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271616300491
[13] 张欢, 李弘毅, 李浩杰, 等. 基于机载LiDAR的高寒山区遥感高程数据精度评估[J]. 遥感技术与应用, 2021, 36(6):1311-1320.
[13] Zhang H, Li H Y, Li H J, et al. Accuracy evaluation of remote sensing elevation data in alpine mountains based on airborne LiDAR[J]. Remote Sensing Technology and Application, 2021, 36(6):1311-1320.
[14] Neuenschwander A L, Magruder L A. The potential impact of vertical sampling uncertainty on ICESat-2/ATLAS terrain and canopy height retrievals for multiple ecosystems[J]. Remote Sensing, 2016, 8(12):1039.
doi: 10.3390/rs8121039 url: http://www.mdpi.com/2072-4292/8/12/1039
[15] Narine L L, Popescu S, Neuenschwander A, et al. Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data[J]. Remote Sensing of Environment, 2019, 224:1-11.
doi: 10.1016/j.rse.2019.01.037
[16] Zhang J, Tian J, Li X, et al. Leaf area index retrieval with ICESat-2 photon counting LiDAR[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 103:102488.
doi: 10.1016/j.jag.2021.102488 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243421001951
[17] Shi L, Zhao H, Li Y, et al. Evaluation of Shangri-La County’s tourism resources and ecotourism carrying capacity[J]. International Journal of Sustainable Development and World Ecology, 2015, 22(2):103-109.
doi: 10.1080/13504509.2014.927018 url: http://www.tandfonline.com/doi/abs/10.1080/13504509.2014.927018
[18] 俄相颖, 戴光耀, 吴松华. ICESat-2 ATL03数据预处理及校正方法[J]. 红外与激光工程, 2021, 50(6):92-102.
[18] E X Y, Dai G Y, Wu S H. ICESat-2 ATL03 data preprocessing and correction method[J]. Infrared and Laser Engineering, 2021, 50(6):92-102.
[19] Magruder L, Neumann T, Kurtz N. ICESat-2 early mission synopsis and observatory performance[J]. Earth and Space Science, 2021, 8(5): e2020EA001555.
[20] Neuenschwander A, Guenther E, White J C, et al. Validation of ICESat-2 terrain and canopy heights in boreal forests[J]. Remote Sensing of Environment, 2020, 251:112110.
doi: 10.1016/j.rse.2020.112110 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425720304831
[21] Parrish C E, Magruder L A, Neuenschwander A L, et al. Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS’s bathymetric mapping performance[J]. Remote Sensing, 2019, 11(14):1634.
doi: 10.3390/rs11141634 url: https://www.mdpi.com/2072-4292/11/14/1634
[22] Wang C, Zhu X, Nie S, et al. Ground elevation accuracy verification of ICESat-2 data:A case study in Alaska,USA[J]. Optics Express, 2019, 27(26):38168-38179.
doi: 10.1364/OE.27.038168 url: https://opg.optica.org/abstract.cfm?URI=oe-27-26-38168
[23] Ghosh S M, Behera M D, Paramanik S. Canopy height estimation using Sentinel series images through machine learning models in a mangrove forest[J]. Remote Sensing, 2020, 12(9):1519.
doi: 10.3390/rs12091519 url: https://www.mdpi.com/2072-4292/12/9/1519
[24] 马山木, 甘甫平, 吴怀春, 等. ICESat-2数据监测青藏高原湖泊2018—2021年水位变化[J]. 自然资源遥感, 2022, 34(3):164-172.doi:10.6046/zrzyyg.2021329.
doi: 10.6046/zrzyyg.2021329
[24] Ma S M, Gan F P, Wu H C, et al. Monitoring lake level changes on the Tibetan Plateau from 2018 to 2021 using ICESat-2 data[J]. Remote Sensing for Natural Resources, 2022, 34(3):164-172.doi:10.6046/zrzyyg.2021329.
doi: 10.6046/zrzyyg.2021329
[25] Shen X, Ke C Q, Yu X, et al. Evaluation of ice,cloud,and land elevation satellite-2 (ICESat-2) land ice surface heights using airborne topographic mapper (ATM) data in Antarctica[J]. International Journal of Remote Sensing, 2021, 42(7):2556-2573.
doi: 10.1080/01431161.2020.1856962 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2020.1856962
[26] Chen Y, Ma L, Yu D, et al. Improving leaf area index retrieval using multi-sensor images and stacking learning in subtropical forests of China[J]. Remote Sensing, 2021, 14(1):148.
doi: 10.3390/rs14010148 url: https://www.mdpi.com/2072-4292/14/1/148
[27] Fang H, Baret F, Plummer S, et al. An overview of global leaf area index (LAI):Methods,products,validation,and applications[J]. Reviews of Geophysics, 2019, 57(3):739-799.
doi: 10.1029/2018RG000608 url: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018RG000608
[28] Chen B, Pang Y, Li Z, et al. Ground and top of canopy extraction from photon-counting LiDAR data using local outlier factor with ellipse searching area[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9):1447-1451.
doi: 10.1109/LGRS.8859 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859
[29] Nie S, Wang C, Xi X, et al. Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data[J]. Optics Express, 2018, 26(10):A520-A540.
doi: 10.1364/OE.26.00A520 url: https://opg.optica.org/abstract.cfm?URI=oe-26-10-A520
[30] An M, Xing W, Han Y, et al. The optimal soil water content models based on crop-LAI and hyperspectral data of winter wheat[J]. Irrigation Science, 2021, 39(6):687-701.
doi: 10.1007/s00271-021-00745-z
[31] Zhang J, Kerekes J. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 12(4):726-730.
doi: 10.1109/LGRS.2014.2360367 url: http://ieeexplore.ieee.org/document/6918368/
[32] Xia S, Wang C, Xi X H, et al. Point cloud filtering and tree height estimation using airborne experiment data of ICESat-2[J]. Remote Sensing, 2014, 18(4):1199-1207.
[33] 陆大进, 黎东, 朱笑笑, 等. 基于卷积神经网络的ICESat-2光子点云去噪分类[J]. 地球信息科学学报, 2021, 23(11):2086-2095.
doi: 10.12082/dqxxkx.2021.210103
[33] Lu D J, Li D, Zhu X X, et al. Denoising and classification of ICESat-2 photon point cloud based on convolutional neural network[J] Journal of Geo-Information Science, 2021, 23(11):2086-2095.
[34] Zhu X, Nie S, Wang C, et al. A ground elevation and vegetation height retrieval algorithm using micro-pulse photon-counting LiDAR data[J]. Remote Sensing, 2018, 10(12):1962.
doi: 10.3390/rs10121962 url: http://www.mdpi.com/2072-4292/10/12/1962
[35] Tang H, Dubayah R, Swatantran A, et al. Retrieval of vertical LAI profiles over tropical rain forests using waveform LiDAR at La Selva,Costa Rica[J]. Remote Sensing of Environment, 2012, 124(9):242-250.
doi: 10.1016/j.rse.2012.05.005 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425712002088
[36] Nie S, Wang C, Dong P, et al. A revised progressive TIN densification for filtering airborne LiDAR data[J]. Measurement, 2017, 104:70-77.
doi: 10.1016/j.measurement.2017.03.007 url: https://linkinghub.elsevier.com/retrieve/pii/S0263224117301537
[37] Axelsson P. DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 33(4):110-117.
[38] Zhang L, Zeng Y, Zhuang R, et al. In situ observation-constrained global surface soil moisture using random forest model[J]. Remote Sensing, 2021, 13(23):4893.
doi: 10.3390/rs13234893 url: https://www.mdpi.com/2072-4292/13/23/4893
[39] Zhu X, Wang C, Nie S, et al. Mapping forest height using photon-counting LiDAR data and Landsat8 OLI data:A case study in Virginia and North Carolina,USA[J]. Ecological Indicators, 2020, 114:106287.
doi: 10.1016/j.ecolind.2020.106287 url: https://linkinghub.elsevier.com/retrieve/pii/S1470160X20302247
[40] Zhou R, Yang C, Li E, et al. Object-based wetland vegetation classification using multi-feature selection of unoccupied aerial vehicle RGB imagery[J]. Remote Sensing, 2021, 13(23):4910.
doi: 10.3390/rs13234910 url: https://www.mdpi.com/2072-4292/13/23/4910
[41] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn:Machine learning in Python[J]. The Journal of Mmachine Learning Research, 2011, 12:2825-2830.
[42] Narine L L, Popescu S C, Malambo L. Using ICESat-2 to estimate and map forest aboveground biomass:A first example[J]. Remote Sensing, 2020, 12(11):1824.
doi: 10.3390/rs12111824 url: https://www.mdpi.com/2072-4292/12/11/1824
[43] Narine L L, Popescu S C, Malambo L. Synergy of ICESat-2 and Landsat for mapping forest aboveground biomass with deep learning[J]. Remote Sensing, 2019, 11(12):1503.
doi: 10.3390/rs11121503 url: https://www.mdpi.com/2072-4292/11/12/1503
[44] Su T, Spicer R A, Wu F X, et al. A middle Eocene lowland humid subtropical “Shangri-La” ecosystem in central Tibet[J]. Proceedings of the National Academy of Sciences, 2020, 117(52):32989-32995.
doi: 10.1073/pnas.2012647117 url: https://pnas.org/doi/full/10.1073/pnas.2012647117
[45] Li W, Niu Z, Shang R, et al. High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1,Sentinel-2 and Landsat8 data[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 92:102163.
doi: 10.1016/j.jag.2020.102163 url: https://linkinghub.elsevier.com/retrieve/pii/S030324342030026X
[46] Silva C A, Duncanson L, Hancock S, et al. Fusing simulated GEDI,ICESat-2 and NISAR data for regional aboveground biomass mapping[J]. Remote Sensing of Environment, 2021, 253:112234.
doi: 10.1016/j.rse.2020.112234 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425720306076
[47] 夏恒, 汤健, 乔俊飞. 深度森林研究综述[J]. 北京工业大学学报, 2022, 48(2):182-196.
[47] Xia H, Tang J, Qiao J F. Review of deep forest[J]. Journal of Beijing University of Technology, 2022, 48(2):182-196.
[1] WU Weichao, YE Fawang. Cloud detection of Sentinel-2 images for multiple backgrounds[J]. Remote Sensing for Natural Resources, 2023, 35(3): 124-133.
[2] PARIHA Helili, ZAN Mei. Spatio-temporal changes and influencing factors of ecological environments in oasis cities of arid regions[J]. Remote Sensing for Natural Resources, 2023, 35(3): 201-211.
[3] LIANG Jintao, CHEN Chao, ZHANG Zili, LIU Zhisong. A random forest-based method integrating indices and principal components for classifying remote sensing images[J]. Remote Sensing for Natural Resources, 2023, 35(3): 35-42.
[4] WU Yuxin, WANG Juanle, HAN Baomin, YAN Xinrong. Forest classification for Motuo County: A method based on spatio-temporal-spectral characteristics[J]. Remote Sensing for Natural Resources, 2023, 35(1): 180-188.
[5] ZHANG Hao, GAO Xiaohong, SHI Feifei, LI Runxiang. Sentinel-2 MSI and Sentinel-1 SAR based information extraction of abandoned land in the western Loess Plateau:A case study of Minhe County in Qinghai[J]. Remote Sensing for Natural Resources, 2022, 34(4): 144-154.
[6] WANG Chunxia, ZHANG Jun, LI Yixu, PHOUMILAY. The construction and verification of a water index in the complex environment based on GF-2 images[J]. Remote Sensing for Natural Resources, 2022, 34(3): 50-58.
[7] DENG Jingwen, TIAN Yichao, ZHANG Qiang, TAO Jin, ZHANG Yali, HUANG Shengguang. Application of airborne LiDAR in the estimation of the mean height of mangrove stand[J]. Remote Sensing for Natural Resources, 2022, 34(3): 129-137.
[8] WANG Xuejie, SHI Guoping, ZHOU Ziqin, ZHEN Yang. Revision of solar radiation product ERA5 based on random forest algorithm[J]. Remote Sensing for Natural Resources, 2022, 34(2): 105-111.
[9] WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[10] LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[11] GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[12] LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[13] XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
[14] WU Qian, JIANG Qigang, SHI Pengfei, ZHANG Lili. The estimation of soil calcium carbonate content based on Hyperspectral data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 138-144.
[15] WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech