Abstract
Abstract. To address climate change, accurate and automated forest cover monitoring is crucial. In this study, we propose a Convolutional Neural Network (CNN) which mimics professional interpreters’ manual techniques. Using simultaneously acquired airborne images and LiDAR data, we attempt to reproduce the 3D knowledge of tree shape, which interpreters potentially make use of. Geospatial features which support interpretation are also used as inputs to the CNN. Inspired by the interpreters’ techniques, we propose a unified approach that integrates these datasets in a shallow layer in the CNN network. With the proposed CNN, we show that the multi-modal CNN works robustly, which gets more than 80 % user’s accuracy. We also show that the 3D multi-modal approach is especially suited for deciduous trees thanks to the ability of capturing 3D shapes.
Highlights
The Paris Agreement, adopted at the COP21 in 2015, set out a global action plan to reduce greenhouse-gas emissions, which puts the world on track to avoid dangerous climate change and accelerates the Carbon Disclosure Project (CDP)
To tackle the accurate forest cover classification, we propose a Convolutional Neural Network (CNN) approach which is inspired by professional interpreters
To combine the geospatial input data, we propose a multi-modal CNN, where the input data is integrated in a shallow layer, which is a closer layer to the input than output in the CNN
Summary
The Paris Agreement, adopted at the COP21 in 2015, set out a global action plan to reduce greenhouse-gas emissions, which puts the world on track to avoid dangerous climate change and accelerates the Carbon Disclosure Project (CDP). CDP requests companies and cities to disclose the status of environmental actions against climate change. Under these circumstances, remote sensing, which enables us to observe the planetary surface, is expected to monitor the forest owners’ effort such as sustainable forest management (e.g. organized logging, planting and thinning). To meet the purpose of carbon disclosure, monitoring and frequent and low-cost monitoring is required. Since both these features would be difficult to achieve through manual work, it is urgent to establish an automated forest monitoring method.p. Currently, there already exists automated forest monitoring systems. Aiming for specific targets, a number of different methods have been developed for different types of forests using various remote sensing data, whereas forest cover classification using high-resolution data remains challenging
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