Abstract
Unmanned Aerial Vehicles (UAV) are becoming an essential tool for evaluating the status and the changes in forest ecosystems. This is especially important in Japan due to the sheer magnitude and complexity of the forest area, made up mostly of natural mixed broadleaf deciduous forests. Additionally, Deep Learning (DL) is becoming more popular for forestry applications because it allows for the inclusion of expert human knowledge into the automatic image processing pipeline. In this paper we study and quantify issues related to the use of DL with our own UAV-acquired images in forestry applications such as: the effect of Transfer Learning (TL) and the Deep Learning architecture chosen or whether a simple patch-based framework may produce results in different practical problems. We use two different Deep Learning architectures (ResNet50 and UNet), two in-house datasets (winter and coastal forest) and focus on two separate problem formalizations (Multi-Label Patch or MLP classification and semantic segmentation). Our results show that Transfer Learning is necessary to obtain satisfactory outcome in the problem of MLP classification of deciduous vs evergreen trees in the winter orthomosaic dataset (with a 9.78% improvement from no transfer learning to transfer learning from a a general-purpose dataset). We also observe a further 2.7% improvement when Transfer Learning is performed from a dataset that is closer to our type of images. Finally, we demonstrate the applicability of the patch-based framework with the ResNet50 architecture in a different and complex example: Detection of the invasive broadleaf deciduous black locust (Robinia pseudoacacia) in an evergreen coniferous black pine (Pinus thunbergii) coastal forest typical of Japan. In this case we detect images containing the invasive species with a 75% of True Positives (TP) and 9% False Positives (FP) while the detection of native trees was 95% TP and 10% FP.
Highlights
Forest ecosystems play an important role in water, carbon and nutrient cycling within the soil-vegetation-atmosphere continuum
By dividing mosaics built using Unmanned Aerial Vehicles (UAV)-acquired images into regular patches and using two well-known Deep Learning (DL) architectures (ResNet and UNet), we propose the following objectives: 1. Develop an algorithm to classify patches corresponding to tree species (Multi-Label Patch (MLP) algorithm)
We present experiments using real data corresponding to seven winter mosaics and one summer mosaic from the coastal forest covering a total area of 38.5 ha
Summary
Forest ecosystems play an important role in water, carbon and nutrient cycling within the soil-vegetation-atmosphere continuum. Natural forests are affected by climate change while man-made forest ecosystems such as coastal forests are affected by invasive species that diminish their functions as windbreak [5,7]. Forest research has been carried out using labor and time-consuming land surveys [9]. They are costly and demand a high degree of organization training and expertise. New tools are needed in order to efficiently gain an overall understanding of species interaction and their response to climate change in order to design the proper response policy to ensure the sustainability of forests
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