Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

Abstract

Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the performance of Sentinel-2 and Landsat-8 data for discriminating different successional forest stages of a patch located in a subtropical portion of the Atlantic Rain Forest in Southern Brazil with the aid of two machine learning algorithms and relying on the use of spectral reflectance data selected over two seasons and attributes thereof derived. Random Forest (RF) and Support Vector Machine (SVM) were used as classifiers with different subsets of predictor variables (multitemporal spectral reflectance, textural metrics and vegetation indices). All the experiments reached satisfactory results, with Kappa indices varying between 0.9, with Landsat-8 spectral reflectance alone and the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance alone also associated with the SVM algorithm. The Landsat-8 data had a significant increase in accuracy with the inclusion of other predictor variables in the classification process besides the pure spectral reflectance bands. The classification methods SVM and RF had similar performances in general. As to the RF method, the texture mean of the red-edge and SWIR bands were considered the most important ranked attributes for the classification of Sentinel-2 data, while attributes resulting from multitemporal bands, textural metrics of SWIR bands and vegetation indices were the most important ones in the Landsat-8 data classification.

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

“…The time-series Landsat-8 and Sentinel-2A data, covering the crucial growing and leaf-off seasons of the forest, could provide phenological information to improve the discrimination between different forest types. This result is in good agreement with the findings of [19,50], who summarized that time-series images can obtain much higher accuracy of mapping tropical forest types. Macander et al [53] have also used seasonal composite Landsat imagery and the random forest method for plant type cover mapping and found that multi-temporal imagery improved cover classification.…”

“…Seen from the experimental results of forest type identification by using only Landsat-8 and Sentinel-2A imagery, we concluded that Sentinel-2A images can achieve a higher overall accuracy due to its high spatial resolution (Tables 9 and 10). This result is the same as [50], who concluded that the classification using Sentinel-2 images performed better than Landsat-8 data.…”

“…Topographic information derived from the DEM can better offer different geomorphologic characteristics. These characteristics reflect the habitats of different forest types and can help to identify forest types, similar to the studies established by [13,50]. Seen from the experimental results of forest type identification by using only Landsat-8 and Sentinel-2A imagery, we concluded that Sentinel-2A images can achieve a higher overall accuracy due to its high spatial resolution (Tables 9 and 10).…”

“…The object-based classifier can provide faster and better results and can be easily applied to classify forest types [24,39,40,55]. In addition, this classification method has the ability to handle predictor variables with a multimodal distribution well due to the high variability in time and space [50,56]; especially, no sophisticated parameter tuning is required. Thus, it could obtain a higher overall accuracy.…”

“…This is because there are three red-edge bands and two near-infrared bands for Sentinel-2A imagery, which are conducive to forest type identification based on the spectral feature curve in Figure 3. In addition, the spatial resolution of Landsat-8 imagery is lower than the Sentinel-2A imagery, and the larger pixels imply a greater spectral mixture of classes [50]. Therefore, in the following experiment, we used Sentinel-2A data as the basis, then gradually added the multi-temporal Landsat-8, DEM and Sentinel-1 imagery for a further comparative analysis.…”