Classification of Vegetation over a Residual Megafan Landform in the Amazonian Lowland Based on Optical and SAR Imagery

Abstract

Abstract:The origin of large areas dominated by pristine open vegetation that is in sharp contrast with surrounding dense forest within the Amazonian lowland has generally been related to past arid climates, but this is still an issue open for debate. In this paper, we characterize a large open vegetation patch over a residual megafan located in the northern Amazonia. The main goal was to investigate the relationship between this paleolandform and vegetation classes mapped based on the integration of optical and SAR data using the decision tree. Our remote sensing dataset includes PALSAR and TM/Landsat images. Five classes were identified: rainforest; flooded forest; wooded open vegetation; grassy-shrubby open vegetation; and water body. The output map resulting from the integration of PALSAR and TM/Landsat images showed an overall accuracy of 94%. Narrow, elongated and sinuous belts of forest within the open vegetation areas progressively bifurcate into others revealing paleochannels arranged into distributary pattern. Such characteristics, integrated with pre-existing geological information, led us to propose that the distribution of vegetation classes highlight a morphology attributed to a Quaternary megafan developed previous to the modern fluvial tributary system. The characterization of such megafan is important for reconstructing landscape changes associated with the evolution of the Amazon drainage basin.

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

“…Furthermore, objects enable the consideration of semantic or spatial contextual relationships, which helps to cope with heterogeneous feature such as TFV [6,11]. Several segmentation techniques, which group pixels into meaningful or perceptual regions (objects) for the extraction of TFV, can be found in the literature, including multiresolution segmentation [24,41], Mallat's discrete wavelet transform [42], Markov Random Fields [14,43], and clustering approaches [10,11]. The combination of segmentation techniques with SAR time-series data can be used to discover multi-temporal characteristics and patterns, allowing for the extraction of useful information from enormous and complex data sets [44] and empowering the derivation of time series features with the advantages of an object-based approach.…”

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

“…They used ten sets of ALOS/PALSAR data over four years to examine temporal variations and found that the functional relation of aboveground biomass and backscatter were dependent on precipitation. Moreover, Cremon et al [39] employed a decision tree classifier to map forest and open vegetation over a residual megafan located in a wetland area in the Amazonian lowland based on the integration of optical Landsat TM and ALOS/PALSAR SAR data, and indicated that the vegetation distribution highlights a morphology attributed to a previously developed Quaternary megafan. At a global level, Shimada et al [182] generated forest/non-forest maps based on annual mosaics derived from ALOS/PALSAR HH and HV polarization data at a 25-m spatial resolution for the years 2007 to 2010.…”

“…For example, L-band data have been applied and investigated for the following topics: the detection and mapping of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Vegetation-related research is the most-often published subject; a lot of work has been done in the field of forestry [38][39][40][41] and wetland research [42][43][44][45][46][47][48]. In the context of coastal zone-related studies, the potential of L-band SAR data has been demonstrated for the mapping and monitoring of flooded vegetation [25,49,50], wetland extents and wetland inundation [26,[51][52][53][54][55][56] and for the assessment of mangrove forests [47,57,58].…”

Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

“…Regarding the combined use of different sensors for vegetation monitoring, the studies can be divided into two types: one uses the heterogeneous data from different sensors such as LiDAR with optical sensors [8][9][10][11], synthetic aperture radar (SAR) with optical sensors [12,13] and high geometrical resolution images with hyper/multi spectral images [14]; the other category is through the synthetic use of similar sensors from different platforms, such as TLS with ALS [15], airborne with space-borne and so on.…”