scholarly journals Airborne and Spaceborne Lidar Reveal Trends and Patterns of Functional Diversity in a Semi-Arid Ecosystem

2021 ◽  
Vol 2 ◽  
Author(s):  
Nayani Ilangakoon ◽  
Nancy F. Glenn ◽  
Fabian D. Schneider ◽  
Hamid Dashti ◽  
Steven Hancock ◽  
...  
Keyword(s):  
Functional Diversity ◽  
Spatial Scales ◽  
Ecosystem Processes ◽  
Airborne Lidar ◽  
Arid Ecosystems ◽  
Arid Ecosystem ◽  
Area Index ◽  
Plant Area Index ◽  
Abiotic Controls ◽  
Semi Arid

Assessing functional diversity and its abiotic controls at continuous spatial scales are crucial to understanding changes in ecosystem processes and services. Semi-arid ecosystems cover large portions of the global terrestrial surface and provide carbon cycling, habitat, and biodiversity, among other important ecosystem processes and services. Yet, the spatial trends and patterns of functional diversity in semi-arid ecosystems and their abiotic controls are unclear. The objectives of this study are two-fold. We evaluated the spatial pattern of functional diversity as estimated from small footprint airborne lidar (ALS) with respect to abiotic controls and fire in a semi-arid ecosystem. Secondly, we used our results to understand the capabilities of large footprint spaceborne lidar (GEDI) for future applications to semi-arid ecosystems. Overall, our findings revealed that functional diversity in this ecosystem is mainly governed by elevation, soil, and water availability. In burned areas, the ALS data show a trend of functional recovery with time since fire. With 16 months of data (April 2019-August 2020), GEDI predicted functional traits showed a moderate correlation (r = 41–61%) with the ALS predicted traits except for the plant area index (PAI) (r = 11%) of low height vegetation (<5 m). We found that the number of GEDI footprints relative to the size of the fire-disturbed areas (=< 2 km2) limited the ability to estimate the full effects of fire disturbance. However, the consistency of diversity trends between ALS and GEDI across our study area demonstrates GEDI’s potential of capturing functional diversity in similar semi-arid ecosystems. The capability of spaceborne lidar to map trends and patterns of functional diversity in this semi-arid ecosystem demonstrates its exciting potential to identify critical biophysical and ecological shifts. Furthermore, opportunities to fuse GEDI with complementary spaceborne data such as ICESat-2 or the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR), and fine scale airborne data will allow us to fill gaps across space and time. For the first time, we have the potential to monitor carbon cycle dynamics, habitats and biodiversity across the globe in semi-arid ecosystems at fine vertical scales.

scholarly journals Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar

2020 ◽  
Author(s):  
Ginikanda Yapa Mudiyanselage Nayani Thanuja Ilangakoon
Keyword(s):  
Structure Function ◽  
Functional Diversity ◽  
Vegetation Structure ◽  
Structural Features ◽  
Airborne Lidar ◽  
Arid Ecosystems ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Full Waveform Lidar ◽  
Semi Arid

Semi-arid ecosystems cover approximately 40% of the earth's terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem. Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differentiate key plant functional types (PFTs) using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81-0.86% and 0.60-0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees). Using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI's potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m - 1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area resulted in a more even and less divergent ecosystem state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem's health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. Overall, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This work enhances our understanding of the present state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2 and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance.

scholarly journals Interactions between large herbivores and litter removal by termites across a rainfall gradient in a South African savanna

2011 ◽  
Vol 27 (4) ◽  
pp. 375-382 ◽  
Author(s):  
Robert Buitenwerf ◽  
Nicola Stevens ◽  
Cleo M. Gosling ◽  
T. Michael Anderson ◽  
Han Olff
Keyword(s):  
Plant Biomass ◽  
Spatial Scales ◽  
Landscape Scale ◽  
Local Scale ◽  
Arid Ecosystems ◽  
Annual Rainfall ◽  
Large Herbivores ◽  
Mammalian Herbivores ◽  
Litter Removal ◽  
Semi Arid

Abstract:Litter-feeding termites influence key aspects of the structure and functioning of semi-arid ecosystems around the world by altering nutrient and material fluxes, affecting primary production, foodweb dynamics and modifying vegetation composition. Understanding these complex effects depends on quantifying spatial heterogeneity in termite foraging activities, yet such information is scarce for semi-arid savannas. Here, the amount of litter that was removed from 800 litterbags in eight plots (100 litterbags per plot) was measured in Hluhluwe–iMfolozi Park (HiP) South Africa. These data were used to quantify variation in litter removal at two spatial scales: the local scale (within 450-m2 plots) and the landscape scale (among sites separated by 8–25 km). Subsequently, we attempted to understand the possible determinants of termites’ foraging patterns by testing various ecological correlates, such as plant biomass and bare ground at small scales and rainfall and fences that excluded large mammalian herbivores at larger scales. No strong predictors for heterogeneity in termite foraging intensity were found at the local scale. At the landscape scale termite consumption depended on an interaction between rainfall and the presence of large mammalian herbivores: litter removal by termites was greater in the presence of large herbivores at the drier sites but lower in the presence of large herbivores at the wetter sites. The effect of herbivores on termite foraging intensity may indicate a switch between termites and large herbivore facilitation and competition across a productivity gradient. In general, litter removal decreased with increasing mean annual rainfall, which is in contrast to current understanding of termite consumption across rainfall and productivity gradients. These results generate novel insights into termite ecology and interactions among consumers of vastly different body sizes across spatial scales.

scholarly journals Robust processing of airborne laser scans to plant area density profiles

2020 ◽  
Vol 17 (23) ◽  
pp. 5939-5952
Author(s):  
Johan Arnqvist ◽  
Julia Freier ◽  
Ebba Dellwik
Keyword(s):  
Beech Forest ◽  
Airborne Lidar ◽  
Small Scale ◽  
Average Intensity ◽  
Forest Site ◽  
Area Index ◽  
Area Density ◽  
Intensity Information ◽  
Plant Area Index ◽  
Plant Area

Abstract. We present a new algorithm for the estimation of the plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods do not use the intensity information at all, use only average intensity values, or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new algorithm to three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full-leaf) and winter (bare-tree) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites. Specific weak points regarding the estimation of the PAD from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms. We further show that low-resolution gridding of the PAD will lead to a negative bias in the resulting estimate according to Jensen's inequality for convex functions and that the severity of this bias is method dependent. As a result, the PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for the PAD gridding of airborne lidar scans.

scholarly journals Coordinated variation in leaf and root traits across multiple spatial scales in Chinese semi-arid and arid ecosystems

2010 ◽  
Vol 188 (2) ◽  
pp. 543-553 ◽  
Author(s):  
Guofang Liu ◽  
Grégoire T. Freschet ◽  
Xu Pan ◽  
Johannes H. C. Cornelissen ◽  
Yan Li ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Root Traits ◽  
Arid Ecosystems ◽  
Multiple Spatial Scales ◽  
Semi Arid

A Holistic View of an an Arid Ecosystem: A Synthesis of Research and Its Applications

2006 ◽  
Author(s):  
Brandon T. Bestelmeyer ◽  
Joel R. Brown
Keyword(s):  
Research Program ◽  
Ecosystem Processes ◽  
Primary Objective ◽  
Arid Ecosystems ◽  
Data Sets ◽  
Arid Ecosystem ◽  
Ecological Research ◽  
Long Term Ecological Research ◽  
Jornada Basin

A primary objective of the Jornada Basin research program has been to provide a broad view of arid land ecology. Architects of the program, more recently scientists with the Jornada Basin Long-Term Ecological Research (LTER) program, felt that existing ecological data sets were usually of too short a duration and represented too few ecosystem components to provide a foundation for predicting dynamics in response to disturbances (NSF 1979). This recognition gave rise to the approach of using long-term and multidisciplinary research at particular places to advance a holistic and broad-scale but also mechanistic view of ecological dynamics. Such a view is essential to applying ecological research to natural resources management (Golley 1993; Li 2000). In this synthesis chapter we ask: What has this approach taught us about the structure and function of an arid ecosystem? How should this knowledge change the way we manage arid ecosystems? What gaps in our knowledge still exist and why? The Jornada Basin LTER was established in 1981 with the primary aim of using ecological science to understand the progressive loss of semiarid grasslands and their replacement with shrublands. This motivation echoed that which initiated the Jornada Experimental Range (JER) 69 years earlier. The combined, century-long body of research offers a unique perspective on several core ideas in ecology, including the existence of equilibria in ecosystems, the role of scale, landscape heterogeneity and historic events in ecosystem processes and trajectories, and the linkage between ecosystem processes and biodiversity. From this perspective, we examine key assumptions of this research tradition, including the value of the ecosystem concept and the ability to extrapolate site-based conclusions across a biome. The Jornada Basin research program is also uncommon in its close ties to long-term, management-oriented research. The research questions first asked by the U.S. Forest Service and later by the Agricultural Research Service (ARS), such as how to manage livestock operations, frame much of the Jornada Basin research. This allows us to consider the contributions of this research and synthesis toward answering management questions.

scholarly journals Global biosphere–climate interaction: a multi-scale appraisal of observations and models

2019 ◽  
Author(s):  
Jeroen Claessen ◽  
Annalisa Molini ◽  
Brecht Martens ◽  
Matteo Detto ◽  
Matthias Demuzere ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Climate Impacts ◽  
Cmip5 Models ◽  
Wide Spread ◽  
Climate Control ◽  
Temporal Scales ◽  
Area Index ◽  
Local Character ◽  
The Impact ◽  
Semi Arid

Abstract. Improving the skill of Earth System Models (ESMs) in representing climate–vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise, feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bi-directional nature of climate–vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identifying potentially co-dependent variables. Results based on global and multi-decadal records of remotely-sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. The phenological cycle in energy-driven latitudes is mainly controlled by radiation, while in (semi-)arid regimes precipitation variability dominates at all temporal scales. However, at inter-annual scales, the control of water availability gradually becomes more wide-spread than that of energy constraints. The observational results are used as a benchmark to evaluate ESM simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics, and a particular overestimation of the dominance of precipitation in arid and semi-arid regions. Analogously, CMIP5 models overestimate the control of air temperature on forest seasonal phenology. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models, arguably due to the local character of the analysis that does not allow for the identification of downwind or remote vegetation feedbacks. Nonetheless, wide-spread effects of LAI variability on radiation are observed over the northern latitudes, presumably related to albedo changes, which are well-captured by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables.

scholarly journals Robust processing of airborne laser scans to plant area density profiles

2020 ◽  
Author(s):  
Johan Arnqvist ◽  
Julia Freier ◽  
Ebba Dellwik
Keyword(s):  
Beech Forest ◽  
Airborne Lidar ◽  
Small Scale ◽  
Average Intensity ◽  
Forest Site ◽  
Area Index ◽  
Area Density ◽  
Intensity Information ◽  
Plant Area Index ◽  
Plant Area

Abstract. We present a new algorithm for the estimation of plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods either do not use the intensity information at all, only use average intensity values or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new and three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full leaf) and winter (bare trees) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites, and further and more importantly – it never produces clearly dubious results. Specific weak points for estimation of PAD from airborne lidar data are addressed; the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is minimized using the new algorithm. We further show that low-resolution gridding of PAD will lead to a negative bias in the resulting estimate according to Jensen’s inequality for concave functions, and that the severity of this bias is method-dependent. As a result, PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for PAD gridding of airborne lidar scans.

scholarly journals Assessment of soil microbial and enzyme activity in the rhizosphere zone under different land use/cover of a semi-arid ecosystem, India

2020 ◽  
Author(s):  
Archana Meena ◽  
K.S. Rao
Keyword(s):  
Land Use ◽  
Management Practices ◽  
Forest Cover ◽  
Enzymatic Activities ◽  
Arid Ecosystems ◽  
Nutrient Cycle ◽  
Arid Ecosystem ◽  
Positive Correlation ◽  
Soil Microbial ◽  
Semi Arid

Abstract Background Land use/cover changes and management practices are widely known to influence SOM quality and quantity. The present study investigated the effect of different land uses i.e. forests viz. mixed forest cover (MFC), Prosopis juliflora (Sw.) DC dominated forest cover (PFC), and cultivated viz. agriculture field (AF), vegetable filed (VF), respectively, on soil parameter, microbial activity, and enzymes involved in soil nutrient cycle in a semi-arid ecosystem.Results The results showed a significant reduction (P < 0.05) in soil carbon (SC), soil nitrogen (SN) content (~ 30–80%) and consequently the soil microbial biomass carbon (SMBC) (~ 70–80%), soil basal respiration (SBR), soil substrate induced-respiration (SSIR), and soil enzyme activities (β-glucosidase, acid phosphatase, and dehydrogenase) under cultivated sites in comparison to forest analogs due to land use management practices. Pearson’s correlation showed a positive correlation of SC with SMBC, SBR, and SSIR (P < 0.01) and enzymatic activities i.e. β-glucosidase, dehydrogenase (P < 0.05) suggesting the critical role of SC in regulating microbial and enzymatic activity. Also, a positive correlation of SM with urease (P < 0.01) was observed indicating the importance of soil abiotic factors in controlling enzymatic activities. Additionally, based on the PCA analysis, we observed the clustering of SMBC/SC ratio and qCO2 nearby AF.Conclusion Our study suggested that degraded sites are more sensitive to the land management practices and land use changes and needed immediate attention in future studies related to SC dynamics in semi-arid ecosystems.

scholarly journals Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR

2021 ◽  
Vol 13 (4) ◽  
pp. 710
Author(s):  
Jordan Steven Bates ◽  
Carsten Montzka ◽  
Marius Schmidt ◽  
François Jonard
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Forest Cover ◽  
Growing Season ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Potential Yield ◽  
Area Index ◽  
Canopy Density ◽  
Plant Area Index

Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.

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