谷歌浏览器插件
订阅小程序
在清言上使用

Burned Area in Land Use and Land Cover Classes in Sao Paulo State, Brazil.

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium(2022)

引用 1|浏览13
暂无评分
摘要
This article presents a land use and land cover (LULC) classification map using Random Forest algorithm in the São Paulo State (Brazil), and an assessment of burned areas using two products (MCD64A1 and MapBiomas Fire). The method uses Landsat Operational Land Imager (OLI) time series images from January to December of 2020. We performed the classification class by class considering: water, urban area, forest formation, sugarcane, agriculture, forest plantation and pasture. For each class, we used different spectral bands and image fraction according to the best response for the class. For 2020, the top three areas mapped in São Paulo State were pasture (40.49%), sugarcane (24.74%) and forest formation (20.60%). Comparing the two burned area products, MCD64A1 mapped more burned areas as it uses MODIS images combined with 1 km active fire observations with higher temporal resolution than MapBiomas Fire. About 60% of the burned areas mapped in 2020 occurred in the sugarcane class. The results show the importance of land use and land cover classification for better understanding fire-prone classes given the spatial distribution. It turns as an environmental tool for environmental strategies of planning and monitoring burned area assessment over regional scales.
更多
查看译文
关键词
LULC,Burned area,Image classification,Random Forest,Linear Spectral Mixing Model
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要