AI-ForestWatch: semantic segmentation based end-to-end framework for forest estimation and change detection using multi-spectral remote sensing imagery

Abstract

Forest change detection is crucial for sustainable forest management. The changes in the forest area due to deforestation (such as wild fires or logging due to development activities) or afforestation alter the total forest area. Additionally, it impacts the available stock for commercial purposes, climate change due to carbon emissions, and biodiversity of the forest habitat estimations, which are essential for disaster management and policy making. In recent years, foresters have relied on hand-crafted features or bi-temporal change detection methods to detect change in the remote sensing imagery to estimate the forest area. Due to manual processing steps, these methods are fragile and prone to errors and can generate inaccurate (i.e., under or over) segmentation results. In contrast to traditional methods, we present AI-ForestWatch, an end to end framework for forest estimation and change analysis. The proposed approach uses deep convolution neural network-based semantic segmentation to process multi-spectral spaceborne images to quantitatively monitor the forest cover change patterns by automatically extracting features from the dataset. Our analysis is completely data driven and has been performed using extended (with vegetation indices) Landsat-8 multi-spectral imagery from 2014 to 2020. As a case study, we estimated the forest area in 15 districts of Pakistan and generated forest change maps from 2014 to 2020, where major afforestation activity is carried out during this period. Our critical analysis shows an improvement of forest cover in 14 out of 15 districts. The AI-ForestWatch framework along with the associated dataset will be made public upon publication so that it can be adapted by other countries or regions. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop

“…The dataset has arbitrarily shaped disease regions, is densely annotated, and includes complex foregrounds and backgrounds. Semantic segmentation helps in fine-grained understanding of the structure and layout of images and finds its applications in various computer vision tasks [ 37 ]. Semantic segmentation is also utilized for disease identification and detection in plants and fruits, which is a tedious and complex task [ 38 ].…”

Forest Fragmentation and Landscape Connectivity Changes in Ecuadorian Mangroves: Some Hope for the Future?

“…New remote-sensing products such as the Global Mangrove Watch (GMW) dataset, which measures both forest loss and recovery globally, have shown to be very useful in studies designed to better comprehend how mangrove expansion may reconnect fragmented regions over the long term [47]. Fragmentation is a process unrelated to habitat loss and therefore unrelated to habitat gain; it is what is known as "fragmentation per se" [48].…”

Impact of Tree Cover Loss on Carbon Emission: A Learning-Based Analysis

“…Learning-based techniques have provided new approaches to prediction problems that represent interactions between variables in a deep and layered hierarchy. ML-based techniques like support vector machines (SVMs) and random forest (RF), as well as DLbased algorithms like recurrent neural network (RNN) and LSTM, have attracted lots of attention in recent years because of their applications in a variety of felds [11][12][13][14]. In time series forecasting, DL approaches are capable of identifying data structure and pattern, such as non-linearity and complexity [15,16].…”