Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Our case study is located in an area highly affected by coal mining activity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI and EVI2), chlorophyll vegetation index (CVI), and tasseled cap transformations were used as vegetation spectral indices to estimate canopy height. In turn, canopy height was used to predict a coniferous forest's biomass using Jenkins allometric and Lambert and Ung allometric equations. The biophysical properties of 700 individual trees at eight different scan stations in the study area were obtained using high-resolution ground LiDAR. Nine models (Hi) were established to discover the best relationship between the canopy height model (CHM) from the airborne LiDAR and the vegetation spectral indices (VSIs) from Landsat images for the year 2005, and HB9 (Jenkins allometric equation) and HY9 (Lambert and Ung allometric equation) proved to be the best models (r 2 = 0.78; root mean square error (RMSE) = 44 Mg/H, r 2 = 0.67; RMSE = 58.01 Mg/H, respectively; p < 0.001) for estimating the canopy height and the biomass. This model accurately captured the most affected areas (deforested) and the reclaimed areas (forested) in the study area. Five years were chosen for studying the biomass change : 1988, 1990, 2001, 2005, and 2011. OPEN ACCESSRemote Sens. 2015, 7 2833Additionally, four pixel-based image comparisons were analyzed (i.e., 1988-1990, 1990-2005, 2005-2009, and 2009-2011), and Mann-Kendall statistics for the subsets of years were obtained. The detected change showed that, in general, the environment in the study area was recovering and regaining its initial biomass after the dramatic decrease that occurred in 2005 as a result of intensive mining activities and disturbance.

Environment in arid conditions is dynamic and needs more investigation to understand the complexity of change. This spatiotemporal study will help to assess and monitor the land use and land cover change in the arid region of El-Arish area, where the climate and human activities are the major threats to rural development. In the past 11 years, dramatic changes of environment have been recorded in case studies. The post-classification comparison method was used to observe the changes using multi-temporal satellite images which were captured in the years 1999, 2001, 2005, and 2010. The overall accuracy of the produced thematic images was assessed regarding to the quantity and allocation disagreements. Five classes were defined in this investigation: bare soil, vegetation, urban, sand dunes, and fertile soil. From the year 1999 to 2010, fertile soil was increased by 13 %. Bare soil class occupied more than 50 % of land in the case study during for over a decade. From year 1999 to 2010, vegetation cover witnessed a dramatic increase. Soil and water management are the keys of land development and positive land use and land cover dynamics. Changing agricultural policies of using the available water resources are needed in the case study to prevent severe food shortage in the future

This research focuses on monitoring the desertification change as a result of mitigation and adaptation strategies in arid environmental condition. Exploring environmental hazards, specifically desertification development, is important for understanding loss of productivity in dry lands. Developing a new satellite-based algorithm for monitoring desertification in an arid environment delivers information useful in protecting the environment and mitigating natural hazards. A multi-temporal remote sensing data of MODerate resolution Imaging Spectroradiometer (MODIS) were used for estimating the Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST), based on monthly data during the years 2002, 2005, 2008 and 2011. The MODIS-based disturbance index (MBDI) was improved by estimating the long-term variation in the ratio of annual maximum composite LST and SAVI on a pixel-by-pixel basis. A significant correlation (r = -0.88; P < 0.001) was found between the mean-maximum SAVI and mean-maximum LST in the dry season. The response of the MBDI to land degradation was assessed by comparing the obtained soil salinity data to the algorithm outcomes. The results showed that the proposed new satellite-based algorithm has a high potential to detect the spatial extent of prime land degradation in an arid environment. Also, this algorithm was able to recognize the difference between the natural variability and instantaneous/non-instantaneous desertification symptoms in an arid environment. The mitigation strategies in the case study decreased the desertification development and combat the land degradation in the last decade

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