Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data

This paper evaluates the usefulness of the hyperspectral imager (HSI) onboard Chinese HJ-1-A small satellite in vegetation mapping. Fractional vegetation cover (FVC) is an important surface microclimate parameter for characterizing land surface vegetation cover as well as the most effective indicator for assessing desertification and crop growth condition. The HJ-1/HSI data were used to calcula...

Evaluation of Fractional Green Vegetation Cover in Residential Area

Green vegetation plays rather important role in urban environment. Recently, re-vegetation in urban residential area is becoming important policy for urban planners from the view point of climate change measures. That is, by maintaining and regenerating green vegetation, both mitigation and adaptation can be achieved. In this regard, establishment of accurate assessment and evaluation methods f...

Fractional Vegetation Cover Estimation In Urban Environments

Quality of life in urban environments is closely related to vegetation cover. The Urban growth and its related environmental problems, planners are forced to implement policies to improve the quality of urban environment. Thus, vegetation mapping for planning and managing urban is critical. Given the spectral complexity of the urban environment and the sparse vegetation in these areas, to gener...

Comparison of field- and satellite-based vegetation cover estimation methods

Background: Monitoring terrestrial vegetation cover condition is important to evaluate its current condition and to identify potential vulnerabilities. Due to simplicity and low cost, point intercept method has been widely used in evaluating grassland surface and quantifying cover conditions. Field-based digital photography method is gaining popularity for the purpose of cover estimate, as it c...

Utilizing satellite images and artificial neural networks in estimation of vegetation fraction in arid regions