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

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.


Introduction
Anthropogenic activities are responsible for the current global temperature increase of about 1.0 • C and for an expected increase of 1.5 • C sometime between 2030 and 2052. Potential impacts and risks associated with increasing temperature include elevation of the sea level, higher frequency and intensity of extreme temperatures, storms, and droughts, loss of biodiversity, reduction in oxygen concentration in the oceans, and shortage of food production [1]. A reduction in anthropogenic CO 2 emissions is mandatory to control the global rise in temperature. Deforestation and forest degradation are the second largest anthropogenic sources of CO 2 emissions into the atmosphere, since they are related to the combustion of forest biomass, as well as to the decomposition of remaining plant materials. Approximately 65% of Brazilian CO 2 emissions in 2019 came from deforestation and forest degradation [2]. According to Qin et al. [3], forest degradation contributes three times more to the aboveground gross biomass loss than deforestation in the Brazilian Amazon. This is observed in the study site is dense rainforest with patches of open rainforest and dominant palm trees and lianas [49]. The Jamari National Forest has an area of approximately 220,000 hectares, of which an area of 96,000 hectares has been managed and used as forest concessions since 2008 [50]. Three companies were selected by the Brazilian Forest Service (SFB) under the Ministry of Agriculture, Livestock, and Food Supply to implement forest management plans of timber and no-timber forest products (latex, fruits, and leaves) within previously established forest management units (UMF I, II, and III) in that National Forest. UMF III was divided into different annual production units (UPAs). UPA 11 was explored in 2018 and, therefore, was selected as the test site in this analysis. The timber companies need to present forest inventory data to obtain authorization for selective logging in those UMFs, which include the number and the list of tree species that occur at that site. They also need to provide a tree species census of the concession area, including the species identification, diameter at breast height (DBH), circumference at breast height (CBH), estimated volume, and LiDAR point clouds from aerial surveys (before and after exploitation).

SAR Data
This study was based on SAR images acquired by the COSMO-SkyMed constellation of four satellites, allowing a few-hourly revisiting time with varying incidence angles. The main payload of the COSMO-SkyMed satellite is a multi-resolution and multi-polarized X-band imaging radar with spatial resolution ranging from 1 m to 100 m and nominal incidence angles between 20° and 59°. Table 1 shows the details of the images selected by this study: three overpasses from 8 January 2018, 5 June 2018, and 8 October 2018, comprising the logging period in the study area, which took place from April to September of the same year.  The timber companies need to present forest inventory data to obtain authorization for selective logging in those UMFs, which include the number and the list of tree species that occur at that site. They also need to provide a tree species census of the concession area, including the species identification, diameter at breast height (DBH), circumference at breast height (CBH), estimated volume, and LiDAR point clouds from aerial surveys (before and after exploitation).

SAR Data
This study was based on SAR images acquired by the COSMO-SkyMed constellation of four satellites, allowing a few-hourly revisiting time with varying incidence angles. The main payload of the COSMO-SkyMed satellite is a multi-resolution and multi-polarized X-band imaging radar with spatial resolution ranging from 1 m to 100 m and nominal incidence angles between 20 • and 59 • . Table 1 shows the details of the images selected by this study: three overpasses from 8 January 2018, 5 June 2018, and 8 October 2018, comprising the logging period in the study area, which took place from April to September of the same year.
The image preprocessing included the following steps: 1.
Download of the complex images in H5 format, which stores the data in hierarchical data format (HDF), containing the sensor's scan metadata; 2.
Multi-look filtering, defined as one look in range and azimuth, which resulted in a grid cell of 3 m, representing the best spatial resolution of the StripMap image acquisition mode, and conversion from slant range to ground range; 3.
Co-registration for correction of relative translational and rotational deviations and scale difference between images; 4.
Application of the GammaMAP filter [51], with a 3 × 3 window and ENL 1.1, which was pointed out by [33] as presenting the best results for multitemporal analyzes in tropical forests;

Cloud Computing of LiDAR Points
The LiDAR point clouds acquired in 2018 and 2019 over a portion of UPA 11 were processed to generate a digital surface model (DSM) from the first return points, which correspond to the pulses with the shortest time between emission and return, which are those that correspond to the outermost surface of forest canopy. This procedure involves the generation of a matrix, where each cell, with a resolution defined by the specialist (1 m × 1 m in the case of this research), receives the value of the first pulse received within that cell. The scanning process was set up by the company that carried out the survey to acquire approximately 21 pulses per square meter of land surface from the airborne LiDAR Optech ALTM Gemini sensor. The ratio of these two DSMs was also obtained to highlight the areas with tree extraction. Pixels representing selectively logged forests show higher values using this ratio. Subsequently, the pixels representing selectively logged forests were manually delineated on a computer screen and converted into vector-based shapefile format and considered as ground truth ( Figure 2). The image preprocessing included the following steps: 1. Download of the complex images in H5 format, which stores the data in hierarc data format (HDF), containing the sensor's scan metadata; 2. Multi-look filtering, defined as one look in range and azimuth, which resulted grid cell of 3 m, representing the best spatial resolution of the StripMap im acquisition mode, and conversion from slant range to ground range; 3. Co-registration for correction of relative translational and rotational deviations scale difference between images; 4. Application of the GammaMAP filter [51], with a 3 × 3 window and ENL 1.1, w was pointed out by [33] as presenting the best results for multitemporal analyz tropical forests; 5. Geocoding using the digital elevation model produced from the Phased Array L-band Synthetic Aperture Radar (PALSAR) sensor and conversion to the backsc coefficients (σ°, units in dB).

Cloud Computing of LiDAR Points
The LiDAR point clouds acquired in 2018 and 2019 over a portion of UPA 11 processed to generate a digital surface model (DSM) from the first return points, w correspond to the pulses with the shortest time between emission and return, whic those that correspond to the outermost surface of forest canopy. This procedure invo the generation of a matrix, where each cell, with a resolution defined by the special m × 1 m in the case of this research), receives the value of the first pulse received w that cell. The scanning process was set up by the company that carried out the surv acquire approximately 21 pulses per square meter of land surface from the airb LiDAR Optech ALTM Gemini sensor. The ratio of these two DSMs was also obtain highlight the areas with tree extraction. Pixels representing selectively logged fo show higher values using this ratio. Subsequently, the pixels representing select logged forests were manually delineated on a computer screen and converted into ve based shapefile format and considered as ground truth ( Figure 2).

SAR Attribute Extraction
The first step for SAR attribute extraction consisted of generating the coefficients of variation (CV) between two SAR images. In this case, the images from 5 June 2018 and 8 October 2018 were chosen as Time 1 (T1) and Time 2 (T2), respectively. CV has been used to detect changes in SAR images as an alternative to the normalized ratio, with good results [52][53][54]. CV can assume real numbers within the 0-1 range, where 0 means no Remote Sens. 2021, 13, 3341 5 of 22 change. Several attempts were made to define the threshold between selective logging and undisturbed forests. The threshold should result in commission errors mostly related to those selectively logged and undisturbed forests and omission errors close to zero. This threshold was applied to the CV image, resulting in a binary image where 0 represents undisturbed forests and 1 represents logged forests. Then, pixels with a value of 1 were converted into polygons. For each polygon, the following spatial attributes were calculated: area, perimeter, circularity, and shape factor [55]. A kernel map was also generated from those polygons using the area as the weight to define the centroids. The large density of polygons representing isolated pixels received smaller weights than the groups of larger polygons, which were more characteristic of selective logging. For each polygon, several zonal statistics were generated (count, sum, mean, median, standard deviation, minimum, maximum, range, minority, majority, and variance [56]) for the following matrices: 1.
Each polygon received 154 spectral, textural, and spatial attributes. With the help of selective logging samples obtained through the steps described in Section 2.3, each polygon was classified as timber extraction and no-timber extraction. This attribute is essential for the machine learning process, since this label is used by the algorithms to learn which other attributes describe each class and define their limits.

Classification Tests through Machine Learning
Orange data mining [59] was used for testing machine learning algorithms, which is a platform to perform data analysis and visualization based on visual programming, with the possibility of implementation using Python 3 libraries. Initially, to evaluate the predictive performance of the machine learning model [60], the dataset was divided randomly into training (70%) and validation (30%) sets, respectively. The sampling set was fixed to allow subsequent tests to use the same dataset, making the results comparable with each other. It was also stratified to mimic the composition of the input dataset. The attributes were used as input in the supervised machine learning algorithms. The first test was conducted considering the random forest classifier, which offers some advantages over most statistical modeling methods, including the ability to model nonlinear relationships, resistance to overfitting, and relative robustness to the presence of noise in the data [39]. Random forest is an ensemble classifier that uses several classification and regression trees to perform a prediction [61] (Figure 3).
Each decision tree is produced independently, and each node is split using a randomly selected number of user-defined attributes (Mtry). By growing the forest to a user-defined number of trees (Ntree), the algorithm creates trees with high variance and low bias. The final decision can be made using different strategies, including the average probability of class assignment from all produced trees. A new unlabeled data entry is evaluated against all decision trees created in the set and each tree vote for a membership class. The membership class with the highest number of votes is selected.
Studies involving application of random forest for SAR data indicated that Ntree = 70 is the best suggestion without gain in the classification results [62,63]. The Mtry default Remote Sens. 2021, 13, 3341 6 of 22 is defined as the square root of the number of input attributes. In this study, the random forest classifier was analyzed considering several parameters, as defined in Table 2. Each decision tree is produced independently, and each node is split using a randomly selected number of user-defined attributes (Mtry). By growing the forest to a user-defined number of trees (Ntree), the algorithm creates trees with high variance and low bias. The final decision can be made using different strategies, including the average probability of class assignment from all produced trees. A new unlabeled data entry is evaluated against all decision trees created in the set and each tree vote for a membership class. The membership class with the highest number of votes is selected.
Studies involving application of random forest for SAR data indicated that Ntree = 70 is the best suggestion without gain in the classification results [62,63]. The Mtry default is defined as the square root of the number of input attributes. In this study, the random forest classifier was analyzed considering several parameters, as defined in Table 2. The boosted decision trees (DTs) (or augmented DTs; Figure 4) are also assembly methods that rely on DTs. In these cases, as the models are built, they are adjusted in an attempt to minimize the errors of the previous trees [64]. One type of boosted DT is Adaptative Boosting or Adaboost, which was also evaluated in this study. This method comprises three components: weak learners (individual bad predictors), a function that applies a penalty for incorrect classifications, and an additive model which allows individual weak learners to be combined in such a way that the loss function is minimized. The algorithm considers all trees, since the additive model is designed in such a way that the combination of all trees provides an optimal solution. Tests with the AdaBoost  The boosted decision trees (DTs) (or augmented DTs; Figure 4) are also assembly methods that rely on DTs. In these cases, as the models are built, they are adjusted in an attempt to minimize the errors of the previous trees [64]. One type of boosted DT is Adaptative Boosting or Adaboost, which was also evaluated in this study. This method comprises three components: weak learners (individual bad predictors), a function that applies a penalty for incorrect classifications, and an additive model which allows individual weak learners to be combined in such a way that the loss function is minimized. The algorithm considers all trees, since the additive model is designed in such a way that the combination of all trees provides an optimal solution. Tests with the AdaBoost classifier were performed considering different numbers of estimators ( Table 3). The weak learners used by the classifier were the DTs.
Artificial neural networks (ANNs) have been used in various applications [65], including remote sensing [66]. The basic elements of an ANN are neurons, equivalent to biological axons, which are organized in layers ( Figure 5).
An ANN has input and output layers, for example, one neuron for each input variable and one neuron for each output class. ANNs typically have hidden nodes organized into one or more additional layers. Some models have the characteristic that all neurons in a layer are connected to all neurons in all adjacent layers [67]. These connections have fully connected weights. The weights in the connections, in combination with the typically Remote Sens. 2021, 13, 3341 7 of 22 nonlinear activation function, further modify the values in each neuron and determine how the input values are mapped to values in the output nodes. The potential of describing very complex decision boundaries can be improved by increasing the number of neurons in the hidden layer, especially by adding even more hidden layers. Neural networks are normally trained by initial random values for the weights which are then iteratively adjusted, observing the effect on the output nodes [67].
Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 23 classifier were performed considering different numbers of estimators ( Table 3). The weak learners used by the classifier were the DTs.  1.00 SAMME.R 1 The learning rate determines the extent to which newly acquired information will replace old information (0 = agent learns nothing; 1 = agent considers the most recent information). 2 Rank algorithm: SAMME (updates the base estimator weights with ranking results) or SAMME.R (updates the base estimator weights with probability estimates).
Artificial neural networks (ANNs) have been used in various applications [65], including remote sensing [66]. The basic elements of an ANN are neurons, equivalent to biological axons, which are organized in layers ( Figure 5).  The learning rate determines the extent to which newly acquired information will replace old information (0 = agent learns nothing; 1 = agent considers the most recent information). 2 Rank algorithm: SAMME (updates the base estimator weights with ranking results) or SAMME.R (updates the base estimator weights with probability estimates).
In general, the challenges in using ANNs are as follows: the training stage can be slow and laborious; there can be network overfitting; there is a need for several parameters to be specified by the user [64]. Some advantages of neural approaches in remote sensing are as follows: more accurate performance than other techniques such as statistical methods, particularly when the feature space is complex and the source data have different statistical distributions; more rapid performance than other techniques such as statistical classifiers; incorporation of a priori knowledge and realistic physical constraints into the analysis; incorporation of different types of data (including those from different sensors) [68].
There are several architectures of ANNs applicable to problems in remote sensing [34]. The architecture of the ANN tested in this work was a feedforward network with multiple layers, called multilayer perceptron (MLP). Feedforward ANNs are the most commonly applied, where the flow is always toward the output layer. When there is more than one hidden layer, these networks are called deep neural networks or deep learning [69]. An ANN has input and output layers, for example, one neuron for each input variable and one neuron for each output class. ANNs typically have hidden nodes organized into one or more additional layers. Some models have the characteristic that all neurons in a layer are connected to all neurons in all adjacent layers [67]. These connections have fully connected weights. The weights in the connections, in combination with the typically nonlinear activation function, further modify the values in each neuron and determine how the input values are mapped to values in the output nodes. The potential of describing very complex decision boundaries can be improved by increasing the number of neurons in the hidden layer, especially by adding even more hidden layers. Neural networks are normally trained by initial random values for the weights which are then iteratively adjusted, observing the effect on the output nodes [67].
In general, the challenges in using ANNs are as follows: the training stage can be slow and laborious; there can be network overfitting; there is a need for several parameters to be specified by the user [64]. Some advantages of neural approaches in remote sensing are as follows: more accurate performance than other techniques such as statistical methods, particularly when the feature space is complex and the source data have different statistical distributions; more rapid performance than other techniques such as statistical classifiers; incorporation of a priori knowledge and realistic physical constraints into the analysis; incorporation of different types of data (including those from different sensors) [68].
There are several architectures of ANNs applicable to problems in remote sensing [34]. The architecture of the ANN tested in this work was a feedforward network with multiple layers, called multilayer perceptron (MLP). Feedforward ANNs are the most commonly applied, where the flow is always toward the output layer. When there is more than one hidden layer, these networks are called deep neural networks or deep learning [69].
With the aim of finding the best ANN topology for the data considered in this work, several hyperparameters were tested (Table 4), varying the number of neurons in each hidden layer, the number of hidden layers, the activation function (Relu and hyperbolic tangent (tanh)), the weight optimizers (L-BFGS-B [70], SGD [71], and ADAM [72]), the maximum number of iterations, and the stop criteria. The tests from NN61 to NN70 were performed using the five topologies tested from NN1 to NN60 that showed the best results, changing the activation function to hyperbolic tangent and the maximum number of iterations of 2000. With the aim of finding the best ANN topology for the data considered in this work, several hyperparameters were tested (Table 4), varying the number of neurons in each hidden layer, the number of hidden layers, the activation function (Relu and hyperbolic tangent (tanh)), the weight optimizers (L-BFGS-B [70], SGD [71], and ADAM [72]), the maximum number of iterations, and the stop criteria. The tests from NN61 to NN70 were performed using the five topologies tested from NN1 to NN60 that showed the best results, changing the activation function to hyperbolic tangent and the maximum number of iterations of 2000.
Test results were evaluated by applying the trained network over the validation set by calculating the following parameters [73]: Area under receiver operator curve (AUC): an AUC of 0.5 suggests no discrimination between classes; 0.7 to 0.8 is considered acceptable; 0.8 to 0.9 is considered excellent; more than 0.9 is considered exceptional.
Precision: proportion of true positives among correctly classified samples as positive, for example, the extract proportion correctly classified as selectively logged. 5.
Recall: proportion of true positives among all positive instances in the data. 6.
In order to evaluate the results obtained with each classifier and typology, the confusion matrices of the best results obtained were compared, indicating the proportion of error in each class separately. To test the generalizability of the training set, five topologies that presented the best classification results were analyzed for the complete COSMO-SkyMed scene, including the area that exceeds the limits of UPA 11 (Figure 1), validating the results obtained by comparing them with the records of extracted trees provided by the SFB.
The generalization capability was also tested with the application of the network trained to detect the features of selective logging that occurred between the COSMO-SkyMed acquisitions on 8 January 2018 and 5 June 2018.

Exploratory Attribute Analysis
A threshold of 0.4 was obtained in the CV image from the June and October 2018 overpasses of the COSMO-SkyMed satellite, including changes (nonstationary processes) and speckle (stationary process) [53]. This procedure covered all pixels classified as timber extraction in the LiDAR images plus 70% commission errors (mainly speckle). Among the 2974 polygons generated, 870 belonged to the timber extraction class and 2104 belonged to the no-timber extraction class. By overlaying the samples selected as timber extraction in the LiDAR images, the coordinates of extracted trees provided by the SFB, and the polygons generated by the thresholding of the CV, we found 849 trees extracted in the UPA 11. SAR images were able to identify 96% of these trees, while the remaining 4% were related to extractions with little change in the canopy structure and, therefore, not detected in the X-band SAR data. This limited ability to detect selective logging through canopy gaps was previously reported by [6,7].
The exploratory analysis of the attributes showed redundancy and overlapping of several attributes. Figure 6 exemplifies, through boxplots of four attributes randomly selected from the set, that the data are linearly inseparable, which points out the need to use classifiers capable of dealing with these situations [38,44], as proposed in this work. The training set resulted in a total of 2082 samples stratified into timber extraction and no-timber extraction. The samples, although randomly selected, were replicable; that is, all tests used the same set to avoid differences in results arising from different sample selections. Table 5 shows the results obtained from the variation of the parameters and topology of the random forest algorithm. All topologies based on random forest presented good results, since the accuracy and the precision obtained were close to 95% and the AUC values were high (near 0.95).

Tests with the Random Forest Classifier
A common problem in machine learning techniques is the comparison of the classification results (topologies) applied to a dataset. Corani and Benavoli [74] presented a Bayesian approach for comparative statistical inference of two concurrent algorithms evaluated through cross-validation. Such an approach is composed of two parts: the first, constituted by a proposal of correlated Bayesian t-test for the analysis of the results of the cross-validation of a single dataset responsible for the correlation due to the overlapping training sets; the second, by merging the posterior probabilities calculated by the Bayesian t-test correlated on the different datasets to make inferences on various datasets (or subsets) by adopting a binomial Poisson model. Inferences across multiple datasets account for different uncertainties in cross-validation results over different datasets. The performance of this test in the k-fold cross-validation with k = 10 showed that all random forest topologies were equivalent, with a probability of 0.03.
in the LiDAR images, the coordinates of extracted trees provided by the SFB, and the polygons generated by the thresholding of the CV, we found 849 trees extracted in the UPA 11. SAR images were able to identify 96% of these trees, while the remaining 4% were related to extractions with little change in the canopy structure and, therefore, not detected in the X-band SAR data. This limited ability to detect selective logging through canopy gaps was previously reported by [6,7].
The exploratory analysis of the attributes showed redundancy and overlapping of several attributes. Figure 6 exemplifies, through boxplots of four attributes randomly selected from the set, that the data are linearly inseparable, which points out the need to use classifiers capable of dealing with these situations [38,44], as proposed in this work. The training set resulted in a total of 2082 samples stratified into timber extraction and notimber extraction. The samples, although randomly selected, were replicable; that is, all tests used the same set to avoid differences in results arising from different sample selections. Figure 6. Boxplots showing the distribution of four attributes (normalized maximum ratio, minimum, shape, and gradient ranges) for the timber extraction and no-timber extraction classes. Table 5 shows the results obtained from the variation of the parameters and topology of the random forest algorithm. All topologies based on random forest presented good results, since the accuracy and the precision obtained were close to 95% and the AUC values were high (near 0.95).    Figure 7 shows the confusion matrices of the accuracy, omission, and commission errors regarding the nine selected random forest topologies. RF3 showed better precision and accuracy: accuracy rates of 87% and 97.2% for the selectively logged forest and undisturbed forest classes, respectively. The shortest training and validation times were obtained with the RF1 and RF2, although RF3 also presented very close times.

Tests with the Random Forest Classifier
Yu et al. [40] previously highlighted random forest as a good SAR image classifier together with optical images for forest type classification. Gosh and Behera [38] showed good results with the same method applied to biomass estimation by combining optical and SAR images acquired using Sentinel satellites. Shiraishi et al. [75] compared several machine learning methods for land-use classification in SAR images, and the best results obtained were also obtained with the random forest technique.
performance of this test in the k-fold cross-validation with k = 10 showed that all ran forest topologies were equivalent, with a probability of 0.03. Figure 7 shows the confusion matrices of the accuracy, omission, and comm errors regarding the nine selected random forest topologies. RF3 showed better pre and accuracy: accuracy rates of 87% and 97.2% for the selectively logged fores undisturbed forest classes, respectively. The shortest training and validation times obtained with the RF1 and RF2, although RF3 also presented very close times. Yu et al. [40] previously highlighted random forest as a good SAR image clas together with optical images for forest type classification. Gosh and Behera [38] sh good results with the same method applied to biomass estimation by combining o and SAR images acquired using Sentinel satellites. Shiraishi et al. [75] compared se machine learning methods for land-use classification in SAR images, and the best r obtained were also obtained with the random forest technique. Table 6 shows the results obtained from the variation of the parameters and top in the AdaBoost classifier. The variation of the topology in terms of number of estim did not result in significant changes in any of the quality measures of the classifiers all being considered equivalent.  Table 6 shows the results obtained from the variation of the parameters and topology in the AdaBoost classifier. The variation of the topology in terms of number of estimators did not result in significant changes in any of the quality measures of the classifiers, with all being considered equivalent. The confusion matrices ( Figure 8) showed that the timber extraction class had classification accuracy close to 80% in all topologies, while the undisturbed forest class varied around 94%, without significant gain with an increase in the number of estimators. AB2, AB3, and AB4 topologies had a higher accuracy rate for the selectively logged forest class, although slightly higher accuracy and precision values were obtained by AB5 and AB6.

Tests with the AdaBoost Classifier
Remote Sens. 2021, 13, x FOR PEER REVIEW 12 The confusion matrices ( Figure 8) showed that the timber extraction class classification accuracy close to 80% in all topologies, while the undisturbed forest varied around 94%, without significant gain with an increase in the number of estim AB2, AB3, and AB4 topologies had a higher accuracy rate for the selectively logged class, although slightly higher accuracy and precision values were obtained by AB AB6. In general, the AdaBoost classifier performed worse than the random forest same conclusion was reported by Shiraishi et al. [75], who evaluated dif methodologies applied to SAR for classification of land use and land cover in a reg tropical forests.

Tests with MLP-ANN
The typologies used in the NN61 to NN70 tests, which replicated the typolog the five best results obtained in tests from NN1 to NN60, were NN23, NN26, NN31, N In general, the AdaBoost classifier performed worse than the random forest. The same conclusion was reported by Shiraishi et al. [75], who evaluated different methodologies applied to SAR for classification of land use and land cover in a region of tropical forests.

Tests with MLP-ANN
The typologies used in the NN61 to NN70 tests, which replicated the typologies of the five best results obtained in tests from NN1 to NN60, were NN23, NN26, NN31, NN22, and NN21, respectively. Table 7 shows the 10 best results obtained from the variation of the parameters and topology of the NNs, in relation to accuracy and precision. Confusion matrices showing accuracy and omission and commission errors as a percentage of the best results obtained through the ANNs are presented in Figure 9. The highest accuracy and precision were obtained with the NN26 topology, with a function of ReLu activation, extensively applied in neural networks for image classification [76], and the SGD weight optimizer, contradicting the results obtained by [77] (although, for class tree, the results obtained by [77] were very close for all optimizers), showing no difference with an increase in the number of iterations. The referred topologies resulted in 89% and 96.4% accuracy for the selectively logged and undisturbed forest classes, respectively. The test time was also the lowest (0.161 s), and the training time (9456 s) was significantly lower than those presented by the NN29, NN32, and NN37 topologies. Confusion matrices showing accuracy and omission and commission errors as percentage of the best results obtained through the ANNs are presented in Figure 9. Th highest accuracy and precision were obtained with the NN26 topology, with a function o ReLu activation, extensively applied in neural networks for image classification [76], an the SGD weight optimizer, contradicting the results obtained by [77] (although, for clas tree, the results obtained by [77] were very close for all optimizers), showing no differenc with an increase in the number of iterations. The referred topologies resulted in 89% an 96.4% accuracy for the selectively logged and undisturbed forest classes, respectively. Th test time was also the lowest (0.161 s), and the training time (9456 s) was significantl lower than those presented by the NN29, NN32, and NN37 topologies.

Comparative Assessment of Machine Learning Techniques
All tests performed showed good results for detecting selective logging, a previously shown in the land use and land cover classification studies [43], pointing ou that X-band SAR images, associated with machine learning techniques, despite the shallow interaction with the canopy, have potential in relation to other SAR bands an other methodologies presented in the literature [25,27], especially due to the hig revisiting capacity of the COSMO-SkyMed constellations and the need for only on multitemporal pair of images.
Among the different architectures tested in the set of attributes generated on th images before and after exploitation, the best result was obtained by the NN26 topology

Comparative Assessment of Machine Learning Techniques
All tests performed showed good results for detecting selective logging, as previously shown in the land use and land cover classification studies [43], pointing out that X-band SAR images, associated with machine learning techniques, despite their shallow interaction with the canopy, have potential in relation to other SAR bands and other methodologies presented in the literature [25,27], especially due to the high revisiting capacity of the COSMO-SkyMed constellations and the need for only one multitemporal pair of images.
Among the different architectures tested in the set of attributes generated on the images before and after exploitation, the best result was obtained by the NN26 topology, which presented a precision and accuracy of 0.961, being 89% correspondent with the validation samples in terms of selective logging class and 96.4% in terms of undisturbed forest class. Despite exhaustive tests varying the parameters of the RF and neural network topologies, the results did not show an improvement in relation to those presented. This fact indicated the need for an analysis of the NN26 topology error.
Analyzing the boxplots of features correctly classified as timber extraction with those that were misclassified as no-timber extraction, it was seen that the center, amplitude, and symmetry of these features are different (Figure 10), which exemplifies some randomly selected attributes and presents the t-test, rejecting the null hypothesis and demonstrating that the means are statistically different. Likewise, comparing the features correctly classified as undisturbed forest with those that were misclassified as selectively logged forests, asymmetric data were found. In summary, the features classified erroneously are outliers, with attribute values differing from the rest of the samples. classified as undisturbed forest with those that were misclassified as selectively logged forests, asymmetric data were found. In summary, the features classified erroneously are outliers, with attribute values differing from the rest of the samples. Analyzing the LiDAR data available for the study area, it was possible to observe that the extracted trees informed by the companies were not delimited in the stage of thresholding of the CV. Therefore, they were not included in the classification process in timber extraction or no-timber extraction (11% of the trees cut). They are small crown structures (between 6 m and 10 m in diameter) with corymbiform architecture [78] and not emergent in relation to neighboring crowns ( Figure 11). Figure 11. Digital surface models generated from LiDAR data before and after exploitation, showing a tree undetected by the proposed method.
According to Locks [7], who carried out a study of canopy damage identification by selective logging based on LiDAR data, about 93.3% of felled trees result in canopy Analyzing the LiDAR data available for the study area, it was possible to observe that the extracted trees informed by the companies were not delimited in the stage of thresholding of the CV. Therefore, they were not included in the classification process in timber extraction or no-timber extraction (11% of the trees cut). They are small crown structures (between 6 m and 10 m in diameter) with corymbiform architecture [78] and not emergent in relation to neighboring crowns ( Figure 11).
According to Locks [7], who carried out a study of canopy damage identification by selective logging based on LiDAR data, about 93.3% of felled trees result in canopy damage; that is, approximately 7% of the extractions are unidentified in SAR images that interact at the surface level of canopy, as is the case of the X-band.
Analyzing the LiDAR data available for the study area, it was possible to observe that the extracted trees informed by the companies were not delimited in the stage of thresholding of the CV. Therefore, they were not included in the classification process in timber extraction or no-timber extraction (11% of the trees cut). They are small crown structures (between 6 m and 10 m in diameter) with corymbiform architecture [78] and not emergent in relation to neighboring crowns ( Figure 11). Figure 11. Digital surface models generated from LiDAR data before and after exploitation, showing a tree undetected by the proposed method.
According to Locks [7], who carried out a study of canopy damage identification by selective logging based on LiDAR data, about 93.3% of felled trees result in canopy damage; that is, approximately 7% of the extractions are unidentified in SAR images that interact at the surface level of canopy, as is the case of the X-band.

Generalization Test
Generalization capability refers to the ability of the network to correctly classify data not used for network training [67]. In this work, the NN26 generalization ability test was performed, the first step of which consisted of thresholding the CV image generated between the COSMO-SkyMed overpasses from June-October and January-June 2018, at Figure 11. Digital surface models generated from LiDAR data before and after exploitation, showing a tree undetected by the proposed method.

Generalization Test
Generalization capability refers to the ability of the network to correctly classify data not used for network training [67]. In this work, the NN26 generalization ability test was performed, the first step of which consisted of thresholding the CV image generated between the COSMO-SkyMed overpasses from June-October and January-June 2018, at a value of 0.4. This procedure resulted in 186,886 polygons for the June-October images and 224,250 polygons for the January-June images ( Figure 12).   Figure 12 also presents the results of the classification through NN26 for the extraction class. The data were validated by selecting all polygons contained in the area where the forest inventory provided by the SFB is available, which includes the geographic position of the cut trees, comparing them with the classification result using the inventory data. For the result obtained between the COSMO-SkyMed overpasses of 5 June 2018 and 8 October 2018, on which the typologies were trained, the selectively logged and undisturbed forests correctly classified represented 89% and 95%, respectively. The overall accuracy was 91%.
An analysis of the forest inventory data showed that 11% of the trees reported as being cut were unidentified and did not even participate in the classification process because they were not delimited in the first stage (thresholding CV). The evaluation of 11% of trees cut and undetected showed that 36% represent logging that did not result in forest clearance; therefore, it was not possible to identify them through high-frequency SAR sensors (Figure 13). Those features were also unidentified in the digital surface model product generated from the last LiDAR return. The remaining 64% are trees with small  Figure 12 also presents the results of the classification through NN26 for the extraction class. The data were validated by selecting all polygons contained in the area where the forest inventory provided by the SFB is available, which includes the geographic position of the cut trees, comparing them with the classification result using the inventory data. For the result obtained between the COSMO-SkyMed overpasses of 5 June 2018 and 8 October 2018, on which the typologies were trained, the selectively logged and undisturbed forests correctly classified represented 89% and 95%, respectively. The overall accuracy was 91%.
An analysis of the forest inventory data showed that 11% of the trees reported as being cut were unidentified and did not even participate in the classification process because they were not delimited in the first stage (thresholding CV). The evaluation of 11% of trees cut and undetected showed that 36% represent logging that did not result in forest clearance; therefore, it was not possible to identify them through high-frequency SAR sensors ( Figure 13). Those features were also unidentified in the digital surface model product generated from the last LiDAR return. The remaining 64% are trees with small crowns or shaded by neighboring crowns and located in front of them in relation to the sensor imaging direction. According to [16], the detection of changes in the shadowing effect depends on factors such as polarization, incidence angle, and characteristics of the study area. In this study, three-dimensional features involving the target, surroundings, and sensor characteristics made the change detection partially difficult. For the January-June COSMO-SkyMed images, correctly classified timber extractions and no-timber extractions represented 88% and 92%, respectively. The overall accuracy was 89%. The January image was noisy (Figure 14), changing the values of the backscatter coefficient and, consequently, generating high values of CV between the images, causing a high incidence of false positives for the timber extraction class. Therefore, it is necessary to observe the occasional noise, which may result from atmospheric effects frequently present in X-band data [79] or sensor problems, which may interfere in the quality of the results. Although machine learning algorithms were trained using data collected in areas of legal forest concession, the generalization test highlighted logging from areas outside the studied National Forest. The overlapping of the logging activities detected in the period between June and October with the polygons pointed out by the Selective Logging For the January-June COSMO-SkyMed images, correctly classified timber extractions and no-timber extractions represented 88% and 92%, respectively. The overall accuracy was 89%. The January image was noisy (Figure 14), changing the values of the backscatter coefficient and, consequently, generating high values of CV between the images, causing a high incidence of false positives for the timber extraction class. Therefore, it is necessary to observe the occasional noise, which may result from atmospheric effects frequently present in X-band data [79] or sensor problems, which may interfere in the quality of the results. For the January-June COSMO-SkyMed images, correctly classified timber extractions and no-timber extractions represented 88% and 92%, respectively. The overall accuracy was 89%. The January image was noisy (Figure 14), changing the values of the backscatter coefficient and, consequently, generating high values of CV between the images, causing a high incidence of false positives for the timber extraction class. Therefore, it is necessary to observe the occasional noise, which may result from atmospheric effects frequently present in X-band data [79] or sensor problems, which may interfere in the quality of the results. Although machine learning algorithms were trained using data collected in areas of legal forest concession, the generalization test highlighted logging from areas outside the studied National Forest. The overlapping of the logging activities detected in the period between June and October with the polygons pointed out by the Selective Logging Although machine learning algorithms were trained using data collected in areas of legal forest concession, the generalization test highlighted logging from areas outside the studied National Forest. The overlapping of the logging activities detected in the period between June and October with the polygons pointed out by the Selective Logging Detection System (DETEX) [80] as areas of illegal logging that took place between June and October, from the partnership between the SFB and the National Institute for Space Research (INPE), demonstrated that our methodology may be useful in detecting illegal logging in the Brazilian Amazon, as there was a good agreement between our study's approach and that of INPE ( Figure 15). The advantage of the proposed method is that, once based on SAR data, it allows monitoring even under cloud cover; moreover, given the correlation between gaps and log volume [7], it allows estimating the intensity of logging.
Remote Sens. 2021, 13, x FOR PEER REVIEW 17 of 23 the correlation between gaps and log volume [7], it allows estimating the intensity of logging. Deutscher et al. [54] reported that X-band SAR data from the TerraSAR-X sensor are able to identify disturbances in tropical forests larger than 0.5 ha with an accuracy of 98%. In this research, it was shown that X-band SAR data are also promising for the detection of smaller scars, such as selective logging, and that, in conjunction with machine learning techniques, present high generalization capacity, resulting in greater accuracy than 85% for the extraction class.

Discussion
Our study is the first to investigate the potential of X-band SAR images acquired in the StripMap mode for monitoring selective logging in tropical forests. Our results indicate good possibility for the use of this dataset, as the logging activity causes changes in the backscattering processes because of the changes in the structure of the forest canopy. However, about 7% of the extracted trees could not be identified because of the insufficient SAR spatial resolution and because of the arrangements of the extracted tree crowns in relation to their neighbors, as observed by Locks [7].
The large amount of data available on the Jamari National Forest (LiDAR images and the forest inventory) allowed the identification of forest scars caused by selective logging, essential for training the machine learning algorithms tested in this study. Multitemporal changes in shading and illumination in the X-band SAR images, whose transmitted signals mostly interact at the top of canopies, were able to be detected and delimited by defining a threshold in terms of the coefficient of variation. These changes in shading and lighting were also reported by Bouvet et al. [16] as potential criteria for deforestation detection and mapping. The threshold based on coefficient of variation, as reported by Koeniguer and Nicolas [53], is suitable for detecting changes due to its simple formulation and remarkable statistical properties.
Koeniguer and Nicolas [53] presented the first theoretical study demonstrating that the coefficient of variation is relevant for detecting changes even in areas with speckle, Deutscher et al. [54] reported that X-band SAR data from the TerraSAR-X sensor are able to identify disturbances in tropical forests larger than 0.5 ha with an accuracy of 98%. In this research, it was shown that X-band SAR data are also promising for the detection of smaller scars, such as selective logging, and that, in conjunction with machine learning techniques, present high generalization capacity, resulting in greater accuracy than 85% for the extraction class.

Discussion
Our study is the first to investigate the potential of X-band SAR images acquired in the StripMap mode for monitoring selective logging in tropical forests. Our results indicate good possibility for the use of this dataset, as the logging activity causes changes in the backscattering processes because of the changes in the structure of the forest canopy. However, about 7% of the extracted trees could not be identified because of the insufficient SAR spatial resolution and because of the arrangements of the extracted tree crowns in relation to their neighbors, as observed by Locks [7].
The large amount of data available on the Jamari National Forest (LiDAR images and the forest inventory) allowed the identification of forest scars caused by selective logging, essential for training the machine learning algorithms tested in this study. Multitemporal changes in shading and illumination in the X-band SAR images, whose transmitted signals mostly interact at the top of canopies, were able to be detected and delimited by defining a threshold in terms of the coefficient of variation. These changes in shading and lighting were also reported by Bouvet et al. [16] as potential criteria for deforestation detection and mapping. The threshold based on coefficient of variation, as reported by Koeniguer and Nicolas [53], is suitable for detecting changes due to its simple formulation and remarkable statistical properties.
Koeniguer and Nicolas [53] presented the first theoretical study demonstrating that the coefficient of variation is relevant for detecting changes even in areas with speckle, and that it has different statistical properties for at least three categories of temporal profiles: permanent disperser, stable natural speckle area, and a nonstationary area that generally is interpreted as a change. In this study, the forest areas that did not face anthropogenic interference were considered natural with stable speckle, and the selective loggings corresponded to changes. A threshold of 0.4 enabled the delimitation of changed features (selective logging) together with some other changes related to speckle and atmospheric interference (false positives).
The machine learning techniques considered in this study showed good results in comparison with previous studies on the automatic detection of selective logging based on high-resolution optical or SAR images, semiautomated methods, and low temporal frequency [26,81,82]. The advantages over previous studies are as follows: (1) after training the algorithms, the method is fully automatic, reducing errors associated with human interpretation; (2) the possibility of constant monitoring even under adverse weather conditions; (3) high revisit capability of X-band COSMO-SkyMed constellation satellites; (4) high generalization capacity of pretrained networks, allowing the use of the same training set if the SAR images are acquired under the same image acquisition modes.
The tests of generalization capacity presented results close to those obtained by the algorithms that rely on training processes, demonstrating high generalization of the trained networks. A frequent reported problem in studies based on machine learning classifiers is the specialization of the trained network (or overfitting) and the problem of local minima, limiting the generalization capacity and demanding new training samples for each scene to be classified [83]. A hypothesis suggested for the good results obtained in this work is the stability of the SAR signal in the images used and the use of textural and spatial attributes, influenced by spectral changes on a smaller scale than the spectral attributes. In addition to the ionospheric effects, climate and seasonal surface conditions can considerably affect SAR measurements, modifying timeseries characteristics and limiting large-scale applications [84]. In the case of X-band SAR images, rain interception, for example, can add up to 3 dB to the backscattered signal. Several authors have studied these phenomena and pointed out solutions for stabilizing the multitemporal SAR data signal [84].
The image acquired in January 2018 used in this study showed radiometric problems related to the sensor, as can be seen in Figure 14, which did not negatively affect the generalizability of the pretrained network. However, the method still needs to be tested regarding the generalizability of the prediction according to factors that can affect the detection by the model, such as scenes covering distinct forests and obtained under adverse weather conditions. Convolutional neural network (CNN) tests are also suggested, which have great potential for generalization in areas not seen before, as they use contextual information and are not strongly affected by absolute pixel values [85]. Other suggested approaches to reduce the overfitting problem are endless learning [86] and self-taught learning [87], which can be tested in the future.
Although several tests were performed using deep neural networks, the best result was obtained using a single-layer neural network. Regardless of the depth of the network, an advantage of using artificial neural networks lies in the fact that learning is based on the characteristics of the data presented and not on the a priori knowledge of the interpreter [83]. Thus, an important step to obtain good results is the availability of reliable data for training algorithms. The adoption of neural networks for remote sensing problems has advantages in problems whose physical models are complex (nonlinear, for example), not yet well understood, or even difficult to be generalized [83]. The disadvantage of this type of approach is to find the appropriate hyperparameters for the dataset and problem considered, as well as the long training time (which increases with the number of hidden layers).
Machine learning-based approaches, especially CNN, are often treated as black boxes due to the difficulty in understanding the parameters that led the network to a given decision. Kattenborn et al. [85] justified this treatment due to the users' unfamiliarity with these techniques and the incomparable depth and number of parameters of these models. However, most machine learning models have a clear and linear structure and basic operators such as pooling and activation functions. Regarding the attributes used in this study, it is possible, through various approaches such as genetic algorithms [88], to know the individual contribution to obtaining the results, seeking combinatorial optimization. This will be the subject of further investigation, in addition to tests using CNN.

Conclusions
The images used in this study, in which acquisition dates coincided with the period before and after selective logging, allowed us to identify the features resulting from this type of activity, since all images were acquired according to the same parameters (orbit direction, angle of incidence, band, and polarization); therefore, temporal changes refer to changes in land cover instead of differences in acquisition parameters. Other factors that can cause multitemporal changes in X-band SAR images are severe atmospheric events and flaws in the imaging acquisition process.
To our best knowledge, no research had been published addressing the use of X-band SAR data acquired in StripMap mode for systematic monitoring of selective logging in a tropical forest. With the increasing availability of these data, through the launch of new X-band SAR constellations, new information extraction methodologies are required for applications that demand high revisits.
The machine learning techniques tested showed good results from the attributes generated with images acquired before and after the exploitation of the area, showing the potential for detecting selective logging in tropical forests through X-band SAR data. The accuracy rates obtained for the selectively logged forest class were approximately 88% using an ANN with 50 neurons in a hidden layer, with ReLu activation function and an SGD weight optimizer with 1000 iterations, even in the validation test applied to a dataset different from that used for training.
As the X-band interacts mostly at the top of the forest canopy, only selective extractions that cause canopy damage can be detected. As a result, 11% of the trees identified as being logged in UPA 11 could not be identified in the first stage of the proposed method, and those logged forests could not be detected using the digital surface model product derived from the first return of LiDAR data.
We recommend that future studies should better explore methods of suppression or minimization of errors related to systematic noise in images or caused by extreme meteorological events, which affect X-band backscatter signals, causing an increase in commission errors. Another suggestion is to extend the tests including the CNN machine learning algorithm and the development of a method based on the probability of being a logging event, rather than the binary classification adopted in this study.

Data Availability Statement:
The data presented in this study are available upon request from the author.