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Displaying 211 - 215 of 661Co-investments in land management: lessons from the Galessa watershed in Ethiopia
The use of co-investment activities to motivate farmers to carry out sustainable land management is increasingly recognized. Several co-investment efforts have been implemented to combat land degradation and increase agricultural production in the Ethiopian highlands. Nevertheless, these co-investment activities have not been documented. Moreover, the impacts of these activities have not been evaluated. This study presents a co-investment initiative for sustainable land management in the Galessa watershed in Ethiopia.
Evaluation 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.
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.
Feature extraction for high-resolution imagery based on human visual perception
Feature extraction is highly important for classification of remote-sensing (RS) images. However, extraction of comprehensive spatial features from high-resolution imagery is still challenging, leading to many misclassifications in various applications. To address the problem, a shape-adaptive neighbourhood (SAN) technique is presented based on human visual perception. The SAN technique is an adaptive feature-extraction method that not only considers spectral feature information but also the spatial neighbourhood as well as the shape of features.