Remote Sensing of Shrubland Drying in the South-East Mediterranean, 1995–2010: Water-Use-Efficiency-Based Mapping of Biomass Change

Abstract

Recent climate studies of the South-Eastern Mediterranean indicate an increase in drought frequencies and decreasing water resources since the turn of the century. A four-phase methodology was developed for assessing above-ground biomass changes in shrublands caused by these recent trends. Firstly, we generalized the function SB = 0.008MAP 1.54 describing the shrublands above-ground biomass (SB) dependence on mean annual precipitation (MAP) for areas of full shrub cover. Secondly, relationships between MAP and NDVI were formalized, allowing an estimation of precipitation levels from observed NDVI values (MAPNDVI). Thirdly, relative water-use efficiency (RWUE) was defined as the ratio between MAPNDVI and MAP. Finally, the function SBRWUE = 0.008MAP 0.54 + RWUE was formalized, utilizing RWUE in estimating shrublands biomass. This methodology was implemented using Landsat TM images (1994 to 2011) for an area between the Judean Mountains and the deserts bordering them to the east and south. More than 50% of the study area revealed low biomass change (±0.2 kg/m

Recent changes and relations among drought, vegetation and wildfires in the Eastern Mediterranean: The case of Israel

“…A statistically significant negative trend in FPAR values in all seasons (although quite weak in DJF) was identified in the transition zone between the northern Negev desert and the Mediterranean climate region. This finding corresponds to other studies that found a statistically significant decrease in vegetation cover and in productivity along this desert fringe area, using MODIS derived time series of FPAR (Levin, 2016), Landsat images which enabled to identify the drying of shrubland (Shoshany and Karnibad, 2015) and the drying of planted forests (Dorman et al, 2013). Such increased dry conditions may well enhance the probability of wildfires to propagate, as has been found in previous studies (Kutiel, 2012 …”

Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery

“…The NDVI has found extensive use in various applications such as space-time trend analysis of vegetation health (Shoshany and Karnibad [8]), mapping of invasive species (Paz-Kagan et al [5]), and identification of environmental factors that influence vegetation (Karnieli et al [9]). Despite widspread adoption, several notable limitations of this index have been documented.…”

Precipitation-Sensitive Dynamic Threshold: A New and Simple Method to Detect and Monitor Forest and Woody Vegetation Cover in Sub-Humid to Arid Areas

“…However, observations show large difference in the values of NDVI obtained for similar values of vegetation cover, when comparing across regions with different levels of precipitation [11]. Such differences can result in under-detection of forests, especially in regions with low precipitation load.…”

“…LAI derived from remotely sensed data is usually based on parametrization of the Fractional Vegetation Cover (FVC). Therefore, from the remote-sensing perspective it is difficult to distinguish between the two causes of change, although in many cases increasing inputs of precipitation will be associated with an increase in both vegetation cover, plant biomass, and LAI [11,17].…”