A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

Abstract

The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

“…In general, all models applied to covmingrad and cov filtered and unfiltered images presented good performance, with accuracy above 90%, which proves the ability of these models to classify selective logging in SAR images. These results are similar, in terms of accuracy, to those obtained by [57] with the application of the Artificial Neural Network Multi Layer Perceptron (ANN-MLP) on attributes extracted by the authors of the products of the same bitemporal COSMO-SkyMed images used in this study. The CNNs approach eliminates the attribute generation step, reducing processing time, and its main advantage compared to its predecessors is that it automatically detects and learns texture and context features that describe the target without any human supervision.…”

“…Thus, neighborhood relationships between targets (context) are not taken into account, which is an important parameter in the detection of activities such as logging. Kuck et al [57] explored, in addition to textural parameters, spectral, spatial, and context parameters, and obtained results similar to those of CNNs. A future approach could combine the attributes of the two ML techniques.…”

“…These differences affect backscatter and can produce false detections [5]. More studies should be carried out to quantify the effect of these artifacts on detections through CNNs, although [57] have shown that ANN-MLPs are capable of separating these artifacts from selective logging.…”

“…For this, the coefficient of variation (CV) between the two images was generated, since the CV has been pointed out as an advantageous alternative for detecting changes in SAR images [31]. As obtained by [57], the value of 0.4 was defined as the boundary between non-logging and selective logging, thus values greater than 0.4 were included as candidates for the selective logging class. Later, such sets of pixels were vectorized, forming candidate polygons for the selective logging class.…”

“…The tested models were InceptionV3, VGG16, VGG19, SqueezeNet, Painters, and DeepLoc (for details, see Section 2.2). The last fully connected layer was defined according to the tests performed by [57], who presented, as the best result for classification of selective logging, that obtained by an Artificial Neural Network of the Multi Layer Perceptron (ANN-MLP) type, with 1 hidden layer of 50 neurons, with ReLu activation function and SGD weight activator, α = 0.00002 and 1000 iterations (Figure 4, step 5).…”