Co-Authors:
Karnieli, A., The Remote Sensing Laboratory, Jacob Blaustein Inst. Desert Res., Ben Gurion University of the Negev, Sede-Boker Campus 84990, Israel
Gabai, A., The Remote Sensing Laboratory, Jacob Blaustein Inst. Desert Res., Ben Gurion University of the Negev, Sede-Boker Campus 84990, Israel
Ichoku, C., The Remote Sensing Laboratory, Jacob Blaustein Inst. Desert Res., Ben Gurion University of the Negev, Sede-Boker Campus 84990, Israel
Zaady, E., The Remote Sensing Laboratory, Jacob Blaustein Inst. Desert Res., Ben Gurion University of the Negev, Sede-Boker Campus 84990, Israel
Shachak, M., The Remote Sensing Laboratory, Jacob Blaustein Inst. Desert Res., Ben Gurion University of the Negev, Sede-Boker Campus 84990, Israel
Abstract:
This paper discusses several difficulties encountered in detecting and monitoring temporal changes in vegetation using multispectral imagery from airborne or spaceborne sensors. These difficulties are due to (1) temporal change in the vegetation state; (2) temporal change in the soil/rock signature; and (3) difficulty in discriminating vegetation from soil or rock background. The seasonal dynamics of soil and vegetation was investigated over two years on permanent sample plots in a natural fenced-off area in the semi-arid region (200 mm annual average rainfall) of the Northern Negev, Israel. Results show that temporal analysis of natural vegetation in semi-arid regions should take into account three ground features-perennials, annuals, and biological soil crusts; all having phenological cycles with the same basic elements - oscillation from null (or low) to full photosynthetic status. However, these cycles occur in successive periods throughout the year. The phenological cycle of perennial plants is related to the adaptation of desert plants to scarcity of water. Annuals are green only for a relatively short period during the wet season and turn into dry organic matter during the summer. The microphytic communities (lower plants) of the biological soil crusts are rapidly affected by moisture and turn green immediately after the first rain, in a timescale of minutes. In arid environments, where the higher plants are sparse, this type of plant has considerable importance in the overall production of the greenness signal. However, crust-covered areas are visually similar to bare soil throughout the dry period. This paper concludes that a priori knowledge of the phenological changes in desert plants (lower and higher) is valuable in the interpretation of remote sensing data of arid environments. It is shown that rainfall amount and regime are the keys for understanding the dynamic processes of the different ground features. Through polynomial fitting, simple functions describing the annual variations in the NDVI of the different cover types have been formulated and validated; showing the feasibility and viability of modelling the processes. Although fluctuations in the rainfall regime between years poses a problem to designing a unique model, it is believed that such a problem can be overcome with long-term observations.
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