Among the existing global observing sensors, such as Terra/MODIS, NOAA/AVHRR, ADEOS-II/ GLI, with a large field of view, there is a need to take Bidirectional Reflectance Distribution Function (BRDF) effect into account when using data of these sensors to analyze. For estimating the global NPP (Net Primary Production) from the spectral data of global observing sensors, and improving the accuracy of this estimation method simultaneously, it is necessary to know how this method was affected by BRDF effect, especially in the sensor's observation conditions. Using a Japanese cedar forest as the objective and a semi-empirical kernel-driven BRDF model (the RossThick-LiSparse model), this study analyzed BRDF effects on NPP estimation. The BRDF data of the forest used in this study were measured by a sensor onboard a radio-controlled helicopter with bidirectional reflectance factors (BRF) observations in July 2002. After validating the application of Ross-Li BRDF model to Japanese cedar forest, parameters of this model for the Japanese cedar forest were obtained, and used to retrieve reflectance for the nadir view and nadir illumination. With the retrieved reflectance, for the cedar forest, NPP estimations were affected by BRDF effects of approximately 11% under the GLI simulated observation conditions. This study also sought to validate the NPP estimation algorithm based on the pattern-decomposition-based vegetation index (VIPD) and photosynthetically active radiation (PAR) for multi-spectral sensor data. From the retrieved reflectance, the NPP of the study forest was estimated to be 0.36 kgCO₂/m²/month. For comparison, forest surveys at the same study site have been conducted since the BRF observation. Using the ground-measured data, NPP was estimated to be 0.32 kgCO₂/m²/month, which is in agreement with the preliminary result.

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Metropolitan cities grow beyond imaginable proportions, particularly in the developing world. Urban issues therefore warrant - and receive - increasing attention. This paper summarizes the general pattern of urban land-cover changes in Karachi, Pakistan between the years 1992 and 2003 and explores the impacts of these urban land cover changes on water resources in the study area. Subsets of two Landsat TM images acquired in March 1992 and May 1998 and a subset of Landsat ETM+ image acquired in March 2003 were used for mapping land cover changes. An unsupervised classification approach, which uses a minimum spectral distance to assign pixels to clusters, was used with the overall accuracy ranging from 89 percent to 91 percent. Land cover statistics quantify that substantial land cover changes have taken place in Karachi metropolis and that the urban land has expanded by 203.1 km² while barren and vegetated lands have decreased by 191.9 km² and 13.5 km², respectively over the study period (1992-2003) . The results demonstrate that land cover changes due to urbanization has not only contaminated the water quality but also aggravated the water shortfall in the study area, making difficulties in equal distribution of supply of water to all the areas of large city.

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Surface reflectance model of forests with a consideration of interactions among trees and land surface including grasses in terms of multiple reflections by means of Monte-Carlo simulations together with experiments is proposed. The Monte Carlo method statistically simulates the paths of photons inside the atmosphere and the forested area to clarify the behaviors of individual photon. The radiance on ellipse and cone types of canopy calculated agreed well with the measured. Influences due to shape of the trees, trunk tree distance, and ratio of horizontal and vertical size of the trees and so on to the parameter estimation are clarified with the experimental results. It is found that a few to more than 10% of influence due to multiple reflections among trees are anticipated. The potentialities of the code are then depicted over different types of forests including coniferous and broadleaf canopies.

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