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
ZHANG Hao1,2,3(), GAO Xiaohong1,2,3,4(), SHI Feifei1,2,3, LI Runxiang1,2,3
1. School of Geographical Sciences, Qinghai Normal University, Xining 810008, China 2. MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological, Xining 810008, China 3. Qinghai Province Key Laboratory of Physical Geography and Environmental Process, Xining 810008, China 4. Academy of Plateau Science and Sustainability, Xining 810008, China
The eastern agricultural areas of Qinghai are located in the transitional zone from the Loess Plateau to the Qinghai-Tibet Plateau. In this transitional zone, the loess hills feature various landforms, large fluctuations, and fragmentation. With the acceleration of urbanization in recent decades, the shortage of available rural labor force has aggravated land abandonment. Therefore, ascertaining the distribution of abandoned land in the eastern agricultural areas of Qinghai is very crucial to protecting cultivated and ecological land. This study investigated Minhe County in Qinghai Province based on the GEE cloud platform. According to the phenological characteristics of crops, both Sentinel-2 MSI and Sentinel-1 SAR satellite images covering the growth and planting periods of crops were selected as the main data source. With the aid of the DEM and by combining the characteristics of spectra, terrain, polarization, and tasseled cap, this study automatically classified the land cover from 2018 to 2020 in the study area using the random forest method, obtaining the three-year land cover data of the study area. Then, this study built a decision tree based on the determination rules for abandoned land and extracted and verified the abandoned land information using the decision tree. The study results are as follows. The overall classification precision of land cover in 2018, 2019, and 2020 were 86.93%, 87.36%, and 88.54%, respectively. The area of abandoned land in Minhe County in 2020 was 43.17 km2, accounting for 2.28% of the total study area. The abandoned land was mainly distributed in areas with an altitude of 2 200~2 600 m, a slope of 6°~25°, and a shady slope direction. The integration of the polarization characteristics of Sentinel-1 SAR images into Sentinel-2 MSI multi-season images can effectively improve the land cover classification precision and yield accurate information on the abandoned land. This study will provide a reference method and basis for the information extraction of abandoned land in areas with similar terrain.
张昊, 高小红, 史飞飞, 李润祥. 基于Sentinel-2 MSI与Sentinel-1 SAR相结合的黄土高原西部撂荒地提取——以青海民和县为例[J]. 自然资源遥感, 2022, 34(4): 144-154.
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. Remote Sensing for Natural Resources, 2022, 34(4): 144-154.
Chen X Y, Zheng G Q. Research progress on arable land abandonment in China and abroad[J]. China Population,Resources and Environment, 2018, 28(s2):37-41.
Duan Y M, Zhou H, Liu X H, et al. Research progress and prospect of abandonment of China’s cultivated land[J]. Jiangsu Agricultural Sciences, 2018, 46(13):13-17.
Zhu C M, Luo J C, Shen Z F, et al. Winter wheat planting area extraction using multi-temporal remote sensing data based on filed parcel characteristic[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(9):94-99.
Chen Y S, Huang C L, Hou J L, et al. Extraction of maize planting area based on multi-temporal Sentinel-2 imagery in the middle reaches of Heihe River[J]. Remote Sensing Technology and Application, 2021, 36(2):324-331.
Song H L, Lei H M, Shang M. Crop classification based on Sentinel 2A/B time series data in Heilonggang River basin[J]. Jiangsu Journal of Agricultural Sciences, 2021, 37(1):83-92.
Xue J, Yu L F, Lin Q N, et al. Using Sentinel-1 multi-temporal InSAR data to monitor the damage degree of shoot beetle in Yunnan pine forests[J]. Remote Sensing for Land and Resources, 2018, 30(4):108-114.doi:10.6046/gtzyyg.2018.04.17.
doi: 10.6046/gtzyyg.2018.04.17
Xiao G F, Zhu X F, Hou C Y, et al. Extraction and analysis of abandoned farmland:A case study of Qingyun and Wudi counties in Shandong Province[J]. Acta Geographica Sinica, 2018, 73(9):1658-1673.
[8]
Wu M H, Hu Y M, Wang H M, et al. Remote sensing extraction and feature analysis of abandoned farmland in hilly and mountainous areas:A case study of Xingning,Guangdong[J]. Remote Sensing Applications:Society and Environment, 2020, 20:100403.
doi: 10.1016/j.rsase.2020.100403
Tian Y, Chen Z Q, Hui F M, et al. ESAS Sentinel-2A/B satellite:Characteristics and applications[J]. Journal of Beijing Normal University(Natural Science), 2019, 55(1):57-65.
Yang T, Guo X D, Qiu D P, et al. Information extraction and driving factor assessment of farmland abandonment based on joint change detection[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(6):201-208.
[11]
He S, Shao H Y, Xian W, et al. Extraction of abandoned land in hilly areas based on the spatio-temporal fusion of multi-source remote sensing images[J]. Remote Sensing, 2021, 13(19):3956.
doi: 10.3390/rs13193956
Wang H Y, Wang X F, Gao L, et al. Study on extraction method of abandoned farmland based on the seasonal variation characteristics of remotely sensed images[J]. Remote Sensing Technology and Application, 2020, 35(3):596-605.
Ma Y D, Wu J, Li C B, et al. Cultivated land abandonment information extraction based on Sentinel-2 image data:Taking Baliwan Town,Tianshui City,Gansu Province as an example[J]. Productivity Research, 2020(5):61-64.
[14]
Zeng H W, Wu B F, Wang S, et al. A synthesizing land-cover classification method based on Google Earth Engine:A case study in Nzhelele and Levhuvu Catchments,South Africa[J]. Chinese Geographical Science, 2020, 30(3):13.
[15]
Gomez C, White J C, Wulder M A. Optical remotely sensed time series data for land cover classification:A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116:55-72.
doi: 10.1016/j.isprsjprs.2016.03.008
Ma H J, Gao X H, Gu X T. Random forest classification of Landsat8 imagery for the complex terrain area based on the combination of spectral,topographic and texture information[J]. Journal of Geo-Information Science, 2019, 21(3):359-371.
Zhang X Y, Li F R, Zhen Z, et al. Forest vegetation classification of Landsat-8 remote sensing image based on random forests model[J]. Journal of Northeast Forestry University, 2016, 44(6):53-57,74.
Hu Y F, Shang L J, Zhang Q L, et al. Land change patterns and driving mechanism Beijing since 1990 based on GEE platform[J]. Remote Sensing Technology and Application, 2018, 33(4):573-583.
[19]
Souverijns N, Buchhorn M, Horion S, et al. Thirty years of land cover and fraction cover changes over the Sudano-Sahel using Landsat time series[J]. Remote Sensing, 2020, 12(22):3817.
doi: 10.3390/rs12223817
Zhou D W, Li X L, Kang R C, et al. Research on land cover classification method based on Google Earth Engine[J]. Geomatics and Spatial Information Technology, 2021, 44(s1):100-102,105,109.
Li W J, Jiao S J, Li R. Effects of climate change on winter wheat in Minhe Count[J]. Science and Technology of Qinghai Agriculture and Forestry, 2018(4):6-11.
Mou X L, Li H, Huang C, et al. Application progress of Google Earth Engine in land use and land cover remote sensing information extraction[J]. Remote Sensing for Land and Resources, 2021, 33(2):1-10.doi:10.6046/gtzyyg.2020189.
doi: 10.6046/gtzyyg.2020189
[23]
Hu Y, Xu X L, Wu F Y, et al. Estimating forest stock volume in Hunan Province,China,by integrating in situ plot data,Sentinel-2 images,and linear and machine learning regression models[J]. Remote Sensing, 2020, 12(1):186.
doi: 10.3390/rs12010186
[24]
Ion S, Alberto G M, Leire S P, et al. Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 73:63-76.
doi: 10.1016/j.jag.2018.05.020
Wu H A, Jiang J J, Zhang H L, et al. Application of ratio resident-area index to retrieve urban residential areas based on Landsat TM data[J]. Journal of Nanjing Normal University(Natural Science Edition), 2006, 29(3):118-121.
[27]
王正兴, 刘闯, Alfredo H. 植被指数研究进展:从AVHRR-NDVI到MODIS-EVI[J]. 生态学报, 2003(5):979-987.
Wang Z X, Liu C, Alfredo H. From AVHRR-NDVI to MODIS-EVI:Advances in vegetation index research[J]. Acta Ecologica Sinica, 2003(5):979-987.
Xu H Q. A study on information extraction of water body with the modified normalized difference water index(MNDWI)[J]. National Remote Sensing Bulletin, 2005(5):589-595.
[29]
Belcore E, Piras M, Wozniak E. Specific alpine environment land cover classification methodology:Google Earth Engine processing for Sentinel-2 data[J]. The International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences, 2020, 43:663-670.
Gao C C, Hui X W. GLCM-based texture feature extraction[J]. Computer Systems Applications, 2010, 19(6):195-198.
[31]
Nedkov R. Orthogonal transformation of segmented images from the satellite Sentinel-2[J]. Comptes Rendus de l’Academie Bulgare des Sciences, 2017, 70(5):687-692.
Shi T C, Xu X H. Extraction and validation of abandoned farmland parcel in typical counties of Chongqing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(24):261-267.
Wang D J, Jiang Q G, Li Y H, et al. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land and Resources, 2020, 32(4):236-243.doi:10.6046/gtzyyg.2020.04.29.
doi: 10.6046/gtzyyg.2020.04.29
Liu Z L, Zhang Q B, Yue D P, et al. Extraction of urban built-up areas based on Sentinel-2A and NPP-VIIRS nighttime light data[J]. Remote Sensing for Land and Resources, 2019, 31(4):227-234.doi:10.6046/gtzyyg.2019.04.29.
doi: 10.6046/gtzyyg.2019.04.29
Wang H, Yang Q P, Tian Y J, et al. Vegetation coverage monitoring in the Central Asian countries using multi-temporal Landsat images[J]. Arid Land Geography, 2020, 43(4):1023-1032.
[36]
Ghorbanian A, Zaghian S, Asiyabi R M, et al. Mangrove ecosystem mapping using Sentinel-1 and Sentinel-2 satellite images and random forest algorithm in Google Earth Engine[J]. Remote Sensing, 2021, 13(13):2565.
doi: 10.3390/rs13132565