Visualizing and quantifying the movement of vegetative drought using remote-sensing data and GIS

Abstract

Remote-sensing-based drought monitoring methods provide fast and useful information for a sustainable management strategy of drought impact over a region. Common pixel-based monitoring methods are limited in the analysis of the dynamics of this impact at regional scale. For instance, these hardly allow us to quantify the movement of drought in space and time and to compare drought with rainfall deficits without losing the variability of these events within a region. This study proposed an object-based approach that allowed us to visualize and quantify the spatio-temporal movement of drought impact on vegetation, called vegetative drought, in a region. The GIS software Dynomap was used to extract and track objects. Measures of distance and angle were used for determining the speed and direction of vegetative drought and rainfall deficit objects, calculated from the National Oceanic and Atmospheric Administration's (NOAA's) normalized difference vegetation index and rainfall estimates data. The methods were applied to the two rainy seasons during the drought year 1999 in East Africa. Results showed that vegetative drought objects moved into the southwestern direction at an average angle of −138.5 • during the first season and −144.5 • during the second season. The speed of objects varied between 38 km dekad −1 and 185 km dekad −1 during the first season and between 33 km dekad −1 and 144 km dekad −1 during the second season, reflecting the rate of spread between dekads. Vegetative drought objects close to rainfall deficit objects showed similar trajectories and sometimes regions overlapped. This indicated that the two events are related. We conclude that a spatiotemporal relationship existed between the two types of events and that this could be quantified.

Geospatial big data handling theory and methods: A review and research challenges

“…Spatial statistics has always had good opportunities for visualizing spatial and spatio-temporal data, including the uncertainties (see Rulinda et al (2013) for an excellent example in the space-time domain). Their opportunities are still present also for geospatial big data.…”

Assessing Agricultural Vulnerability to Drought in a Heterogeneous Environment: A Remote Sensing-Based Approach

“…For evaluating drought conditions in the heterogeneous environment, a land cover map could be used to mask out non-agricultural lands. For example, Rulinda et al [4] used a land cover map to mask out non-vegetated areas and forest to separate vegetative areas. Note that this study uses the threshold of NDVI to segregate cropland from other land cover types and applies Equation (2) to measure and highlight agricultural drought conditions.…”

“…Drought is one of the natural hazards characterized by a prolonged water shortage. The impacts of drought are multifaceted, ranging from the environment of a country to its economy and society [1][2][3][4][5][6]. Various components including agriculture, vegetation, ecosystem, and water resources can be affected by drought [7,8].…”

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