Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy

Abstract

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.

Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning

“…Higher NDVI threshold has been used in some studies [35], but we selected 0.5 threshold as even the brightest pixels of some of the species in the study area showed lower NDVI values (e.g., Euphorbia kibwezensis).…”

Time Series of Images to Improve Tree Species Classification

“…The identification of species and successional stage of forests helps to monitor this type of vegetation and Remote Sensing measurements appear as a promising alternative, mainly with imaging spectroscopy using hyperspectral sensors (Féret and Asner, 2013;Baldeck et al, 2015;Näsi et al, 2016;Nevalainen et al, 2017).…”

Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy

“…As an alternative, partial supervised methods (single-class classification approaches) may provide an effective means to overcome such challenges because they require collection of training data (endmembers) solely from the focal class. These methods are designed to increase detection performance of a single focal class, while reducing the requirement for training data collection, often a necessity in multi-class classification methods [22,23]. For example, Mixture Tuned Matched Filtering (MTMF) focuses on identifying the presence of a single focal class and only requires that training data be collected from the focal class.…”

“…Additionally, the Biased Support Vector Machine (BSVM) [30] is another single-class classification approach that has been successfully used to identify focal plant species in the upper canopy, including in tropical forests [23,31]. BSVM has the same general architecture as the binary SVM, but the labeled training data used in BSVM come only from the focal class (detailed description available in [31]).…”