Emissivity-based vegetation indices to monitor deforestation and forest degradation in the Congo basin rainforest

Vegetation stress is a major widespread problem in many countries because of climate change and anthropogenic activities. Deforestation and forest degradation phenomena may be caused for several reasons such as infrastructure development, agriculture, collection of wood energy, forest exploitation. Over the last decade, a severe decline in vegetation was observed in the Congo Basin rainforest, the second-largest tropical forest in the world, behind the Amazon. Therefore, actions are required to monitor and detect vegetation stresses to mitigate their negative impacts on human life, wildlife, and plant communities. Vegetation stress can be estimated using three different methods: field measurements, meteorological data, and remote sensing. The present study is mainly focused on satellite remote sensing. The main objective is to develop and test new indices of vegetation-soil dryness based on the surface emissivity. Until now, the problem has been attacked through indices such as the normalized differential vegetation index (or NDVI). The problem of NDVI is that it is a greenness index and is not capable to distinguish bare soil from senescent vegetation, whereas this distinction is important especially when forest degradation followed by eventual regeneration occurs and when dealing with semi-arid regions, where we could have desert sand. We propose to follow the strategy of using surface emissivity (ε), which is more closely related to surface type and coverage. By properly using surface emissivity in the infrared we can define a set of channels that are particularly sensitive to bare soil, green, and senescent vegetation. From these emissivity channels, we can derive a suitable emissivity contrast index or ECI, which is sensitive to green vegetation, senescent vegetation, and bare soil, therefore overcoming the NDVI limitation concerning its capability to distinguish bares soil from senescent vegetation. The analysis is performed with CAMEL (Combined ASTER and MODIS Emissivity for Land) database from 2000 to 2016.

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