Environmental analysis, management and modelling require detailed and precise land-use/land-cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image-based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A-factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM + satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non-corrected data.

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A decomposition scheme was applied to ALOS/PALSAR data obtained from a fast-growing tree plantation in Sumatra, Indonesia to extract tree stem information and then estimate the forest stand volume. The scattering power decomposition of the polarimetric SAR data was performed both with and without a rotation matrix and compared to the following field-measured forest biometric parameters: tree diameter, tree height and stand volume. The analytical results involving the rotation matrix correlated better than those without the rotation matrix even for natural scattering surfaces within the forests. Our primary finding was that all of the decomposition powers from the rotated matrix correlated significantly to the forest biometric parameters when divided by the total power. The surface scattering ratio of the total power markedly decreased with the forest growth, whereas the canopy and double-bounce scattering ratios increased. The observations of the OPEN ACCESS Remote Sens. 2012, 4 3059 decomposition powers were consistent with the tree growth characteristics. Consequently, we found a significant logarithmic relationship between the decomposition powers and the forest biometric parameters that can potentially be used to estimate the forest stand volume.

Climatological data records of temperature, rainfall, and number of rainy days provided by the Zaire Meteorological Agency (METTELSAT) at seven Zairian stations for the 1960–1992 period are analyzed for the first time since the 1970s. Our investigations focus on climate variability as related with environmental changes over the Zaire River Basin, which is climatologically and biogenetically one of the most important regions in the world. On the basis of the 30‐year monthly mean climatologies, it is shown that the solar annual cycle dominates the seasonal changes of both the temperature and rainfall over this region. On the interannual time‐scales, the variability of these climatic variables is characterized by (1) a 2‐to 5‐year oscillation strongly correlated to the southern oscillation index, thus to the ENSO phenomenon, and (2) a nearly 10‐year oscillation (called here the quasi‐decadal oscillation, QDO) with a very remarkable correlationship with the solar activity (sunspots number). On the long‐term timescale, a remarkable decreasing trend in rainfall and number of rainy days, as well as increasing temperatures over the 30‐year period, has been detected as the most dominant climatological features all over the basin. The magnitudes of temperature increase are by far larger than those reported in previous works for both the global mean and hemispherical mean warmings. We postulate that this trend of regional warming and desiccation from within the heart of the African rainforests is due to the increase in surface albedo, itself triggered by uncontrolled land‐use policies and forests development over this area (logging, slash and burn, bushfire, fuelwood, farming, ranching, urbanization, etc.).

The Serengeti–Mara ecosystem in East Africa is a spectacular natural heritage endowed with diverse fauna and flora. The presence of the seasonally migrating wildebeest (Connochaetes taurinus) is a major boost for tourism. This migration however has enormous impacts to the ecosystem. Consequently efforts at monitoring the herd's migration trends and patterns remain a challenge to wildlife managers and ecologists in the region. In this paper, the relative influence of vegetation (normalized difference vegetation index), landscape and relief on herds migration routes are investigated and the migration routes simulated using GIS and remote sensing techniques. The results are compared with the annual mean route taken by the herds, as determined by radio tracking over the 1995–1997 period. Green vegetation availability is shown to be the major criterion in route choice. It is also shown that during the dry season phases of the migration (western trek, western corridor), the herd endures complex relief (complexity quantified based on slope and inter‐visibility) in the search for greener grass. During the season of abundance (southern trek), relief becomes critical in making route choices, with herds avoiding difficult terrain, notwithstanding their relatively more abundant vegetation. The method proposed in this paper is viable for rapid prediction of approximate routes for the migrating wildebeest in different climatic conditions.