Forest survey and spatio-temporal analysis in West Tianshan mountains supported by Google Earth Engine

doi:10.13474/j.cnki.11-2246.2020.0240

Abstract

Abstract: Aiming at the problems of difficult data acquisition, low calculation efficiency, large amount of data and low accuracy in large-scale spatial distribution information extraction and long-time series change monitoring, supported by the Google Earth Engine platform, based on multi-source remote sensing data, a multi-dimensional classification feature set is constructed, and the forest extraction and classification of West Tianshan mountains in 1995, 2000, 2005, 2010, 2015 and 2018 are studied by random forest classification algorithm. Six periods of forest high-precision extraction and mapping analysis are completed by cloud-local combination. The results show that the process constructed in this paper has high accuracy. During the past 23 years, the forest area in West Tianshan mountains has been expanding dynamically, increasing from 3 953.57 km² in 1995 to 4 243.41 km² in 2018, with an increasing rate of 12.60 km²/a. In terms of time, the growth rate of forest has been rising steadily in the past 23 years. The growth rate in 2005-2018 is higher than that in 1995-2015. In terms of spatial distribution, there are few conversions between different forest types. The newly added forest land mainly comes from non-forest land. The area of non-forest land converted to forest land has been about 520 km² in the past 23 years. The Tex county branch, Nileke country branch, Zhaosu county branch, Xinyuan forest farm, Gongliu branch, and Yining branch have transferred areas of 111.14, 102.19, 67.16, 56.45, 42.76, and 40.71 km².

Key words: forest remote sensing, GEE cloud caculation, spatio-temporal change, random forest, West Tianshan mountains

CLC Number:

P237

SHAO Yakui, WANG Lei, ZHU Changming, FANG Hui, ZHANG Xin, HUANG Duan, TAO Li. Forest survey and spatio-temporal analysis in West Tianshan mountains supported by Google Earth Engine[J]. Bulletin of Surveying and Mapping, 2020, 0(8): 13-17.

References

[1] 唐守正.中国森林资源及其对环境的影响[J].生物学通报,1998,33(11):2-6.
[2] 李虎,吕巡贤,陈蜀疆,等.新疆森林资源动态分析——基于RS与GIS的森林资源动态研究[J].地理学报,2003,58(1):133-138.
[3] 黄龙生,王兵,牛香,等.东北和内蒙古重点国有林区天然林保护工程生态效益分析[J].中国水土保持科学, 2017,15(1):89-96.
[4] OUYANG Z, ZHENG H, XIAO Y, et al. Improvements in ecosystem services from investments in natural capital[J]. Science, 2016, 352(6292):1455-1459.
[5] 加尔肯居马肯·爱特, 朱海涌. 伊犁河谷生态功能区划探讨[J]. 环境科学与管理, 2009, 34(4):140-142.
[6] LIU X, HU G, CHEN Y, et al. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine platform[J]. Remote Sensing of Environment, 2018, 209:227-239.
[7] 郝斌飞, 韩旭军, 马明国, 等. Google Earth Engine在地球科学与环境科学中的应用研究进展[J]. 遥感技术与应用, 2018(4):600-611.
[8] HANSEN M C, POTAPOV P V, MOORE R, et al. High-resolution global maps of 21st-century forest cover change[J]. Ecological Science, 2013,342(6160):850-853.
[9] GONG P, LIU H, ZHANG M N, 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.
[10] WANG J, XIAO X, QIN Y, et al. Mapping the dynamics of eastern redcedar encroachment into grasslands during 1984-2010 through PALSAR and time series Landsat images[J]. Remote Sensing of Environment, 2017, 190:233-246.
[11] 闫俊杰, 张静, 雷雨, 等. 2000-2016年新疆伊犁河谷草地NDVI变化趋势分析[J]. 草地学报, 2018, 26(4):72-81.
[12] FARR T G, ROSEN P A, CARO E, et al. The shuttle radar topography mission[J]. Reviews of Geophysics, 2007, 45(2):1-33.
[13] 段均泽, 毛炜峄, 黄逸静, 等. 近56 a伊犁河谷冷空气过程气候变化特征[J]. 干旱气象, 2018, 36(5):46-54.
[14] 肖德顺, 赵义峰, 安志清. 伊犁地区降水特征及变化趋势分析[J]. 干旱区资源与环境, 2006,20(4):49-52.
[15] BREIMAN L, CUTLER R A. Random forests machine learning[J]. Journal of Clinical Microbiology, 2001(2):199-228.
[16] 冯文卿,眭海刚,涂继辉,等. 高分辨率遥感影像的随机森林变化检测方法[J]. 测绘学报,2017,46(11):90-100.