Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

Ma, Xuanlong; Migliavacca, Mirco; Wirth, Christian; Bohn, Friedrich J.; Huth, Andreas; Richter, Ronny; Mahecha, Miguel D.

doi:10.3390/rs12081248

Cited by 25 publications

(18 citation statements)

References 65 publications

(78 reference statements)

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“…Active remote sensing technologies such as Sentinel‐2 satellites, synthetic active radar (SAR) and light detection and ranging (LiDAR) instruments help to monitor plant traits at very high resolutions (e.g. 10 m) and spatial extents (Durán et al 2019, Ma et al 2020). For example, Durán et al (2019) used both imaging spectroscopy and foliar traits to estimate functional diversity in tropical forests across a large elevation gradient (215–3537 m) in South America.…”

Section: Towards An Understanding Of Functional Diversity and Species...mentioning

confidence: 99%

Using multi‐scale spatially explicit frameworks to understand the relationship between functional diversity and species richness

et al. 2022

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Understanding how ecosystem functioning is impacted by global change drivers is a central topic in ecology and conservation science. We need to assess not only how environmental change affects species richness, but also how the distribution of functional traits (i.e. functional diversity) mediate the relationship between species richness and ecosystem functioning. However, most evidence about the capacity of functional diversity to explain ecosystem functioning has been developed from studies conducted at a single spatial scale. Here, we explore theory, expectations and evidence for why and how species richness and functional diversity relationships vary with spatial scale. Despite the importance of accounting for spatial processes at multiple scales, we show that most studies of the species richness–functional diversity relationship focus on single scale analyses that ignore spatial context. Thus, we discuss the need to establish a spatially explicit, multi‐scale framework for understanding the relationship between species richness and functional diversity. As a starting point to developing such a framework, we detail some expected trajectories and mechanisms by which the diversity of species and functional traits may change across increasing spatial scales. We also explore what is known about two important gaps in the literature about this relationship: 1) the influence of spatial autocorrelation on community assembly processes and 2) the variation in the structure of species interactions across spatial extents. We present some key challenges that could be addressed by integrating approaches from community and landscape ecology. This information will help improve our understanding of the relative influence of local and large‐scale processes on community structure, while providing a foundation for improving biodiversity monitoring, policy and ecosystem function based conservation.

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Section: Towards An Understanding Of Functional Diversity and Species...mentioning

confidence: 99%

Using multi‐scale spatially explicit frameworks to understand the relationship between functional diversity and species richness

et al. 2022

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“…Shifts in the content of photosynthetic pigments are related to the long-term variability of plants. It is known that the composition of photosynthetic pigments is very variable for different species [150,161]; this composition changes regarding the individual development of the plant [162][163][164], and it can be strongly dependent on local environmental conditions [161]. Particularly, the mineral nutrition is an important factor influencing concentrations of pigments in plants; e.g., changes in the nitrogen nutrition can influence concentrations of photosynthetic pigments and the content of RUBISCO, which is the key photosynthetic enzyme [7,[165][166][167][168].…”

Section: Variability Of Plantsmentioning

confidence: 99%

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

2021

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Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

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“…However, forest sites where crown shapes, in particular, (and the amount of leaf area per crown volume) differ more between tree species will be more difficult to analyze and will require a closer look. In this case, our method could be extended toward a linear combination of multiple leaf-tree matrices (one for each dominant tree species) if information on the species composition is available (e.g., by optical sensors) [55,56]. A recent study [31] demonstrated how stem diameter distributions of different plant functional types can be derived based on site-specific calibration.…”

Section: Strengths and Limitations Of The Presented Approachmentioning

confidence: 99%

Deriving Tree Size Distributions of Tropical Forests from Lidar

et al. 2021

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Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

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Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

Cited by 25 publications

References 65 publications

Using multi‐scale spatially explicit frameworks to understand the relationship between functional diversity and species richness

Using multi‐scale spatially explicit frameworks to understand the relationship between functional diversity and species richness

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Deriving Tree Size Distributions of Tropical Forests from Lidar

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