Disentangling the Relationships between Net Primary Production and Precipitation in Southern Africa Savannas Using Satellite Observations from 1982 to 2010

Zhu, Likai; Southworth, Jane

doi:10.3390/rs5083803

Cited by 65 publications

(60 citation statements)

References 55 publications

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“…Previous studies showed the ability of GIMMS3g to capture productivity dynamics (Wang et al 2014, Zhu & Southworth 2013. Our result of NDVI increasing trends is in agreement with the increase in carbon storage observed for African intact tropical forests ), as well as with the increase in net primary production recorded at a global scale (Nemani et al 2003).…”

Section: Regional Forest Productivity and Climate Variable Trendssupporting

confidence: 79%

Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana

Laurin¹,

Hawthorne²,

Chiti³

et al. 2016

iForest

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(1-3) Degradation, a reduction of the ecosystem's capacity to supply goods and services, is widespread in tropical forests and mainly caused by human disturbance. To maintain the full range of forest ecosystem services and support the development of effective conservation policies, we must understand the overall impact of degradation on different forest resources. This research investigates the response to disturbance of forest structure using several indicators: soil carbon content, arboreal richness and biodiversity, functional composition (guild and wood density), and productivity. We drew upon large field and remote sensing datasets from different forest types in Ghana, characterized by varied protection status, to investigate impacts of selective logging, and of illegal land use and resources extraction, which are the main disturbance causes in West Africa. Results indicate that functional composition and the overall number of species are less affected by degradation, while forest structure, soil carbon content and species abundance are seriously impacted, with resources distribution reflecting the protection level of the areas. Remote sensing analysis showed an increase in productivity in the last three decades, with higher resiliency to change in drier forest types, and stronger productivity correlation with solar radiation in the short dry season. The study region is affected by growing anthropogenic pressure on natural resources and by an increased climate variability: possible interactions of disturbance with climate are also discussed, together with the urgency to reduce degradation in order to preserve the full range of ecosystem functions.

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Section: Regional Forest Productivity and Climate Variable Trendssupporting

confidence: 79%

Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana

Laurin¹,

Hawthorne²,

Chiti³

et al. 2016

iForest

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“…The critical importance of water is well recognized across savanna vegetation types (from open grassland savanna to densely wooded savanna) [11,12], although its role differs across vegetation types and biomes [13]. These studies have also demonstrated that fire regime, soils, herbivory, nutrients and land use also contribute to local patterns of ecosystem structure.…”

Section: Introductionmentioning

confidence: 80%

Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

et al. 2013

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Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)-vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982-2010) data found that for OPEN ACCESSRemote Sens. 2013, 5 6514 MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas.

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“…The ANN model showed better performance and was chosen. As for regression variables, we did not use vegetation data as a factor because of the "saturate" effect [71,72], which means it is not a good indicator of precipitation in this area. In addition, many ecology studies usually relate vegetation information (NDVI) to precipitation.…”

Section: Discussionmentioning

confidence: 99%

Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China

Liu

et al. 2015

Water

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Abstract:The lack of high spatial resolution precipitation data, which are crucial for the modeling and managing of hydrological systems, has triggered many attempts at spatial downscaling. The essence of downscaling lies in extracting extra information from a dataset through some scale-invariant characteristics related to the process of interest. While most studies utilize only one source of information, here we propose an approach that integrates two independent information sources, which are characterized by self-similar and relationship with other geo-referenced factors, respectively. This approach is applied to 16 years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) of TRMM 3B43 monthly precipitation data in an orographic and monsoon influenced region in South China. Elevation, latitude, and longitude are used as predictive variables in the regression model, while self-similarity is characterized by multifractals and modeled by a log-normal multiplicative random cascade. The original 0.25° precipitation field was downscaled to the 0.01° scale. The result was validated with rain gauge data. Good consistency was achieved on coefficient of determination, bias, and root mean square error. This study contributes to the current precipitation downscaling OPEN ACCESSWater 2015, 7 3084 methodology and is helpful for hydrology and water resources management, especially in areas with insufficient ground gauges.

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Disentangling the Relationships between Net Primary Production and Precipitation in Southern Africa Savannas Using Satellite Observations from 1982 to 2010

Cited by 65 publications

References 55 publications

Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana

Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana

Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China

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