Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species

Abstract

Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. This study produces highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (~1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. Combining multi-temporal vegetation indices and canopy height models led to an overall accuracy of 95.28% and kappa of 94.17%. Five woody species were discriminated resulting in producer accuracies ranging from 86.12% to 98.38%. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data.

An Overview of the Special Issue “Remote Sensing Applications in Vegetation Classification”

“…The published manuscripts covered a wide range of topics regarding vegetation classification using remote sensing techniques. In 14 research papers, the classifications were performed to identify different types of vegetation [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Different types of crops were analysed most frequently [2][3][4]6,6,9,12].…”

“…Different types of crops were analysed most frequently [2][3][4]6,6,9,12]. In three cases, the authors recognized different types of grasslands [8,11,14], whereas trees were the object of the studies in two papers [1,10]. Additionally, there were also literature review and technical note, both concerning different perspective of vegetation identification [15,16].…”

“…Generally, it can be stated that most of the methods use optical data [2][3][4][5][6]6,[8][9][10][11]14]; however, one employs radar data [12]. In nine cases, free Copernicus data Sentinel-1, Sentinel-2 or both were analysed [2][3][4][5][6]8,9,12,13].…”

“…To accurately map more heterogeneous vegetation, different data fusion platforms were used [1,7,10,11,14]. Feng et al [1] utilized Sentinel-2, shuttle radar topography mission (SRTM) and light detection and ranging (LiDAR) data for the classification of bamboo forests covering a large spatial range in the south-eastern hilly region of China.…”

“…A framework for producing a species-specific woody vegetation map in Arizona, USA, including five of the most abundant woody species in a large semi-arid region, was provided in [10] by utilizing a fusion of simultaneously acquired airborne LiDAR and high spatial resolution hyperspectral data to improve classification accuracies. The authors used three classifiers, RF, SVM, a classification and regression tree (CART) and multitemporal datasets, and they found an evident influence of fusing spectral and structural information in a RF classifier for tree identification.…”