Abstract
The use of light detection and ranging (LiDAR) techniques for recording and analyzing tree and forest structural variables shows strong promise for improving established hyperspectral-based tree species classifications; however, previous multi-sensoral projects were often limited by error resulting from seasonal or flight path differences. The National Aeronautics and Space Administration (NASA) Goddard’s LiDAR, hyperspectral, and thermal imager (G-LiHT) is now providing co-registered data on experimental forests in the United States, which are associated with established ground truths from existing forest plots. Free, user-friendly machine learning applications like the Orange Data Mining Extension for Python recently simplified the process of combining datasets, handling variable redundancy and noise, and reducing dimensionality in remotely sensed datasets. Neural networks, CN2 rules, and support vector machine methods are used here to achieve a final classification accuracy of 67% for dominant tree species in experimental plots of Howland Experimental Forest, a mixed coniferous–deciduous forest with ten dominant tree species, and 59% for plots in Penobscot Experimental Forest, a mixed coniferous–deciduous forest with 15 dominant tree species. These accuracies are higher than those produced using LiDAR or hyperspectral datasets separately, suggesting that combined spectral and structural data have a greater richness of complementary information than either dataset alone. Using greatly simplified datasets created by our dimensionality reduction methodology, machine learner performance remains comparable or higher to that using the full dataset. Across forests, the identification of shared structural and spectral variables suggests that this methodology can successfully identify parameters with high explanatory power for differentiating among tree species, and opens the possibility of addressing large-scale forestry questions using optimized remote sensing workflows.
Highlights
The use of geographic information systems (GIS) and remote sensing techniques for forestry applications has been a major concern in the field of geography since its creation, and a technical revolution in the last three decades allowed for increasingly sophisticated analysis of forest structure, composition, and dynamics
An initial assessment of species-specific structure shows that individual tree diameter at breast height (DBH) and height measurements in Howland and Penobscot Experimental Forests vary in absolute magnitude and in degree of within-species variability (Figure 2)
Teehne amgegarneginagtifounllyofstuhmismbiaormizeetdry[9d3a]t.a Abyltshuobupglhoti-nleivtieall setxemplocroautinotnisojfusatgognreegoaftisoenvemraeltwhoadyss rinevweahleicdhthdaattathceomuladjohriatyveofbseuenbpmloetsanwinogufludllbye sausmsigmnaerdiztehde [s9a3m].eAdlothmoiungahnt isnpiteicailesexrepgloarradtlieosns ooff aggregation methods revealed that the majority of subplots would be assigned the same dominant species regardless of method, this choice necessarily affects the exact classification accuracies www.mdpi.com/journal/remotesensing method, this choice necessarily affects the exact classification accuracies achieved in this analysis
Summary
The use of geographic information systems (GIS) and remote sensing techniques for forestry applications has been a major concern in the field of geography since its creation, and a technical revolution in the last three decades allowed for increasingly sophisticated analysis of forest structure, composition, and dynamics. Multispectral, and hyperspectral remote-sensing techniques are traditionally used to gather data on forests, the incorporation of data on tree and canopy structure can improve analysis of forest biomass and health, carbon sequestration potential, and range, potentially even at the species level [1]. Light detection and ranging (LiDAR) technologies are increasingly being employed to collect data on structural features of tree canopies and branching patterns, forest structure and succession, and even estimates of tree physiological metrics such as leaf area index [2]. Similar structural indices were used with success when surveying vegetation biodiversity [13], and it is, possible to take advantage of high-density LiDAR data to examine branching patterns and other architectural data on a single-tree or tree-stand basis, and to relate this to the species of individual trees or of the predominant species in a stand
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
