GIS-based stratification of malaria risk zones for Zimbabwe

Abstract

Malaria burden has considerably declined in the last 15 years mainly due to large-scale vector control. The continued decline can be sustained through malaria risk stratification. Malaria stratification is the classification of geographical areas according to malaria risk. In this study, ecological niche modelling using the maximum entropy algorithm was applied to predict malaria vector habitat suitability in terms of bioclimatic and topographic variables. The output vector suitability map was integrated with malaria prevalence data in a GIS to stratify Zimbabwe into different malaria risk zones. Five improved and validated malaria risk zones were successfully delimited for Zimbabwe based on the World Health Organization classification scheme. These results suggest that the probability of occurrence of major vectors of malaria is a key determinant of malaria prevalence. The delimited malaria risk zones could be used by National Malaria Control programmes to plan and implement targeted malaria interventions based on vector control.     

Land-use and land tenure explain spatial and temporal patterns in terrestrial net primary productivity (NPP) in Southern Africa

Net primary productivity (NPP) is an important indicator of ecosystem health and its estimation and understanding of factors determining its spatial and temporal variations is critical. It is important to note that biophysical factors and human induced factors are interlinked in determining NPP patterns. Nevertheless, it is difficult to consider some aspects of human management systems in relation to NPP variations on a global scale analysis than on a local scale analysis. In this study, we tested the hypothesis that, at the local scale, particularly in highly intensive systems, land-use/land tenure types influence NPP variations by altering the biophysical conditions of the land. We estimated NPP between 2000 and 2009 using MODIS data and used ANOVA to test the abovementioned hypotheses. Results showed that NPP significantly (p < 0.05) varied by land-use/land tenure type. We also found that biophysical factors remained essential in explaining NPP variations even at local scales. These results exhibit the intricacies that exist between the biophysical and human-induced factors in explaining NPP variations within ecological landscapes.     

Detection of subtle deforestation due to logging using satellite remote sensing in wet and dry savanna woodlands of Southern Africa

Tropical deforestation through logging activities poses a direct threat to biodiversity. However, the detection of logging has remained a challenge. Based on study sites in Zimbabwe and Zambia, we tested whether the Normalized Difference Vegetation Index (NDVI) and the Coefficient of Variation in NDVI (CVNDVI) derived from high and medium spatial resolution satellite data could be used to detect logging in dry and wet miombo woodlands. Separately, we integrated NDVI and CVNDVI in logistic regression to test whether each can be used to successfully predict logging in the study sites. We tested whether the spatial resolution of satellite data has an effect in detection of logging using NDVI and CVNDVI derived from Landsat 8 and Worldview-2. Based on the ROC curves, we concluded that remotely sensed data could provide an effective predictive tool for detecting logging. However, in wet miombo woodlands the predictive power of remotely sensed data is weak.     

Changes in landuse/landcover patterns and human population growth in the Lake Chivero catchment,Zimbabwe

The land use and land cover pattern of a region is a consequence of natural and socio-economic factors and their utilization by man in time and space. In this study, we hypothesized that land use and land cover change patterns in the Lake Chivero catchment, Zimbabwe, were related to its human population dynamics. Using nonparametric correlation coefficients (Spearman’s rho, ρ), we found that bareland, cropland and built-up land had positive relations with human population growth of ρ = 0.7, ρ = 0.9 and ρ = 1, respectively. Grassland/shrubland, water and forest, on the other hand, had a negative relationship with human population growth of ρ = ?0.9, ρ = ?0.7 and ρ = ?0.667, respectively. However, these relationships were only significant (p < 0.05) for cropland, grassland/shrubland and built-up land. Human population dynamics in the Lake Chivero catchment could be one of the major drivers of land use and land cover change in the catchment between 1986 and 2014.     

Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R² ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.     

Controls of 234Th removal from the oligotrophic ocean by polyuronic acids and modification by microbial activity

To gain new insights into the variability of particulate organic carbon (POC) fluxes and to better understand the factors controlling the POC/²³⁴Th ratios in suspended and sinking particulate matter, we investigated the relationships between POC/²³⁴Th ratios and biochemical composition (uronic acids, URA; total carbohydrates, TCHO; acid polysaccharides, APS; and POC) of suspended and sinking matter from the Gulf of Mexico in 2005 and 2006. Our data show that URA/POC in sediment traps (STs), APS/POC in the suspended particles, and turnover times of particulate ²³⁴Th in the water column and those of bacteria in STs inside eddies usually increased with depth, whereas particulate POC/²³⁴Th (10–50 μm) and the sediment-trap parameters (POC flux, POC/²³⁴Th ratio, bacterial biomass, and bacterial production) decreased with depth. However, this trend was not the case for most biological parameters (e.g., phytoplankton and bacterial biomass) or for the other parameters at the edges of eddies or at coastal-upwelling sites.In general, the following relationships were observed: 1) ²³⁴Th/POC ratios in STs were correlated with APS flux, and these ratios in the 10–50 μm suspended particles also correlated with URA/POC ratios; 2) neither URA fluxes nor URA/POC ratios were significantly related to bacterial biomass; 3) the sum of two uronic acids (G2, glucuronic, and galacturonic acid, which composed most of the URA pool) was positively related to bacterial biomass; and 4) the POC/²³⁴Th ratios in intermediate-sized particles (10–50 μm) were close to those in sinking particles but much lower than those in > 50 μm particles. The results indicate that acid polysaccharides, though a minor fraction (~ 1%) of the organic carbon, act more likely as proxy compound classes that might contain the more refractory ²³⁴Th-binding biopolymer, rather than acting as the original ²³⁴Th “scavenger” compound. Moreover, these acid polysaccharides, which might first be produced by phytoplankton and then modified by bacteria, also influence the on-and-off “piggy-back” processes of organic matter and ²³⁴Th, thus causing additional variability of the POC/²³⁴Th in particles of different sizes.     

Palaeoclimate inferred from δ18O and palaeobotanical indicators in freshwater tufa of Lake Äntu Sinijärv,Estonia

We investigated a 3.75-m-long lacustrine sediment record from Lake Äntu Sinijärv, northern Estonia, which has a modeled basal age >12,800 cal yr BP. Our multi-proxy approach focused on the stable oxygen isotope composition (δ¹⁸O) of freshwater tufa. Our new palaeoclimate information for the Eastern Baltic region, based on high-resolution δ¹⁸O data (219 samples), is supported by pollen and plant macrofossil data. Radiocarbon dates were used to develop a core chronology and estimate sedimentation rates. Freshwater tufa precipitation started ca. 10,700 cal yr BP, ca. 2,000 years later than suggested by previous studies on the same lake. Younger Dryas cooling is documented clearly in Lake Äntu Sinijärv sediments by abrupt appearance of diagnostic pollen (Betula nana, Dryas octopetala), highest mineral matter content in sediments (up to 90 %) and low values of δ¹⁸O (less than ?12 ‰). Globally recognized 9.3- and 8.2-ka cold events are weakly defined by negative shifts in δ¹⁸O values, to ?11.3 and ?11.7 ‰, respectively, and low concentrations of herb pollen and charcoal particles. The Holocene thermal maximum (HTM) is palaeobotanically well documented by the first appearance and establishment of nemoral thermophilous taxa and presence of water lilies requiring warm conditions. Isotope values show an increasing trend during the HTM, from ?11.5 to ?10.5 ‰. Relatively stable environmental conditions, represented by only a small-scale increase in δ¹⁸O (up to 1 ‰) and high pollen concentrations between 5,000 and 3,000 cal yr BP, were followed by a decrease in δ¹⁸O, reaching the most negative value (?12.7 ‰) recorded in the freshwater tufa ca. 900 cal yr BP.     

Predicting the spatial distribution of hunted and non-hunted Sable antelope (Hippotragus niger niger) using remotely sensed woody cover in a Southern African savanna

From remotely sensed woody cover, we tested whether sables under hunting pressure preferred closed woodland habitats and whether those not under hunting preferred more open woodland habitats. We applied a two factorial logistic regression analysis to model the probability of occurrence of sable antelope in hunted and non-hunted areas of northwest Zimbabwe as a function of vegetation cover density (estimated by a normalized difference vegetation index (NDVI)). We validated the results by high-spatial resolution imagery derived tree canopy area. We subsequently compared the predictions from the two models in order to compare sable cover selection between hunted and non-hunted areas. Our results suggest that hunted sables are likely to select closed woodland, while non-hunted ones would prefer more open woodland habitats. We also established a significant positive relationship between NDVI and tree canopy cover, thus emphasizing the importance of remote sensing in studies that measure the impact of hunting on habitat selection of targeted species.     

Accounting for phenology in maize yield prediction using remotely sensed dry dekads

Rain-fed agriculture is threatened by an increased frequency of droughts worldwide thereby putting millions of livelihoods at risk especially in sub-Saharan Africa. This makes drought preparedness critical. In this study, we sought to establish whether maize yield can be predicted using the number of dry dekads that occur at specific maize growth stages for purposes of yield early warning. The dry dekads were derived from remotely sensed Vegetation Condition Index calculated from the SPOT NDVI time series ranging from 1998 to 2013. Regression between dry dekads and maize yield show a negative linear relationship for four growing seasons (2010–2013) and indicates that dry dekads at both the vegetative and reproductive stages are important for predicting maize yield. Results suggest that early warning alert could be given using dry dekads that occur at the vegetative stage, while those at the reproductive stage can be used to give better yield estimate later on.     

Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor

Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R² of 0.67 and a RMSE-CV of 2.2 t ha^?1) when compared to when the red-edge band was included as a co-variate (R² of 0.73 and a RMSE-CV of 2.04 t ha^?1). Also, similar results were obseved when all WorldView-2 vegetation indices (without the red-edge computed ones), producing slightly low accuracies (R² of about 0.67 and a RMSE-CV of 2.20 t ha^?1), when compared to those obtained using all indices and RE-computed indices(R² of 0.76 and a RMSE-CV of 1.88 t ha^?1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint.