Dataset Information

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.

SUBMITTER: Baldeck CA

PROVIDER: S-EPMC4496029 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Baldeck Claire A CA Asner Gregory P GP Martin Robin E RE Anderson Christopher B CB Knapp David E DE Kellner James R JR Wright S Joseph SJ

PloS one 20150708 7

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 B ...[more]

PMID: 26153693

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Project description:BackgroundBiogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could overcome this challenge for vegetation mapping. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. In this paper, I describe a trait-based approach to species identification with imaging spectroscopy, the Center for Conservation Biology species identification (CCB-ID) method, which was developed as part of an ecological data science evaluation competition.MethodsThese methods were developed using airborne imaging spectroscopy data from the National Ecological Observatory Network (NEON). CCB-ID classified tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method inspired by botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis (PCA) and resampled to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale.ResultsCCB-ID received a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the competition test data. Accuracy and specificity scores were high for all species, but precision and recall scores varied for rare species. PCA transformation improved accuracy scores compared to models trained using reflectance data, but outlier removal and data resampling exacerbated class imbalance problems.DiscussionCCB-ID accurately classified tree species using NEON data, reporting the best scores among participants. However, it failed to overcome several species mapping challenges like precisely identifying rare species. Key takeaways include (1) selecting models using metrics beyond accuracy (e.g., recall) could improve rare species predictions, (2) within-genus trait variation may drive spectral separability, precluding efforts to distinguish between functionally convergent species, (3) outlier removal and data resampling can exacerbate class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) targeted feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support species mapping efforts.

Project description:The extent to which interspecific niche differences structure plant communities is highly debated, with extreme viewpoints ranging from fine-scaled niche partitioning, where every species in the community is specialized to a distinct niche, to neutrality, where species have no niche or fitness differences. However, there exists a default position wherein niches of species in a community are determined by their evolutionary and biogeographic histories, irrespective of other species within the community. According to this viewpoint, a broad range of pair-wise niche overlaps-from completely overlapping to completely distinct-are expected in any community without the need to invoke interspecific interactions. We develop a method that can test for both habitat associations and niche differences along an arbitrary number of spatial and temporal niche dimensions and apply it to a 24-yr data set of the eight dominant woody-plant species (representing 84% and 76% of total community abundance and basal area, respectively) from a 50-ha permanent plot in a southern Indian tropical dry forest, using edaphic, topographic, and precipitation variables as niche axes. Species separated into two broad groups in niche space-one consisting of three canopy species and the other of a canopy species and four understory species-along axes that corresponded mainly to variation in soil P, Al and a topographic index of wetness. Species within groups tended to have significantly greater niche overlap than expected by chance. Community-wide niche overlap in spatial and temporal niche axes was never smaller than expected by chance. Species-habitat associations were neither necessary nor sufficient preconditions for niche differences to be present. Our results suggest that this tropical dry-forest community consists of several tree species with broadly overlapping niches, and where significant niche differences do exist, they are not readily interpretable as evidence for niche differentiation. We argue, based on a survey of the literature, that many of the observed niche differences in tropical forests are more parsimoniously viewed as autecological differences between species that exist independently of interspecific interactions.

Project description:Rodents affect the post-dispersal fate of seeds by acting either as on-site seed predators or as secondary dispersers when they scatter-hoard seeds. The tropical forests of north-east India harbour a high diversity of little-studied terrestrial murid and hystricid rodents. We examined the role played by these rodents in determining the seed fates of tropical evergreen tree species in a forest site in north-east India. We selected ten tree species (3 mammal-dispersed and 7 bird-dispersed) that varied in seed size and followed the fates of 10,777 tagged seeds. We used camera traps to determine the identity of rodent visitors, visitation rates and their seed-handling behavior. Seeds of all tree species were handled by at least one rodent taxon. Overall rates of seed removal (44.5%) were much higher than direct on-site seed predation (9.9%), but seed-handling behavior differed between the terrestrial rodent groups: two species of murid rodents removed and cached seeds, and two species of porcupines were on-site seed predators. In addition, a true cricket, Brachytrupes sp., cached seeds of three species underground. We found 309 caches formed by the rodents and the cricket; most were single-seeded (79%) and seeds were moved up to 19 m. Over 40% of seeds were re-cached from primary cache locations, while about 12% germinated in the primary caches. Seed removal rates varied widely amongst tree species, from 3% in Beilschmiedia assamica to 97% in Actinodaphne obovata. Seed predation was observed in nine species. Chisocheton cumingianus (57%) and Prunus ceylanica (25%) had moderate levels of seed predation while the remaining species had less than 10% seed predation. We hypothesized that seed traits that provide information on resource quantity would influence rodent choice of a seed, while traits that determine resource accessibility would influence whether seeds are removed or eaten. Removal rates significantly decreased (p < 0.001) while predation rates increased (p = 0.06) with seed size. Removal rates were significantly lower for soft seeds (p = 0.002), whereas predation rates were significantly higher on soft seeds (p = 0.01). Our results show that murid rodents play a very important role in affecting the seed fates of tropical trees in the Eastern Himalayas. We also found that the different rodent groups differed in their seed handling behavior and responses to changes in seed characteristics.

Dataset Information

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

Publications

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

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