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Displaying 211 - 215 of 661Evaluation of land cover classification based on multispectral versus pansharpened landsat ETM+ imagery
Land cover generated from satellite images is widely used in many real-world applications such as natural resource management, forest type mapping, hydrological modeling, crop monitoring, regional planning, transportation planning, public information services, and so on. Moreover, land cover data are one of the primary inputs to many geospatial models.
Social network effects on the adoption of sustainable natural resource management practices in Ethiopia
Soil loss, nutrient depletion and land degradation contribute to the skimpy performance of smallholder agriculture and pose serious policy challenges in developing countries. Surprisingly, natural resource management practices that enhance sustainability while improving productivity have not been fully adopted despite continuous efforts of promotion.
RGB-NDVI color composites for monitoring the change in mangrove area at the Maubesi Nature Reserve, Indonesia
The Maubesi Nature Reserve (MNR) is a protected lowland area in eastern Indonesia that mainly consists of mangrove forest. The objective of this paper was to demonstrate a simple technique to visualize and quantify the change in mangrove area using a 3-year dataset of Landsat TM images acquired in 1989, 2003 and 2009. The normalized difference vegetation index (NDVI) was calculated to determine high and low vegetation biomass in each image.
Estimating net surface longwave radiation from net surface shortwave radiation for cloudy skies
This work addresses the estimation of net surface longwave radiation (NSLR) from net surface shortwave radiation (NSSR) by analysing the Surface Radiation Budget Network (SURFRAD) radiation data under cloudy conditions. A general model is developed to estimate NSLR from the NSSR for cloudy skies with a root mean square error (RMSE) of 23.16 W m⁻² compared with in situ data. The model is applied to AmeriFlux data. The results show that the mean error and RMSE are –2.31 W m⁻² and 29.25 W m⁻², respectively, compared with the measurement of AmeriFlux.
Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion
Time series of vegetation index (VI) information derived from remote sensing is important for land-cover change detection. Although traditional change vector analysis (TCVA) is an effective method for extracting land-cover change information from a time series of VI data, it has the disadvantage of being too sensitive to temporal fluctuations in VI values. The method tends to overestimate the changes and confuse the actual land-cover conversion with the land covers that have not been converted but experience significant VI changes.