Modeling plant composition as community continua in a forest landscape with <scp>L</scp>i<scp>DAR</scp> and hyperspectral remote sensing

Hakkenberg, Christopher R.; Peet, Robert K.; Urban, Dean L.; Song, Conghe

doi:10.1002/eap.1638

Cited by 39 publications

(53 citation statements)

References 74 publications

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“…It may also be possible to use remote sensing approaches to initialize spatial PFT transitions across landscapes (Hakkenberg et al. ). Incorporating PFT shifts into models that employ mechanistic, target‐driven initialization may further improve biogeochemical projections in disturbance‐prone watersheds.…”

Section: Discussionmentioning

confidence: 99%

“…Several ecosystem biogeochemical models have addressed this limitation by incorporating submodels for temporal shifts in plant functional type (PFT; e.g., Moorcroft et al 2001, Bond-Lamberty et al 2005, Keane et al 2011). It may also be possible to use remote sensing approaches to initialize spatial PFT transitions across landscapes (Hakkenberg et al 2018). Incorporating PFT shifts into models that employ mechanistic, target-driven initialization may further improve biogeochemical projections in disturbance-prone watersheds.…”

Section: Future Workmentioning

confidence: 99%

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Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up

Hanan

Tague

Choate

et al. 2018

Ecological Applications

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Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.

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Section: Discussionmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up

Hanan

Tague

Choate

et al. 2018

Ecological Applications

View full text Add to dashboard Cite

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“…Other approaches used multispectral data to propose a 2D-based analysis for disease detection [30] and segmentation of vegetation areas [31]. In addition, recent contributions are provided by the fusion of LiDAR and hyperspectral remotely sensed data [32]. Regarding the novelty of our methodology to previous works, we propose the generation of a reflectance map for each multispectral image to project all pixel values on the point cloud.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

et al. 2020

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3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.

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“…The second challenge is in the spectral heterogeneity of these complex ecosystems over a large spatial extent. The spectral variability of vegetation is mainly a function of canopy structure (e.g., height and leaf area) and biochemistry, such as chlorophyll content [14][15][16][17][18]. For example, different tree and shrub species might have similar spectral signatures due to complex interactions between canopy structure, canopy biochemistry, and light [19,20].…”

Section: Introductionmentioning

confidence: 99%

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

et al. 2019

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The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems.

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Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing

Cited by 39 publications

References 74 publications

Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up

Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

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