System for remote deforestation detection in the State of Espírito Santo
Name: IGOR VIEIRA LEITE
Publication date: 28/03/2024
Examining board:
Name |
Role |
|---|---|
| ADRIANO RIBEIRO DE MENDONCA | Presidente |
| JEFERSON PEREIRA MARTINS SILVA | Examinador Externo |
| TAPIS RIZZO MOREIRA | Examinador Externo |
Summary: Deforestation and forest degradation pose a significant threat to biodiversity and environmental balance. The state of Espírito Santo, although known for its rich biodiversity, faces challenges in monitoring these environmental impacts.
Orbital remote sensing and the development of robust platforms for geospatial data processing, such as Google Earth Engine (GEE), emerge as alternatives to overcome these monitoring challenges. In this context, this study aimed to evaluate a system with high spatial and temporal resolution to monitor forest degradation and deforestation in the Atlantic Forest of Espírito Santo, using Sentinel-2 images. The native forest base map was derived from the annual cover map data of MapBiomas, combined with Sentinel-2 images in the Scene Classification Layer (SCL) band for soil, vegetation, and cloud classes. To validate the data obtained by FlorESat, a confusion matrix was calculated. A pixellevel concordance analysis between classes was performed using the alert database compiled by RAD MapBiomas, which contains 403 polygons from 2019 to 2022. FlorESat's deforestation mapping indicated that the total deforested area for the same years was 1,780.14 ha. In the validation, the proposed system
showed mapping accuracy, precision, and specificity of 93.3%, 94.7%, and 93.8% respectively, for 52 randomly delineated polygons in forest areas in ES when compared to photointerpretation. Additionally, it was found that 59.85% of the
pixels identified by the FlorESat tool matched directly with the alert database issued and validated in the field. Of the non-coincident pixels, 65.11% were covered by clouds and 34.89% were mapped as forests, highlighting a limitation of orbital data. However, when considering the scenario where cloud pixels are considered as deforested areas, the percentage of concordance was 85.99% between the two datasets.
Keywords: Sentinel-2; Atlantic Forest; Degradation; Deforestation.
