scholarly journals Performance and Feasibility of Drone-Mounted Imaging Spectroscopy for Invasive Aquatic Vegetation Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 582
Author(s):  
Erik A. Bolch ◽  
Erin L. Hestir ◽  
Shruti Khanna
Keyword(s):  
Aquatic Plants ◽  
Native Species ◽  
Aquatic Vegetation ◽  
Imaging Spectroscopy ◽  
Aerial Vehicles ◽  
San Joaquin River ◽  
Random Forest Models ◽  
Invasive Aquatic Plants ◽  
Airborne Imaging ◽  
Species Mapping

Invasive plants are non-native species that can spread rapidly, leading to detrimental economic, ecological, or environmental impact. In aquatic systems such as the Sacramento-San Joaquin River Delta in California, USA, management agencies use manned aerial vehicles (MAV) imaging spectroscopy missions to map and track annual changes in invasive aquatic plants. Advances in unmanned aerial vehicles (UAV) and sensor miniaturization are enabling higher spatial resolution species mapping, which is promising for early detection of invasions before they spread over larger areas. This study compared maps made from UAV-based imaging spectroscopy with the manned airborne imaging spectroscopy-derived maps that are currently produced for monitoring invasive aquatic plants in the Sacramento-San Joaquin Delta. Concurrent imagery was collected using the MAV mounted HyMap sensor and the UAV mounted Nano-Hyperspec at a wetland study site and classification maps generated using random forest models were compared. Classification accuracies were comparable between the Nano- and HyMap-derived maps, with the Nano-derived map having a slightly higher overall accuracy. Additionally, the higher resolution of the Nano imagery allowed detection of patches of water hyacinth present in the study site that the HyMap could not. However, it would not be feasible to operate the Nano as a replacement to HyMap at scale despite its improved detection capabilities due to the high costs associated with overcoming area coverage limitations. Overall, UAV-based imaging spectroscopy provides comparable or improved capability, and we suggest it could be used to supplement existing monitoring programs by focusing on target areas of high ecologic or economic priority.

scholarly journals The CCB-ID approach to tree species mapping with airborne imaging spectroscopy

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5666 ◽  
Author(s):  
Christopher B. Anderson
Keyword(s):  
Species Identification ◽  
Tree Species ◽  
Rare Species ◽  
Imaging Spectroscopy ◽  
Class Imbalance ◽  
Accuracy Score ◽  
Ecological Data ◽  
Outlier Removal ◽  
Airborne Imaging ◽  
Species Mapping

Background Biogeographers 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. Methods These 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. Results CCB-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. Discussion CCB-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.

scholarly journals The CCB-ID approach to tree species mapping with airborne imaging spectroscopy

2018 ◽  
Author(s):  
Christopher B Anderson
Keyword(s):  
Tree Species ◽  
Rare Species ◽  
Imaging Spectroscopy ◽  
Class Imbalance ◽  
Accuracy Score ◽  
Spectroscopy Data ◽  
Ecological Data ◽  
Outlier Removal ◽  
Airborne Imaging ◽  
Species Mapping

Background. Biogeographers 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 obviate this challenge. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. Plant traits are often highly conserved within species, and highly variable between species, which provides the biophysical basis for species mapping. In this paper I describe a trait-based approach to species identification with imaging spectroscopy, CCB-ID, which was developed as part of a NIST-sponsored ecological data science evaluation (ECODSE). Methods. These methods were developed using NEON airborne imaging spectroscopy data. CCB-ID classifies tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method traditionally used in botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis and resampled by species to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were then calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale. Results. This approach performed well according to the competition metrics, receiving a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the test data. Accuracy and specificity scores were high for all species, but precision and recall scores were variable 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. Discussion. CCB-ID accurately classified tree species using NEON imaging spectroscopy data, reporting the best classification scores among participants. However, it failed to overcome several well-known species mapping challenges, like precisely identifying rare species. Key takeaways include (1) training models to maximize 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 exacerbated class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support future species mapping efforts.

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

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0118403 ◽  
Author(s):  
Claire A. Baldeck ◽  
Gregory P. Asner ◽  
Robin E. Martin ◽  
Christopher B. Anderson ◽  
David E. Knapp ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Tree Species ◽  
Imaging Spectroscopy ◽  
Airborne Imaging ◽  
Species Mapping

scholarly journals The CCB-ID approach to tree species mapping with airborne imaging spectroscopy

2018 ◽  
Author(s):  
Christopher B Anderson
Keyword(s):  
Tree Species ◽  
Rare Species ◽  
Imaging Spectroscopy ◽  
Class Imbalance ◽  
Accuracy Score ◽  
Spectroscopy Data ◽  
Ecological Data ◽  
Outlier Removal ◽  
Airborne Imaging ◽  
Species Mapping

Background. Biogeographers 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 obviate this challenge. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. Plant traits are often highly conserved within species, and highly variable between species, which provides the biophysical basis for species mapping. In this paper I describe a trait-based approach to species identification with imaging spectroscopy, CCB-ID, which was developed as part of a NIST-sponsored ecological data science evaluation (ECODSE). Methods. These methods were developed using NEON airborne imaging spectroscopy data. CCB-ID classifies tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method traditionally used in botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis and resampled by species to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were then calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale. Results. This approach performed well according to the competition metrics, receiving a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the test data. Accuracy and specificity scores were high for all species, but precision and recall scores were variable 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. Discussion. CCB-ID accurately classified tree species using NEON imaging spectroscopy data, reporting the best classification scores among participants. However, it failed to overcome several well-known species mapping challenges, like precisely identifying rare species. Key takeaways include (1) training models to maximize 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 exacerbated class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support future species mapping efforts.

FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines

2021 ◽  
Author(s):  
Natalie Queally ◽  
Zhiwei Ye ◽  
Ting Zheng ◽  
Adam Chlus ◽  
Fabian Schneider ◽  
...  
Keyword(s):  
Imaging Spectroscopy ◽  
Airborne Imaging
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