Monitoring Vegetation Change in the Presence of High Cloud Cover with Sentinel-2 in a Lowland Tropical Forest Region in Brazil

Tatiana Nazarova,Gregory Giuliani,Pascal Martin

doi:10.3390/rs12111829

Abstract

Forests play major roles in climate regulation, ecosystem services, carbon storage, biodiversity, terrain stabilization, and water retention, as well as in the economy of numerous countries. Nevertheless, deforestation and forest degradation are rampant in many parts of the world. In particular, the Amazonian rainforest faces the constant threats posed by logging, mining, and burning for agricultural expansion. In Brazil, the “Sete de Setembro Indigenous Land”, a protected area located in a lowland tropical forest region at the border between the Mato Grosso and Rondônia states, is subject to illegal deforestation and therefore necessitates effective vegetation monitoring tools. Optical satellite imagery, while extensively used for landcover assessment and monitoring, is vulnerable to high cloud cover percentages, as these can preclude analysis and strongly limit the temporal resolution. We propose a cloud computing-based coupled detection strategy using (i) cloud and cloud shadow/vegetation detection systems with Sentinel-2 data analyzed on the Google Earth Engine with deep neural network classification models, with (ii) a classification error correction and vegetation loss and gain analysis tool that dynamically compares and updates the classification in a time series. The initial results demonstrate that such a detection system can constitute a powerful monitoring tool to assist in the prevention, early warning, and assessment of deforestation and forest degradation in cloudy tropical regions. Owing to the integrated cloud detection system, the temporal resolution is significantly improved. The limitations of the model in its present state include classification issues during the forest fire period, and a lack of distinction between natural vegetation loss and anthropogenic deforestation. Two possible solutions to the latter problem are proposed, namely, the mapping of known agricultural and bare areas and its subsequent removal from the analyzed data, or the inclusion of radar data, which would allow a large amount of finetuning of the detection processes.

Highlights

Forests are an essential element of Earth’s dynamic equilibrium
The states of Rondônia and Mato Grosso are especially sensitive areas, as they are subjected to high levels of deforestation pressure [8] while encompassing some of the last large intact forest areas and indigenous lands, which play a major role in deterring deforestation and forest degradation [9]
As the scarcely populated Sete de Setembro Indigenous Land (SSIL) covers a vast territory of 250 km2, the timely detection of vegetation loss represents an important challenge that could be overcome by taking advantage of the development and accessibility of cloud-computing technologies to implement automated classification of optical remote sensing data

Summary

Introduction

Forests are an essential element of Earth’s dynamic equilibrium They store an estimated 60% of all the terrestrial carbon [1] and contribute up to 70% of the global evapotranspiration [2], which is a major element of rain cycles on which a large part of human activity is dependent. Forest loss represents a decrease of the carbon sink and a net carbon emission source through the burning and decomposing of plant and soil material; its relative contribution to the global anthropogenic. The Sete de Setembro Indigenous Land (SSIL), Terra Indígena Sete de Setembro, is located on the border between Rondônia and Mato Grosso, in the so-called “arc of deforestation” of the Amazon, and is inhabited by the Suruí, one of the 300 indigenous people of Brazil. As the scarcely populated SSIL covers a vast territory of 250 km, the timely detection of vegetation loss represents an important challenge that could be overcome by taking advantage of the development and accessibility of cloud-computing technologies to implement automated classification of optical remote sensing data

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Conclusion