Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest

Abstract

Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of burn scars. Recent advances in remote-sensing and availability of multimodal data offer a viable solution to this mapping problem. However, the task to segment burn marks is difficult because of its indistinguishably with similar looking land patterns, severe fragmented nature of burn marks and partially labelled noisy datasets.In this work we present AmazonNET -a convolutional based network that allows extracting of burn patters from multimodal remote sensing images. The network consists of UNet-a wellknown encoder decoder type of architecture with skip connections commonly used in biomedical segmentation. The proposed framework utilises stacked RGB-NIR channels to segment burn scars from the pastures by training on a new weakly labelled noisy dataset from Amazonia.Our model illustrates superior performance by correctly identifying partially labelled burn scars and rejecting incorrectly labelled samples, demonstrating our approach as one of the first to effectively utilise deep learning based segmentation models in multimodal burn scar identification.

Amazon Rainforest Wildfires: Causes and Impact on Mammalian and Avian Diversity

“…It is affecting the carbon cycle. All these are affecting agricultural production (Mohla et al, 2020). The destruction of these forests will cause world climate change.…”

“…These fires cause increase in carbon dioxide which will cause increase in global temperature. So, this is one of the most important issues in the world (Mohla et al, 2020). These forests are considered one of the richest regions on the Earth.…”