Remote Sensing of Fractional Cover and Biochemistry in Savannas

2010 ◽  
pp. 235-258
Keyword(s):  
Remote Sensing ◽  
Fractional Cover

scholarly journals Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

2019 ◽  
Vol 12 (1) ◽  
pp. 50 ◽  
Author(s):  
Mahyar Aboutalebi ◽  
Alfonso F. Torres-Rua ◽  
Mac McKee ◽  
William P. Kustas ◽  
Hector Nieto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Point Cloud ◽  
Spatial Information ◽  
Point Cloud Data ◽  
Area Index ◽  
Cloud Data ◽  
Fractional Cover ◽  
Spatially Distributed

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

scholarly journals Remote sensing of macrophyte morphological traits: Implications for the management of shallow lakes

2017 ◽  
Vol 76 (s1) ◽  
Author(s):  
Paolo Villa ◽  
Monica Pinardi ◽  
Viktor R. Tóth ◽  
Peter D. Hunter ◽  
Rossano Bolpagni ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Shallow Lake ◽  
Morphological Traits ◽  
Spectral Response ◽  
Freshwater Ecosystems ◽  
Area Index ◽  
Fractional Cover ◽  
Spectral Models

Macrophytes are important elements of freshwater ecosystems, fulfilling a pivotal role in biogeochemical cycles. The synoptic capabilities provided by remote sensing make it a powerful tool for monitoring aquatic vegetation characteristics and the functional status of shallow lake systems in which they occur. The latest generation of airborne and spaceborne imaging sensors can be effectively exploited for mapping morphologically – and physiologically – relevant vegetation features based on their canopy spectral response. The objectives of this study were to calibrate semi-empirical models for mapping macrophyte morphological traits (i.e., fractional cover, leaf area index and above-water biomass) from hyperspectral data, and to investigate the capabilities of remote sensing in supporting macrophyte monitoring and management. We calibrated spectral models using in situ reflectance and morphological trait measures and applied them to airborne hyperspectral imaging data, acquired over two shallow European water bodies (Lake Hídvégi, in Hungary, and Mantua lakes system, in Italy) in two key phenological phases. Maps of morphological traits were produced covering a broad range of aquatic plant types (submerged, floating, and emergent), common to temperate and continental regions, with an error level of 5.4% for fractional cover, 0.10 m2 m-2 for leaf area index, and 0.06 kg m-2 for above-water biomass. Based on these maps, we discuss how remote sensing could support monitoring strategies and shallow lake management with reference to our two case studies: i.e., by providing insight into spatial and species-wise variability, by assessing nutrient uptake by aquatic plants, and by identifying hotspot areas where invasive species could become a threat to ecosystem functioning and service provision.

Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe

2020 ◽  
Author(s):  
Jan-Peter George ◽  
Jan Pisek ◽  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Ecosystem ◽  
Remote Sensing Data ◽  
Ecosystem Functions ◽  
Area Index ◽  
Fractional Cover ◽  
Semi Empirical

<p>Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify  the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions.</p><p><strong>Liu Y. et al. (2017):</strong> Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.</p>

Estimating Tree Biomass via Remote Sensing, MSAVI 2, and Fractional Cover Model

2014 ◽  
Vol 31 (5) ◽  
pp. 362-368 ◽  
Author(s):  
Teerawong Laosuwan ◽  
Pornchai Uttaruk
Keyword(s):  
Remote Sensing ◽  
Tree Biomass ◽  
Fractional Cover

scholarly journals Comparing Methods of Rocky Desertification Monitoring at the Sub-pixel Scale in a Highly Heterogeneous Karst Region

2018 ◽  
Author(s):  
Xiangkun Qi ◽  
Chunhua Zhang ◽  
Kelin Wang
Keyword(s):  
Remote Sensing ◽  
Anthropogenic Disturbances ◽  
Spectral Mixture Analysis ◽  
Karst Region ◽  
Mixture Analysis ◽  
Rocky Desertification ◽  
Fractional Cover ◽  
Rocky Outcrop ◽  
Rocky Outcrops ◽  
Spectral Mixture

Karst rocky desertification (KRD) is a process where strong anthropogenic disturbances and exposure of carbonate bedrock occurs in fragile karst ecosystems. The fractional cover of rocky outcrops is a key indicator and mechanistic driver of KRD and can be accurately assessed using remote sensing technology. Nevertheless, rugged karst terrain relief can cause shadow effects on satellite imagery and combine with high heterogeneity of karst landscapes to prevent fractional cover retrievals. In this study, we explored the feasibility of applying multispectral high spatial resolution ALOS imagery for fractional cover extraction of rocky outcrops. We selected the dimidiate pixel model (DPM), which has been applied in previous studies, and spectral mixture analysis (SMA; including simple endmember spectral mixture analysis (SESMA) and multiple endmember spectral mixture analysis (MESMA)) to explore the feasibility of using remote images for KRD monitoring and improve accuracy for estimating fractions. Results from MESMA achieved high overall accuracy (76.4%) in monitoring percentage of rocky outcrop fraction in the study area. SESMA appears to underestimate percentage of rocky outcrop likely because the development of KRD was driven by complex factors (soil erosion, dissolution and anthropogenic disturbances). This results in spectral reflectance of rocky outcrop being variable in different settings. Predicted exposed bedrock coverage using SESMA and MESMA was similar in sun-lit and shaded areas although predictions from SESMA were smaller than reference data. DPM underestimated the fractional cover of rocky outcrops on south-facing slopes and overestimated it in shaded areas. Furthermore, SESMA and MESMA effectively reduced topographic effects. We conclude that it is better to extract percentage of rocky outcrop using MESMA in the karst region of southwestern China. Remote sensing is emerging as a feasible method to extract surface condition information in heterogeneous and rugged landscapes.

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