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A Remote Sensing Based Method to Detect Soil Erosion in Forests

Xu, Hanqiu ; Hu, Xiujuan ; Guan, Huade ; Zhang, Bobo ; Wang, Meiya ; Chen, Shanmu ; Chen, Minghua

Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.513 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
A Remote Sensing Based Method to Detect Soil Erosion in Forests
Autor: Xu, Hanqiu ; Hu, Xiujuan ; Guan, Huade ; Zhang, Bobo ; Wang, Meiya ; Chen, Shanmu ; Chen, Minghua
Assuntos: Algorithms ; Canopies ; Case studies ; Detection ; detection model ; Discriminators ; Ecosystems ; Forest conservation ; Forest management ; Forest soils ; Forests ; fractional vegetation coverage ; Laboratories ; Land management ; Landsat ; Landsat satellites ; Multiplication ; Principal components analysis ; Rain water ; red-soil erosion ; Reflectance ; Remote sensing ; Satellite imagery ; SEUFM ; Slopes ; Soil conservation ; Soil density ; Soil erosion ; Soil fertility ; Soil management ; Soil sciences ; Unmanned aerial vehicles ; Vegetation ; vegetation health ; Yellow leaf ; yellow leaf index
É parte de: Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.513
Descrição: Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.
Editor: Basel: MDPI AG
Idioma: Inglês

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A Remote Sensing Based Method to Detect Soil Erosion in Forests

Xu, Hanqiu ; Hu, Xiujuan ; Guan, Huade ; Zhang, Bobo ; Wang, Meiya ; Chen, Shanmu ; Chen, Minghua

Basel: MDPI AG

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