Abstract
Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km2) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90%. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development.
Highlights
Amid radical global environmental degradation, forests with worldwide coverage play a vital role in promoting human sustainable development
To solve the above problems, we propose a multitemporal convolutional network framework (MT-CNN) to recognize different types of forest disturbances
The prediction accuracy of forest disturbances is above 90% on the USA west coast region with the past 35 years of Landsat time-series data
Summary
Amid radical global environmental degradation, forests with worldwide coverage play a vital role in promoting human sustainable development. In terms of balancing the regional ecology system, forests are able to regulate regional climate as well as carbon and water cycles [1,2,3]. Most existing forest ecosystems are continuously disturbed by natural and human-made events, such as fires, pests, and deforestation, Remote Sens. It is urgently needed to develop appropriate land management strategies using accurate forest disturbance maps through long time-series archived remote sensing data. Conventional forest ecology methods often require intensive field investigation and consume abundant resources, but it is difficult to achieve satisfactory forest disturbance detection results on large-scales [6]. Developing a timely and accurate forest disturbance detection strategy is urgently needed
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