GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

毛丽君; 李明诗

doi:10.13203/j.whugis20200633

GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

doi: 10.13203/j.whugis20200633

毛丽君^1,2,
李明诗^1,3, ,

1 南京林业大学林学院, 江苏南京, 210037;
2 南京森林警察学院刑事科学技术学院, 江苏南京, 210023;
3 南京林业大学南方林业协同创新中心, 江苏南京, 210037

基金项目:

中央高校基本科研业务费专项资金（LGYB201704）；国家自然科学基金（31971577）；江苏高校优势学科建设项目（PAPD）。

详细信息

作者简介:
毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

MAO Lijun^1,2,
LI Mingshi^{1,3
, ,}

1 College of Forestry, Nanjing Forestry University, Nanjing 210037, China;
2 College of Criminal Science and Technology, Nanjing Forest Police College, Nanjing 210023, China;
3 Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

Funds:

The Fundamental Research Funds for the Central Universities (LGYB201704)

摘要: 国家公园的土地覆盖分类对于掌握自然资源现状、查明存在的生态安全威胁并快速应对具有基础性数据支撑作用。本文基于谷歌地球引擎（Google Earth Engine，GEE）平台，结合哨兵（Sentinel）主被动遥感数据及其导出的光谱指数、纹理特征和地形特征，分别采用基于像元的随机森林（random forest，RF）算法和面向对象的简单非迭代聚类（simple non-iterative clustering，SNIC）+RF算法实现了钱江源国家公园异质性景观的土地覆盖（耕地、森林、草地、水体、人造地表和裸地）分类。经地面验证表明，在多种输入数据组合中，基于像元和面向对象方法分类获得的最高总体精度分别为92.37%和93.98%。合成孔径雷达（synthetic aperture radar，SAR）数据的纳入能够提高基于像元方法的分类精度，但在面向对象方法中未能体现精度提升效果。SNIC+RF算法生成的土地覆盖分类图完整性更好，所需特征数量较少，并且算法能够在GEE环境下快速执行，适合推广应用于国家公园管理实践中。
- 国家公园 /
- 土地覆盖分类 /
- 哨兵 /
- 随机森林 /
- 简单非迭代聚类 /
- 谷歌地球引擎
Abstract: Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.
- national park /
- land cover classification /
- Sentinel /
- random forest /
- simple non-iterative clustering /
- Google Earth Engine

[1]	He S, Su Y, Wang L, et al. Taking an Ecosystem Services Approach for a New National Park System in China[J]. Resources, Conservation and Recycling, 2018, 137:136-144
[2]	Wang Y, Lu Z, Sheng Y, et al. Remote Sensing Applications in Monitoring of Protected Areas[J]. Remote Sensing, 2020, 12(9):1370
[3]	Chen J, Chen J, Liao A, et al. Global Land Cover Mapping at 30m Resolution:A Pok-Based Operational Approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103:7-27
[4]	Gong P, Liu H, Zhang M, et al. Stable Classification with Limited Sample:Transferring a 30-M Resolution Sample Set Collected in 2015 to Mapping 10-M Resolution Global Land Cover in 2017[J]. Science Bulletin, 2019, 64(6):370-373
[5]	Fawzi N I, Husna V N, Helms J A. Measuring Deforestation Using Remote Sensing and Its Implication for Conservation in Gunung Palung National Park, West Kalimantan, Indonesia[J]. IOP Conference Series:Earth and Environmental Science, 2018, 149:012038
[6]	Recanatesi F, Giuliani C, Ripa M N. Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the Ndvi and Sentinel-2 Images:The Case Study of Castelporziano State Natural Reserve[J]. Sustainability, 2018, 10(9):3308
[7]	Lucas R, De Kerchove R V, Otero V, et al. Structural Characterisation of Mangrove Forests Achieved through Combining Multiple Sources of Remote Sensing Data[J]. Remote Sensing of Environment, 2020, 237:111543
[8]	Clerici N, Valbuena Calderón C A, Posada J M. Fusion of Sentinel-1A and Sentinel-2A Data for Land Cover Mapping:A Case Study in the Lower Magdalena Region, Colombia[J]. Journal of Maps, 2017, 13(2):718-726
[9]	Tavares P A, Beltrão N E S, Guimarães U S, et al. Integration of Sentinel-1 and Sentinel-2 for Classification and Lulc Mapping in the Urban Area of Belém, Eastern Brazilian Amazon[J]. Sensors, 2019, 19(5):1140
[10]	Talukdar S, Singha P, Mahato S, et al. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[J]. Remote Sensing, 2020, 12(7):24
[11]	Li C, Gong P, Wang J, et al. The First All-Season Sample Set for Mapping Global Land Cover with Landsat-8 Data[J]. Science Bulletin, 2017, 62(7):508-515
[12]	Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32
[13]	Wendelberger K S, Gann D, Richards J H. Using Bi-Seasonal Worldview-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park[J]. Sensors, 2018, 18(3):829
[14]	Tsai Y H, Stow D, Chen H L, et al. Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine[J]. Remote Sensing, 2018, 10(6):927
[15]	Lv X, Ming D, Chen Y, et al. Very High Resolution Remote Sensing Image Classification with SEEDS-CNN and Scale Effect Analysis for Superpixel CNN Classification[J]. International Journal of Remote Sensing, 2019, 40(2):506-531
[16]	Achanta R, Süsstrunk S. Superpixels and Polygons Using Simple Non-Iterative Clustering[C].The 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017
[17]	Tu Y, Chen B, Zhang T, et al. Regional Mapping of Essential Urban Land Use Categories in China:A Segmentation-Based Approach[J]. Remote Sensing, 2020, 12(7):1058
[18]	Herrero H V, Southworth J, Bunting E. Utilizing Multiple Lines of Evidence to Determine Landscape Degradation within Protected Area Landscapes:A Case Study of Chobe National Park, Botswana from 1982 to 2011[J]. Remote Sensing, 2016, 8(8):623
[19]	Farr T G, Rosen P A, Caro E, et al. The Shuttle Radar Topography Mission[J]. Reviews of Geophysics, 2007, 45(2):RG2004
[20]	Haklay M, Weber P. Openstreetmap:User-Generated Street Maps[J]. IEEE Pervasive Computing, 2008, 7(4):12-18
[21]	Immitzer M, Vuolo F, Atzberger C. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe[J]. Remote Sensing, 2016, 8(3):166
[22]	Kaplan G. Sentinel-2 Pan Sharpening-Comparative Analysis[J]. Proceedings, 2018, 2(7):345
[23]	Rouse J H. Monitoring Vegetation Systems in the Great Plains with ERTS[J]. Proceedings, Third Earth Resources Technology Satellite-1 Symposium, Greenbelt,1974, 1974:3010-3017
[24]	Huete A, Didan K, Miura T, et al. Overview of the Radiometric and Biophysical Performance of the Modis Vegetation Indices[J]. Remote Sensing of Environment, 2002, 83(1):195-213
[25]	Gitelson A A, Merzlyak M N. Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves[J]. Advances in Space Research, 1998, 22(5):689-692
[26]	Louhaichi M, Borman M M, Johnson D E. Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat[J]. Geocarto International, 2001, 16(1):65-70
[27]	Gitelson A A, Stark R, Grits U, et al. Vegetation and Soil Lines in Visible Spectral Space:A Concept and Technique for Remote Estimation of Vegetation Fraction[J]. International Journal of Remote Sensing, 2002, 23(13):2537-2562
[28]	Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3):289-298
[29]	Merzlyak M N, Gitelson A A, Chivkunova O B, et al. Non-Destructive Optical Detection of Pigment Changes During Leaf Senescence and Fruit Ripening[J]. Physiologia Plantarum, 1999, 106(1):135-141
[30]	Qi J, Chehbouni A, Huete A R, et al. A Modified Soil Adjusted Vegetation Index[J]. Remote Sensing of Environment, 1994, 48(2):119-126
[31]	Díaz-Uriarte R, Alvarez de Andrés S. Gene Selection and Classification of Microarray Data Using Random Forest[J]. BMC Bioinformatics, 2006, 7(1):3
[32]	Nembrini S, König I R, Wright M N. The Revival of the Gini Importance?[J]. Bioinformatics, 2018, 34(21):3711-3718
[33]	Erinjery J J, Singh M, Kent R. Mapping and Assessment of Vegetation Types in the Tropical Rainforests of the Western Ghats Using Multispectral Sentinel-2 and Sar Sentinel-1 Satellite Imagery[J]. Remote Sensing of Environment, 2018, 216:345-354
[34]	Persson M, Lindberg E, Reese H. Tree Species Classification with Multi-Temporal Sentinel-2 Data[J]. Remote Sensing, 2018, 10(11):1794
[35]	Hamad R, Balzter H, Kolo K. Multi-Criteria Assessment of Land Cover Dynamic Changes in Halgurd Sakran National Park (Hsnp), Kurdistan Region of Iraq, Using Remote Sensing and GIS[J]. Land, 2017, 6(1):18
[36]	Wang M, He G, Ishwaran N, et al. Monitoring Vegetation Dynamics in East Rennell Island World Heritage Site Using Multi-Sensor and Multi-Temporal Remote Sensing Data[J]. International Journal of Digital Earth, 2018, 13(3):393-409

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GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

doi: 10.13203/j.whugis20200633

作者简介:
毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

计量

GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

doi: 10.13203/j.whugis20200633

1 南京林业大学林学院, 江苏南京, 210037;

2 南京森林警察学院刑事科学技术学院, 江苏南京, 210023;

3 南京林业大学南方林业协同创新中心, 江苏南京, 210037

作者简介:
毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

English Abstract

Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

1 College of Forestry, Nanjing Forestry University, Nanjing 210037, China;

2 College of Criminal Science and Technology, Nanjing Forest Police College, Nanjing 210023, China;

3 Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

全文HTML

目录

留言板

GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

doi: 10.13203/j.whugis20200633

作者简介: 毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

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出版历程

GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

doi: 10.13203/j.whugis20200633

1 南京林业大学林学院, 江苏南京, 210037; 2 南京森林警察学院刑事科学技术学院, 江苏南京, 210023; 3 南京林业大学南方林业协同创新中心, 江苏南京, 210037

作者简介: 毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

English Abstract

Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

1 College of Forestry, Nanjing Forestry University, Nanjing 210037, China; 2 College of Criminal Science and Technology, Nanjing Forest Police College, Nanjing 210023, China; 3 Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

全文HTML

目录

作者简介:
毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

1 南京林业大学林学院, 江苏南京, 210037;

2 南京森林警察学院刑事科学技术学院, 江苏南京, 210023;

3 南京林业大学南方林业协同创新中心, 江苏南京, 210037

作者简介:
毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

1 College of Forestry, Nanjing Forestry University, Nanjing 210037, China;

2 College of Criminal Science and Technology, Nanjing Forest Police College, Nanjing 210023, China;

3 Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China