1. Introduction
The Legal Amazonia, also known as the Brazilian Amazon, covers a continuous area of more than five million square kilometers [
1], with approximately three million and two hundred thousand square kilometers of tropical forest [
2]. The Amazon forest plays an important role in the global carbon cycle [
3] and biodiversity conservation [
4] and provides diverse ecosystem services, many of which with considerable economic value [
5]. However, Brazil is among the tropical countries with the highest rates of forest loss [
6]. Between 2010 and 2019, the Legal Amazonia, which includes the states of Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima, Tocantins, and part of the Maranhão, lost about 65,348 km² of its forest [
7]. This loss corresponds to the gross carbon emission of 4366 million metric tons of carbon dioxide (Mt CO
2) in this area [
8]. The latest deforestation rate (from August 2018 to July 2019), released by the Project of Deforestation Monitoring of the Brazilian Amazon Forest by satellite (PRODES), showed a 29.5% deforestation increase in relation to the deforestation rate from 2018 [
7].
The last decade’s forest loss is due mainly to the unsustainable expansion of agriculture, cattle ranching, urbanization, illegal logging, and mining [
9,
10]. Moreover, the number of active fires in 2019 was the highest since 2010 [
11], when the Amazonia experienced a severe drought caused by El Niño and Atlantic Ocean warming events [
12]. In 2019, a total of 89,186 heat points were identified in the Legal Amazonia, corresponding to an increase of 30.5% when compared to the same period in 2018 (68,345 heat points) [
13]. Fires occurred mainly in Pará (27,412 km²), Amazonas (15,074 km²), Mato Grosso (14,638 km²), and Rondônia (11,611 km²) states, contributing significantly to the forest loss and degradation [
14].
Intensive forest degradation and deforestation observed in the last decades has led to significant losses of water resources, forest function, growing carbon balance variations, increase habitat fragmentations [
15,
16], and biodiversity threats [
17,
18]. In 2019, the Brazilian environmental protection policies were weakened because of the fewer economic resources destined for environmental monitoring by the two federal environmental institutes, the Brazilian Institute of Environment and Renewable Natural Resources (Ibama), and the Chico Mendes Institute for Biodiversity Conservation (ICMBio) [
19,
20]. This weakening may lead to extreme consequences not only for the Amazonia’s biodiversity conservation, but also for the achievement of national and world’s goals of reducing global carbon emissions, as well as of decreasing current global warming rates established in the Paris Agreement, Bonn Challenge, National Plan of Recovering Native Vegetation (PLANAVEG), and the Reducing Emissions from Deforestation and Forest Degradation (REDD+) [
19]. The active monitoring of the Amazonia rainforest is essential to preserve its role in climate change mitigation [
19,
20].
Among the nine states that cover the Legal Amazonia, Pará has the highest rates of clear-cut deforestation (4172 km² in 2019) [
7]. Clear-cut deforestation corresponds to the complete removal of forest cover in a short period of time [
21]. The expansion of agriculture and the illegal logging along BR-163 highway (Cuiabá-Santarém highway) and the construction of Port of Miritituba for grain exportation in the municipality of Itaituba are the major contributors to the high rates of deforestation in this state. Pará also showed one of the highest numbers of heat points in 2018 (total of 22,080 points) and 2019 (30,165 points) [
22].
In the southeastern portion of Pará state, we find the Tapajós National Forest (TNF), a federal conservation unit that comprises many of the unique attributes of the Brazilian Amazon, such as well-preserved forested areas with primary forest/old growth forest and forest in different successional stages. Three successional stages are found: advanced secondary succession (SS3); intermediate secondary succession (SS2); and initial secondary succession (SS1). The area is also surrounded by different types of land use. It also faces multi-factor anthropic pressures, illegal logging impacts [
23], and historically, the area has suffered from alarming fire spread events [
24]. In 2012, TNF lost 17,851 ha of its previous area, due to the Federal Law no. 12.678/2012 that reduced the TNF boundaries, increasing the pressure over considerable primary forest remnants in the excluded zone. Furthermore, amendments to the Brazilian Forest Act promulgated in the same year changed the minimum area requirement that should be maintained as legal reserve (Brazil’s environmental legislation obligates private properties to retain a fixed proportion of their total area with native vegetation, these areas are called “legal reserves”) [
25,
26]. In this context, studies involving land use and land cover (LULC) classes provide more detailed insights into the analysis of the cause/effect relationships of forest loss, allowing better surveillance and monitoring of conservation units.
Optical satellite remote sensing data has been intensively used for detecting and mapping landscape changes [
27]. Such information, in the form of maps, enables the understanding of deforestation patterns [
28]. However, the persistent cloud cover in most tropical regions severely restricts the use of optical remote sensing data. On the other hand, synthetic aperture radar (SAR) sensors are almost independent of atmospheric conditions and are sensitive to variations in forest biomass and structure [
29,
30]. SAR data allow a proper assessment of different LULC classes and forest degradation, especially by fire, even under cloudy conditions, or even under smoke conditions during active fires, as noted in some Amazonia rainforest sites [
31,
32]. Radar sensors with longer wavelengths, such as the one onboard the Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) satellite, present higher penetration of transmitted microwave signals into the forest canopy when compared with radar sensors with shorter wavelengths, such as the X-band, TerraSAR/TanDEM-X and Cosmo-SkyMed satellites, or the C-band, RADARSAT-2, and Sentinel-1A/1B satellites [
33]. SAR signals at relatively long wavelengths of ALOS/PALSAR can interact with tree stems, branches, trunks, and even ground, depending on the canopy structure [
34]. In addition, SAR images obtained in L-band provide better distinction between forest and other LULC classes [
31].
The use of ALOS/PALSAR data to monitor forest disturbances and LULC classes has increased in the past years. Pôssa et al. [
35] evaluated the potential of full polarimetric ALOS/PALSAR-1 scenes to map LULC in the TNF and surroundings by analyzing interferometric coherence and polarimetric attributes. An association of attributes derived by the Cloude–Pottier decomposition and interferometric coherence showed the best classification results, with an overall accuracy (OA) of 78.8% and Kappa index of 0.72. Pereira et al. [
36] also used ALOS/PALSAR-1 image with dual-polarization mode (HH and HV) to evaluate LULC classification in a section of Belterra municipality, Pará state. According to these authors, PALSAR scenes and selected features were not suitable for discrimination of densely forested classes. On the other hand, there was good discrimination among the groups of forested and agro-pastoral classes, as well as among nondensely forested classes, such as pastures, bare soil, and new regeneration. The classification result showed 67.0% for OA, and 0.38 for the Kappa index.
Mermoz and Le Toan [
37] used ALOS/PALSAR-1, dual-polarized data to assess rainforest forest disturbances and regrowth in Vietnam, Cambodia, and Laos between 2007 and 2010. The
ratio showed the best classification results in the time series, with a producer’s accuracy (PA) of 84.7%, and a user’s accuracy (UA) of 96.3%. Martins et al. [
38] evaluated the sensitivity of the full polarimetric ALOS/PALSAR-1 to forest degradation caused by fires in the Brazilian Amazon. They used polarimetric and derivate attributes from backscattering coefficients (σ°) to estimate above-ground biomass (AGB), from multiple regression models. The subset formed by anisotropy, double-bounce, orientation angle, volume index, phase difference, and
presented the best results. These results showed that the attributes were sensitive to canopy biomass variations due to fire events, but they were not capable of discriminating among intermediate classes. Forests mildly affected by fire had a higher contribution to the scattering in the VV polarization, while forests strongly affected by fire and those with multiple fire events had a larger sensitivity to the HH polarization. The results suggest a greater contribution of horizontally distributed components, such as fallen stems and branches in areas severely affected by fire.
Recent studies focusing on multi-sensor analysis for LULC mapping and change detection in tropical regions [
39,
40] have used dual-polarimetric ALOS/PALSAR-2 images integrated with Landsat multispectral images. Pavanelli et al. [
39] highlighted the SAR potential to discriminate savannah-like vegetation in the Brazilian Amazon, with an improvement in OA of 6% in relation to the classification results obtained only from optical data. De Alban et al. [
40], using a similar dataset, assessed the tropical landscapes of southern Myanmar using Random Forest (RF) classifier. They found that SAR-derived texture achieved an OA ranging from 56% to 71%, depending on the dataset used. However, these reported studies rely on low cloud cover conditions during the satellite image acquisition. Hagensieker and Waske [
41] highlighted the importance of using SAR images for overcoming such drawbacks in tropical regions. These authors used dual-polarized ALOS/PALSAR-2, RADARSAT-2, and TerraSAR-X images for LULC classification in the Brazilian Amazon, achieving OA of 62% for mono-temporal analysis with ALOS/PALSAR-2 images.
ALOS/PALSAR-2 was launched in 2014 as a successor of ALOS/PALSAR-1 that stopped operating in early 2011. Full-polarimetric images of the new ALOS mission, which includes improved PALSAR-2 sensor with better radiometric and spatial resolutions, have not been tested for LULC classification purposes in the Tapajós region yet. Similarly, this sensor’s potential to discriminate forest in different successional stages and to detect burn scars in forested areas has not yet been evaluated either. These better radiometric quality and higher spatial resolution are expected to increase classification accuracy.
In this study, the specific goals are: (1) to test the potential of ALOS/PALSAR-2 acquired in 2015 and 2016, full-polarimetric images processed by the polarimetric target decomposition techniques to discriminate different LULC classes and to detect forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classifications and degradation forest studies, and (3) to evaluate the performance of both RF and Support Vector Machine (SVM) classification algorithms to discriminate LULC classes and forest degradation over the TNF region.
It is important to highlight that although two dates are available, this study does not aim to quantitatively compare the results of both dates in terms of LULC changes. This is due to the fact that the ALOS/PALSAR-2 images used were acquired in different periods and seasons, with increased effects of seasonality. Additionally, land management practices also vary throughout the year, which can cause substantial errors in any change detection analysis [
42,
43,
44,
45].
4. Discussion
The attributes extracted from the YH decomposition showed the best classification results (
Table 5), demonstrating an adequate capability to discriminate different land use types, forest degradation, and land cover classes. The potential to discriminate classes with low levels of vegetation coverage (PP, WP, CR, and BS) was highlighted (
Figure 8 and
Figure 9). The only exception occurs for the CR class, with PA of 55.0% in 2015 formed by crops with different phenological stages. This class showed the lowest classification performance and was therefore an exception. Classes with low vegetation coverage levels, like CR, can be misclassified due to the flat surfaces that show low levels of backscattering of transmitted signals in L-band, consequently, they cannot be easily distinguished [
89]. In addition, some agricultural crops in more advanced stages of development can be easily distinguished from flat surfaces, as mature crops cause more volume scattering.
Some misclassifications that occurred in PP and WP classes can be explained by the presence of inajás (
Attalea maripa) and babaçus (
Attalea speciosa) [
35]. They are pioneer trees species sparsely distributed in both poorly and well managed pastures. The structure of these tree species may cause certain confusion during the classification process, mainly when associated with other forested classes.
Concerning classes that present a dense forest structure, the PF was the class that exhibited the highest classification results in 2015 and 2016 (
Figure 8 and
Figure 9), highlighting the great capability of YH decomposition to discriminate primary forest in rainforest environments. However, these attributes were not effective in discriminating SS3 class, especially the misclassification associated with the PA in both years. SS2 also showed intermediate values of PA and UA in both years. Misclassifications between SS2 and SS3 probably occurred due to similarities in their respective structural physiognomic characteristics and the high levels of regenerative development. SS1 also showed intermediate accuracies in both years. In this ecological successional stage, the forest class structure is composed predominantly of only one homogeneous superior stratum and one inferior stratum. The latter is composed of regeneration in its initial succession stages, which are arranged in an irregular form and dispersed in the environment, being influenced mainly by the distribution of pioneer species. Such condition probably was responsible for the misclassification to SS2 and PP classes.
DG classification results showed different behaviors in 2015 and 2016: PA and UA ≥ 53.8%, and F1-score of 0.47, in 2015; and, PA and UA > 90.0% and F1-score of 0.91 in 2016. This difference can be associated with the total number of samples: total of 81 pixels in 2015 and 693 pixels in 2016 (
Table 4). In addition, the degraded forest samples collected during the field campaign in 2015 correspond to forests severely affected by fires and by unsustainable logging, while the samples collected in 2016 corresponded mostly to forests severely affected by fires. In this context, the most homogeneous samples in the DG class were in 2016, which consequently may have contributed to reach better results, demonstrating the capability of the YH attributes to classify forest degradation, mostly degraded by fire (
Figure 12).
In general, the intermediate values of accuracy for SS2 and SS1, and the lowest values of accuracy obtained for SS3 in both years, demonstrate that the YH decomposition has a greater capacity to discriminate ecological succession classes from those with less vegetation coverage, despite its poor ability in distinguishing among classes of different ecological succession and disturbance stages, with the exception of PF class. Ullmann et al. [
90] examined the scattering characteristics from polarimetric data obtained at X-, C-, and L-band in a tundra-dominant ecosystem and found that L-band data were more appropriate to differentiate classes with low levels of vegetation cover. Pavanelli et al. [
39] integrated data from Landsat-8/OLI and ALOS/PALSAR-2 satellites and classified them in the RF algorithm, to map the LULC classes in a fragment located in the northern part of the Brazilian Amazon. They found that ALOS/PALSAR-2 data’s most important contribution was the possibility to discriminate classes with low levels of biomass (grasslands and wooded savanna).
According to the classification results from the optimum subset of attributes for 2015 (α, A, P
v, and VZ
d) and 2016 (H, A, P
s, and BMI) (
Table 5), these groups also showed a satisfactory capability to discriminate LULC classes. P
v and H attributes are more sensitive to volumetric scattering and are mainly associated with dense forest classes. Thus, both attributes can be associated with a higher scattering volume of SAR signals in more dense forest structures. Trisasongko [
91] used the CP decomposition to monitor disturbances in a dense tropical forest in Papua Tengah Province, Indonesia. This author associated high H values with intact forest classes, indicating a volume scattering mechanism’s dominance. Silva et al. [
92] modeled and estimated forest biomass from ALOS/PALSAR-1 polarimetric data in from the TNF and found a significant P
v attribute contribution in primary and secondary forests. According to the results obtained by Kuplich et al. [
93], P
v component also presented the highest contribution in the discrimination of LULC classes of the TNF and its surroundings. In relation to classes with dense forest coverage, a greater contribution of BMI index in SAR signal response is expected since this parameter is related to the quantity of AGB. This index tends to be more sensitive in areas of dense forests, which is the case of the TNF.
Concerning A and VZ
d attributes, Tanase et al. [
94] found a better correlation with severe burning levels in forests affected by fire in a Mediterranean region for C-band data acquired at steep incidences. Martins et al. [
38] and Plank et al. [
95] used full polarimetric data from ALOS/PALSAR, obtaining high A values in forested areas severely affected by fire because of an increase of canopy openings and a decrease of AGB.
The P
s attribute is more sensitive to surface scattering mechanism and can be associated with areas with less vegetation coverage or nonforest areas, such as bare soil and water bodies. Qi et al. [
96] observed that the dominance of surface scattering extracted from the FD decomposition, which helped distinguishing between forest and barren/sparsely vegetated classes. In the context of the present study, the P
s attribute can be associated mainly with bare soil and croplands, especially at the beginning of the crop growing cycle. The plants are starting to emerge above the surface; and consequently, there is a higher influence of soil surface in the backscattered SAR signals.
In this study, the YH polarimetric decomposition classified by the RF algorithm showed the best results in terms of OA and Kappa index for most of the thematic classes, including forest degradation class, in both years. In this context, Varghese et al. [
32] analyzed different polarimetric decompositions to classify forest canopy density, based on the full polarimetric RADARSAT-2 data classified by the SVM algorithm. YH decomposition also showed the best results, followed by VZ, FD, and CP decompositions. Classes with low levels of vegetation coverage, such as cropland and fallows, showed good classification results. In this context, both classes that also were analyzed in our study exhibited similar classification results (
Figure 8 and
Figure 9). Avtar et al. [
89] also used YH, FD, and CP polarimetric decompositions to monitor land cover in a tropical region of Cambodia, based on ALOS/PALSAR-1 full polarimetric data. The authors used the Maximum Likelihood algorithm to classify the decomposition attributes, obtaining the highest value of accuracy, in terms of OA and Kappa index, for the YH decomposition, followed by FD and CP decompositions. Both studies reported above corroborate the results found in our research.
However, some studies presented different classification results. Middinti et al. [
97] used ALOS/PALSAR full polarimetric data to classify different forest types from northeastern India. These authors extracted different attributes derived from backscatter coefficients and polarimetric decompositions. The results found for SVM (CP, FD, YH, and VZ decompositions) exhibited nearly similar classification accuracies, with intermediate values. Different to results presented by these authors, the classification results found in our study showed that YH and FD decompositions obtained high values of accuracy. Pôssa et al. [
35] also analyzed the different polarimetric decompositions in order to map LULC classes from TNF. CP decomposition showed the best classification results (OA of 77.37% and Kappa index of 0.70), followed by FD and YH decompositions. Mirelva and Nagasawa [
98] analyzed FD and YH decompositions, in addition to the backscatter intensities separately, and integrated them into ALOS/PALSAR-1 full polarimetric images for agriculture croplands classification in Indonesia. In their research, the FD and YH decompositions exhibited intermediate overall classification accuracy. Meanwhile, the best classification results were obtained in the integration of backscatter intensities (HH, HV, VH, VV, and HH+HV) and FD decomposition.
The classification results obtained by VZ, FD, and YH polarimetric decompositions and by the optimum subset demonstrated better classification performances in comparison with the classification based on backscatter coefficients, such as RVI and Pope index (
Table 5 and
Table 6). A performance investigation of the two advanced machine learning methods (RF and SVM), based on classification accuracy, demonstrated that both algorithms showed sensitivity to discriminate different LULC classes and forest degradation. RF presented the best computational efficiency (
Figure 7). The higher processing time in YH and optimal subset classifications for 2015 and 2016 may be related to the larger number of components (4), compared with the other groups (3 and 1).
Among the classes with different ecological successions (
Figure 12), the most noteworthy class observed from 2015 to 2016 was the DG class. Unusual relative low air humidity, high temperature, and shortage of rainfall events were reported by MODIS products [
50,
51]. A set of 91 heat points were detected inside the TNF and adjacent areas. In this context, forest degradation, deforestation, and expansion of agricultural and livestock frontiers often cause negative environmental impacts. In order to mitigate these negative impacts, we recommend an effective monitoring and surveillance program in the TNF region.
In this study, only full polarimetric data from ALOS/PALSAR-2 were explored. However, further studies are recommended adding full polarimetric SAR data obtained at different frequencies (currently available from the RADARSAT-2 and the experimental mode TERRASAR-X satellites). Deep learning methods are also recommended to extract more information from targets, thus improving the classification accuracy.
5. Conclusions
The polarimetric attributes extracted from ALOS/PALSAR-2 imagery and classified by RF and SVM algorithms exhibited a good ability to discriminate different types of tropical forests. In terms of OA, Kappa index, and processing time, the RF algorithm, in general, presented the best performance, when compared with that of SVM. YH decomposition, classified by the RF algorithm, presented the best performance concerning LULC and forest degradation classification. The thematic classes with less vegetation coverage were better discriminated than more dense forest structure classes, except for the PF. YH decomposition showed a strong capability of discrimination, with accuracies above 78.0%. However, in terms of processing time, the four components extracted from YH demanded the longest processing times.
The optimum subset of attributes for 2015 (α, A, Pv, and VZd) and for 2016 (H, A, Ps, and BMI) and FD and VZ decompositions also showed satisfactory classification results, indicating an adequate capability to discriminate different types of LULC present in rainforests. These polarimetric decompositions involving three attributes showed the shortest processing time performance compared with that of the optimum subgroup. The polarimetric decompositions showed a better performance than the backscatter coefficients represented by RVI and Pope index. During the period 2015–2016, there was a loss of forest classes due to forest degradation caused by fire events. In this context, the classification based on YH decomposition involving the ALOS/PALSAR-2 full polarimetric images demonstrated the capability to discriminate burned areas in rainforests. Furthermore, forest degradation by fire and the conversion of forest into pasture or cropland was also noticed, demanding initiatives to monitor LULC changes in such an important conservation unit. Future research employing deep learning methods, besides a synergistic approach involving other frequency SAR data, is recommended.
Further research for verifying the impact of the time difference between field data collection and SAR data acquisitions on the LULC changes are strongly recommended. Seasonality effects and temporal changes can induce some mislabeled samples in classification models, mainly when collected in land use classes with high temporal and spatial dynamics.