Remotely sensed surface water extent as an indicator of short-term changes in ecohydrological processes in sub-Saharan Western Africa

Haas, Eva; Bartholomé, Étienne; Lambin, Éric F.; Vanacker, Veerle

doi:10.1016/j.rse.2011.08.007

Cited by 34 publications

(14 citation statements)

References 56 publications

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“…In fact, Gardelle et al [23] demonstrated that most ponds in the Gourma started to grow after the 1970s drought, with an important acceleration of the phenomenon usually a few years after the 1983-1984 drought. The case of the Agoufou pond (located near sites 17, 18, 20 and 31 in Figure 1) illustrates this phenomenon (see also Haas et al [59]). The annual increase of pond's volume derived from Landsat and other high resolution imagery is a proxy of the water entering the pond, and therefore of the run-off collected over the catchment.…”

Section: The Hydrological Sahelian Paradox In the Gourma Regionmentioning

confidence: 59%

Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox

et al. 2014

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Rain Use Efficiency (RUE), defined as Aboveground Net Primary Production (ANPP) divided by rainfall, is increasingly used to diagnose land degradation. Yet, the outcome of RUE monitoring has been much debated since opposite results were found about land degradation in the Sahel region. The debate is fueled by methodological issues, especially when using satellite remote sensing data to estimate ANPP, and by differences in the ecological interpretation. An alternative method which solves part of these issues relies on the residuals of ANPP regressed against rainfall (-ANPP residuals‖). In this paper, we use long-term field observations of herbaceous vegetation mass collected in the Gourma region in Mali together with remote sensing data (GIMMS-3g Normalized Difference Vegetation Index) to estimate ANPP, RUE, and the ANPP residuals, over the period 1984-2010. The residuals as well as RUE do not reveal any trend over time over the Gourma region, implying that vegetation is resilient over that period, when data are aggregated at the Gourma scale. We find no conflict between field-derived and satellite-derived results in terms of trends. The nature (linearity) of the ANPP/rainfall relationship is investigated and is found to have no impact on the RUE and residuals interpretation. However, at odds with a stable RUE, an increased run-off coefficient has OPEN ACCESS Remote Sens. 2014, 6 3447 been observed in the area over the same period, pointing towards land degradation. The divergence of these two indicators of ecosystem resilience (stable RUE) and land degradation (increasing run-off coefficient) is referred to as the -second Sahelian paradox‖. When shallow soils and deep soils are examined separately, high resilience is diagnosed on the deep soil sites. However, some of the shallow soils show signs of degradation, being characterized by decreasing vegetation cover and increasing run-off coefficient. Such results show that contrasted changes may co-exist within a region where a strong overall re-greening pattern is observed, highlighting that both the scale of observations and the scale of the processes have to be considered when performing assessments of vegetation changes and land degradation.

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Section: The Hydrological Sahelian Paradox In the Gourma Regionmentioning

confidence: 59%

Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox

et al. 2014

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“…In recent years, with the development and application of remote sensing technology and geographic information technology, many scholars have used remote sensing images to obtain information on dynamic changes in lakes and to analyze the natural and human factors that lead to changes in lake area (Feng et al, 2012;Haas et al, 2011;Li, Gao, Li, Yan, & Xu, 2017;Li, Liu, & Wang, 2014;Wang, Shi, & Tang, 2011;Yang, Huang, Wu, & Jia, 2014). Li, Liu, & Wang, (2014) studied the dynamic changes in lakes in Northeast China; the results showed that the lake changes were influenced by natural factors and human activities, and the natural factors had a greater influence on the lakes.…”

Section: Introductionmentioning

confidence: 99%

Monitoring dynamics and driving forces of lake changes in different seasons in Xinjiang using multi-source remote sensing

Jing

Fei

Wang

2017

European Journal of Remote Sensing

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Lakes are important for ensuring the development of the regional economy and ecological security. Based on MODIS and Landsat TM/OLI remote sensing images captured in 2005-2015 and a Combined Water Index (CWI), this paper extracted information on changes in the temporal and spatial characteristics and driving forces for lake areas in Xinjiang during past 11 years in different seasons (April, July and September). The results showed that over this time period, the area of the lakes exhibited an increasing trend. The largest surface area usually occurred in April, and the lowest value was measured in September. The lake shrinkage area in Xinjiang occurred mainly in Northern Xinjiang and the northeastern region of Southern Xinjiang, whereas the lake expansion area was concentrated in Eastern Xinjiang. Seasonal changes in Manas, Kanas, Barkol, Arkatag, Arik and Ebinur Lakes showed higher trends. Extracted the lake area based on the MODIS and TM/OLI image were analyzed, and both had a good corresponding relationship. The minimum R 2 was 0.548, and maximum RMSE was 44.1km 2 . Therefore, it was feasible to use MODIS to extract lake area. These changes in lake area resulted from both natural factors and human activities, and coupled effects greatly accelerated local environmental changes.

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“…Accurate wetland maps are needed to: better respond to and prepare for natural disasters and invasive species mediation [14,15], conserve and restore wetland areas following policy and regulation changes [16,17], address water quality and quantity concerns [18,19], and better understand the linkages and seasonality of these ecosystems to biodiversity and other natural resources [20,21]. However, many existing wetland maps are out of date and efforts for updating them tend to happen over small geographic extents or at intervals too infrequent for appropriate environmental mitigation [22].…”

Section: Introductionmentioning

confidence: 99%

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

2013

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Abstract:Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource OPEN ACCESS Remote Sens. 2013, 5 3213 managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

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Remotely sensed surface water extent as an indicator of short-term changes in ecohydrological processes in sub-Saharan Western Africa

Cited by 34 publications

References 56 publications

Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox

Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox

Monitoring dynamics and driving forces of lake changes in different seasons in Xinjiang using multi-source remote sensing

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

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