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

Baldeck CA, Asner GP, Martin RE, Anderson CB, Knapp DE, Kellner JR, Wright SJ - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States of America.

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.

No MeSH data available.


Related in: MedlinePlus

Prediction results for the three focal species across BCI.Blue = D. panamensis, red = H. guayacan, and yellow = J. copaia. Insets show close-up of results for high-density areas of each focal species.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4496029&req=5

pone.0118403.g006: Prediction results for the three focal species across BCI.Blue = D. panamensis, red = H. guayacan, and yellow = J. copaia. Insets show close-up of results for high-density areas of each focal species.

Mentions: The contextual filters reduced the number of pixels assigned to the focal species classes by 12.8%, 26.4%, and 23.1% for D. panamensis, H. guayacan, and J. copaia, respectively. The number of objects (contiguous areas) assigned to the three species was reduced much more drastically by 90.4%, 88.1%, and 85.1%, respectively (Fig 5C). This occurred because the filters removed a large quantity of small objects, often only one pixel in size. After filtering, there were a total of 2,107 predicted crown objects of D. panamensis, 837 of H. guayacan, and 1,405 of J. copaia across the entire island (Fig 6).


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

Baldeck CA, Asner GP, Martin RE, Anderson CB, Knapp DE, Kellner JR, Wright SJ - PLoS ONE (2015)

Prediction results for the three focal species across BCI.Blue = D. panamensis, red = H. guayacan, and yellow = J. copaia. Insets show close-up of results for high-density areas of each focal species.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496029&req=5

pone.0118403.g006: Prediction results for the three focal species across BCI.Blue = D. panamensis, red = H. guayacan, and yellow = J. copaia. Insets show close-up of results for high-density areas of each focal species.
Mentions: The contextual filters reduced the number of pixels assigned to the focal species classes by 12.8%, 26.4%, and 23.1% for D. panamensis, H. guayacan, and J. copaia, respectively. The number of objects (contiguous areas) assigned to the three species was reduced much more drastically by 90.4%, 88.1%, and 85.1%, respectively (Fig 5C). This occurred because the filters removed a large quantity of small objects, often only one pixel in size. After filtering, there were a total of 2,107 predicted crown objects of D. panamensis, 837 of H. guayacan, and 1,405 of J. copaia across the entire island (Fig 6).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States of America.

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.

No MeSH data available.


Related in: MedlinePlus