Abstract
The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.
Highlights
Vegetation is a general term for the plant community on the ground surface, such as forests, shrubs, grassland and agricultural crops, and it can intercept rainfall, alleviate runoffs, prevent desertification and conserve soil and water (Zhang et al.2013a)
In order to assess the accuracy of the Landsat-8 Operational Land Imager (OLI) data in fractional vegetation cover (FVC) estimation, a validation of ground quadratic data and the FVC results retrieved from the Landsat-8 OLI data was made
The results show that the proposed approach performed well in estimating FVC and had good consistency with in situ measurements
Summary
Vegetation is a general term for the plant community on the ground surface, such as forests, shrubs, grassland and agricultural crops, and it can intercept rainfall, alleviate runoffs, prevent desertification and conserve soil and water (Zhang et al.2013a). FVC is one of the main biophysical parameters involved in the surface processes, which is a necessary requirement for Numerical Weather Prediction, regional and global climate modelling, and global change monitoring (Avissar and Pielke 1989; Trimble 1990), and an important parameter for describing the surface vegetation, a comprehensive quantitative variable for plant community on ground surface, and a basic data for characterizing ecosystems, playing an extremely crucial role in the study of regional ecosystems (Godínez-Alvarez et al 2009; Jing et al 2011) It can be a key parameter in thermal remote sensing, since it is a basic parameter from which surface emissivities can be estimated (Jiménez-Muñoz et al 2009). An assessment is conducted concerning the usability of the proposed method in vegetation monitoring
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