A Dynamic Vegetation Senescence Indicator for Near-Real-Time Desert Locust Habitat Monitoring with MODIS

Abstract

Desert locusts (Schistocerca gregaria) represent a major threat for agro-pastoral resources and food security over almost 30 million km 2 from northern Africa to the Arabian peninsula and India. Given the differential food preferences of this insect pest and the extent and remoteness of the their distribution area, near-real-time remotely-sensed information on potential habitats support control operations by narrowing down field surveys to areas favorable for their development and prone to gregarization and outbreaks. The development of dynamic greenness maps, which detect the onset of photosynthetic vegetation, allowed national control centers to identify potential habitats to survey, as locusts prefer green and fresh vegetation. Their successful integration into the daily control operations led to a new need: the near-real-time identification of the onset of dryness, a synonym for the loss of habitat attractiveness, likely to be abandoned by locusts. The timely availability of this information would enable control centers to focus their surveys on areas more prone to gregarization, leading to more efficiency in the allocation of resources and in decision making. In this context, this work developed an original method to detect in near-real-time Remote Sens. 2015, 7 7546 the onset of vegetation senescence. The design of the detection relies on the temporal behavior of two indices: the Normalized Difference Vegetation Index, depending on the green vegetation, and the Normalized Difference Tillage Index, sensitive to both green and dry vegetation. The method is demonstrated in Mauritania, an ever-affected country, with 10-day MODIS mean composites for the years 2010 and 2011. The discrimination performance of three classes ("growth", "density reduction" and "drying") were analyzed for three classification methods: maximum likelihood (61.4% of overall accuracy), decision tree (71.5%) and support vector machine (72.3%). The classification accuracy is heterogeneous in both time and space and is affected by several factors, such as vegetation density, the north-south climatic gradient and the relief. Smoothing the vegetation time series resulted in an increase of the overall accuracy of about 5% at the expense of a loss in timeliness of ten days. To simulate near-real-time monitoring conditions, the decision tree was applied to the decade of 2010. Overall, the seasonal vegetation cycle appeared clear and consistent. The results obtained pave the way for an operational implementation of the senescence dynamic mapping and, consequently, to further strengthen the capacity of the locust control management.

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

“…In fact, the vegetation dynamic in cropland and natural vegetation can fluctuate strongly with latitudes [56][57][58][59]. To limit the boundary effect between grid cells, the training was performed on a larger grid cell.…”

“…The accuracy assessment described in Section 3.4 provides a global performance evaluation, although it is well established that classification accuracy varies across space and that errors are not equally distributed spatially [59,[64][65][66]. Eight potential explanatory variables were proposed to explain the classification accuracy computed by OA and the F-score: latitude and longitude of the grid cell center, availability of cloud-free data and five landscape metrics indices.…”

An information-based criterion to measure pixel-level thematic uncertainty in land cover classifications

“…Accuracy measures have been designed to report accuracy both at the map level and at the class level [see (Story and Congalton 1986) for examples] and are typically assumed to apply uniformly over the region of interest. Yet several studies have also demonstrated that errors vary spatially (Liu et al 2004;Foody 2005;Comber et al 2012;Renier et al 2015;Liu et al 2015;Waldner et al 2015b;Feng et al 2015).…”

Operational Monitoring of the Desert Locust Habitat with Earth Observation: An Assessment

“…Since July 2010, these maps are freely available to the National Locust Control Centers (NLCCs) and the FAO on the DevCoCastportal. More recently, Renier et al [34] developed a dynamic vegetation senescence indicator for near-real-time monitoring of the desert locust habitat with MODIS in order to identify areas likely to be abandoned by locusts.…”