Estimating fractional cover of non-photosynthetic vegetation for various grasslands based on CAI and DFI

The Karimunjawa Islands mangrove forest has been subjected to various direct and indirect human disturbances in the recent years. If not properly managed, this disturbance will lead to the degradation of mangrove habitat health. Assessing forest canopy fractional cover (fc) using remote sensing data is one way of measuring mangrove forest degradation. This study aims to (1) estimate the forest canopy fc using a semi-empirical method, (2) assess the accuracy of the fc estimation and (3) create mangrove forest degradation from the canopy fc results. A sample set of in-situ fc was collected using the hemispherical camera for model development and accuracy assessment purposes. We developed semi-empirical relationship models between pixel values of ALOS AVNIR-2 image (10m pixel size) and field fc, using Enhanced Vegetation Index (EVI) as a proxy of the image spectral response. The results show that the EVI provides reasonable estimation accuracy of mangrove canopy fc in Karimunjawa Island with the values ranged from 0.17 to 0.96 (n = 69). The low fc values correspond to vegetation opening and gaps caused by human activities or mangrove dieback. The high fc values correspond to the healthy and dense mangrove stands, especially the Rhizophora sp formation at the seafront. The results of this research justify the use of simple canopy fractional cover model for assessing the mangrove forest degradation status in the study area. Further research is needed to test the applicability of this approach at different sites.

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Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.11.006 ◽

2019 ◽

Vol 76 ◽

pp. 154-166 ◽

Cited By ~ 12

Author(s):

Guangzhen Wang ◽

Jingpu Wang ◽

Xueyong Zou ◽

Guoqi Chai ◽

Mengquan Wu ◽

...

Keyword(s):

Bare Soil ◽

Modis Data ◽

Fractional Cover

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The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands

Remote Sensing ◽

10.3390/rs12030406 ◽

2020 ◽

Vol 12 (3) ◽

pp. 406 ◽

Cited By ~ 1

Author(s):

Michael J. Hill ◽

Juan P. Guerschman

Keyword(s):

Land Use ◽

Production Systems ◽

Land Use Changes ◽

Bare Soil ◽

Additional Information ◽

Fractional Cover ◽

Grassland Type ◽

Global Vegetation ◽

Global Indicator

Vegetation Fractional Cover (VFC) is an important global indicator of land cover change, land use practice and landscape, and ecosystem function. In this study, we present the Global Vegetation Fractional Cover Product (GVFCP) and explore the levels and trends in VFC across World Grassland Type (WGT) Ecoregions considering variation associated with Global Livestock Production Systems (GLPS). Long-term average levels and trends in fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) are mapped, and variation among GLPS types within WGT Divisions and Ecoregions is explored. Analysis also focused on the savanna-woodland WGT Formations. Many WGT Divisions showed wide variation in long-term average VFC and trends in VFC across GLPS types. Results showed large areas of many ecoregions experiencing significant positive and negative trends in VFC. East Africa, Patagonia, and the Mitchell Grasslands of Australia exhibited large areas of negative trends in FNPV and positive trends FBS. These trends may reflect interactions between extended drought, heavy livestock utilization, expanded agriculture, and other land use changes. Compared to previous studies, explicit measurement of FNPV revealed interesting additional information about vegetation cover and trends in many ecoregions. The Australian and Global products are available via the GEOGLAM RAPP (Group on Earth Observations Global Agricultural Monitoring Rangeland and Pasture Productivity) website, and the scientific community is encouraged to utilize the data and contribute to improved validation.

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A Method for Estimating the Sampling Error Applied to CM-SAF Monthly Mean Cloud Fractional Cover Data Retrieved From MSG SEVIRI

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2010.2041240 ◽

2010 ◽

Vol 48 (6) ◽

pp. 2469-2481 ◽

Cited By ~ 5

Author(s):

Maximilian Reuter ◽

Werner Thomas ◽

Sebastian Mieruch ◽

Rainer Hollmann

Keyword(s):

Sampling Error ◽

Fractional Cover

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Spectral indices for estimating the fractional cover of non-vegetation land-cover types in karst environment

2011 International Conference on Remote Sensing, Environment and Transportation Engineering ◽

10.1109/rsete.2011.5964821 ◽

2011 ◽

Cited By ~ 1

Author(s):

Ling Ding ◽

Xia Zhang ◽

Min Ji ◽

Tong Shuai ◽

Huanhu Qin ◽

...

Keyword(s):

Land Cover ◽

Spectral Indices ◽

Land Cover Types ◽

Karst Environment ◽

Fractional Cover

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

Remote Sensing ◽

10.3390/rs12010050 ◽

2019 ◽

Vol 12 (1) ◽

pp. 50 ◽

Cited By ~ 2

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.

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Shrub Fractional Cover Estimation and Mapping of San Clemente Island Shrubland Based on Airborne Multispectral Imagery and Lidar Data

Remote Sensing ◽

10.3390/rs12213608 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3608

Author(s):

Kelsey Warkentin ◽

Douglas Stow ◽

Kellie Uyeda ◽

John O’Leary ◽

Julie Lambert ◽

...

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Coastal Sage Scrub ◽

Surface Model ◽

Lidar Data ◽

Shrub Cover ◽

Fractional Cover ◽

Major Factors ◽

San Clemente Island

The purpose of this study is to map shrub distributions and estimate shrub cover fractions based on the classification of high-spatial-resolution aerial orthoimagery and light detection and ranging (LiDAR) data for portions of the highly disturbed coastal sage scrub landscapes of San Clemente Island, California. We utilized nine multi-temporal aerial orthoimage sets for the 2010 to 2018 period to map shrub cover. Pixel-based and object-based image analysis (OBIA) approaches to image classification of growth forms were tested. Shrub fractional cover was estimated for 10, 20 and 40 m grid sizes and assessed for accuracy. The most accurate estimates of shrub cover were generated with the OBIA method with both multispectral brightness values and canopy height estimates from a normalized digital surface model (nDSM). Fractional cover products derived from 2015 and 2017 orthoimagery with nDSM data incorporated yielded the highest accuracies. Major factors that influenced the accuracy of shrub maps and fractional cover estimates include the time of year and spatial resolution of the imagery, the type of classifier, feature inputs to the classifier, and the grid size used for fractional cover estimation. While tracking actual changes in shrub cover over time was not the purpose, this study illustrates the importance of consistent mapping approaches and high-quality inputs, including very-high-spatial-resolution imagery and an nDSM.

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