Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data

Jha, Chandra Shekhar; Rakesh,; Singhal, Jayant; Reddy, C. Sudhakar; Rajashekar, G.; Maity, Swapan Kumar; Patnaik, C.; Das, Anup; Misra, Arundhati; Singh, C. P.; Mohapatra, Jakesh; Krishnayya, N. S. R.; Kiran, Sandhya; Townsend, Phil; Martinez, Margarita Huesca

doi:10.18520/cs/v116/i7/1124-1135

Cited by 24 publications

(12 citation statements)

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“…In recent studies, the use of canopy spectroscopy, such as hyperspectral images from AVIRIS and the Hyperspectral Infrared Imager (HyspIRI) and Surface Biology and Geology (SBG) missions, to predict vegetation composition, structure, and function has been shown to be effective in a variety of biomes [13][14][15][16][17][18]. However, similar work remains technically challenging in the arctic tundra as a result of low data availability and high spatial heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…The canopy properties of different vegetation types, including spectral reflectance, temperature, and structure, can strongly affect energy and water exchange through the atmosphere-plant-soil continuum [11,12], and are therefore important for understanding the consequences of rapid climate change. For example, spectral reflectance that describes the light-absorbing and -scattering properties of plant leaves and canopies has shown great effectiveness for retrieving vegetation parameters, such as albedo, leaf pigments, macronutrients, as well as vegetation fractional covers [13][14][15][16][17][18], that are important to inform Earth system models (ESMs). Similarly, vegetation structural details (e.g., canopy height) are essential for monitoring above-ground biomass and primary productivity [19][20][21] while canopy temperature can help detect plant water stress associated with hydrological events [22,23].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

et al. 2020

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Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

et al. 2020

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“…Even using a binary classification of 'healthy' or 'degraded' trees, many environmental factors in natural ecosystems may have adversely affected our 'healthy' reference trees. While methods do exist to collect field-based spectral reflectance data, which could provide a more direct comparison to UAS remotely sensed image features, these methods elicit considerable time and resources for large study areas; especially in complex, mixed-species forests [122][123][124][125]. Another source of uncertainty in this study was the reliance on the Parrot Sequoia+ mul- Despite the successes that this research and similar studies have found in the application of UAS for fine scale forest health monitoring, there are several sources of uncertainty that should be further explored.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models

Fraser

Congalton

2021

Remote Sensing

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Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services and management costs. To understand the patterns and severity of these stressors across complex landscapes, there must be an increase in reliable data at scales compatible with management actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate the capability of UAS multispectral imagery and freely available National Agricultural Imagery Program (NAIP) imagery for differentiating coniferous healthy, coniferous stressed, deciduous healthy, deciduous stressed, and degraded individual trees throughout a complex, mixed-species forests. These methods are first compared to assessments of crown vigor in the field, to evaluate the potential in supplementing this resource intensive practice. This investigation uses the random forest and support vector machine (SVM) machine learning algorithms to classify the imagery into the five forest health classes. Using the random forest classifier, the UAS imagery correctly classified five forest Health classes with an overall accuracy of 65.43%. Using similar methods, the high-resolution airborne NAIP imagery achieved an overall accuracy of 50.50% for the five health classes, a reduction of 14.93%. When these classes were generalized to healthy, stressed, and degraded trees, the accuracy improved to 71.19%, using UAS imagery, and 70.62%, using airborne imagery. Further analysis into the precise calibration of UAS multispectral imagery, a refinement of image segmentation methods, and the fusion of these data with more widely distributed remotely sensed imagery would further enhance the potential of these methods to more effectively and efficiently collect forest health information from the UAS instead of using field methods.

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“…Like in the Neotropics, shade coffee farms in East Africa exist on a gradient from small, heterogeneous farms that can support a high diversity of shade trees (Buechley et al., 2015), to large‐scale sun coffee plantations with few to no shade trees (Moguel & Toledo, 1999). Large partially shaded monocultures are common and marked by a low density and low diversity of shade trees, with the community primarily consisting of Grevillea robusta , Cordia africana, and Albizia sp ., as they grow rapidly, are leguminous and thus fix nitrogen, and are relatively easy to maintain (Jha et al., 2019; Liebig et al., 2016; Rahn, et al., 2018, Schooler unpublished data). While the presence of certification schemes (e.g., Bird Friendly ® Rainforest Alliance ® ) is less conspicuous in East Africa than in the Neotropics, the use of shade trees on coffee farms in East Africa is increasing from education efforts (Coffee Research Institute, 2019; Liebig et al., 2016, Schooler, unpublished data).…”

Section: Introductionmentioning

confidence: 99%

Shade trees preserve avian insectivore biodiversity on coffee farms in a warming climate

Schooler

Johnson

Njoroge³

et al. 2020

Ecology and Evolution

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Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data

Cited by 24 publications

References 0 publications

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models

Shade trees preserve avian insectivore biodiversity on coffee farms in a warming climate

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