Mbulisi Sibanda

The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa

In this study, we test whether we can significantly (p < 0.05) distinguish cotton (Gossypium hirsutum L.) fields from maize (Zea mays L.) and sorghum (Sorghum bicolor) fields in smallholder agricultural landscapes of the Mid-Zambezi Valley, Zimbabwe, using a temporal series of 16-day Moderate Resolution Imaging Spectroradiometer – normalized difference vegetation index (MODIS NDVI) data. We test this hypothesis at different phenological stages over the growing season, that is, early green-up onset, late green-up onset, green-peak, early senescence and late senescence. We also statistically compare the rate of change in the greenness of the three crops at the three phenological stages. Results show that we can significantly (p < 0.05) distinguish cotton fields from maize and sorghum fields using 16-day MODIS NDVI data during the late green-up onset as well as during the green-peak stage of the three crops. Our results indicate that cotton can successfully be distinguished from maize and sorghum in spatially heterogeneous smallholder agricultural landscapes using temporal MODIS NDVI.

Discriminating Rangeland Management Practices Using Simulated HyspIRI, Landsat 8 OLI, Sentinel 2 MSI, and VENµS Spectral Data

This study sought to spectrally discriminate grasses grown under different management practices (i.e., mowing, grazing, fertilizer application, and burning), using field spectrometer data resampled to Hyperspectral Infrared Imager (HyspIRI), Landsat 8 Operational Land Imager (OLI), Sentinel 2 Multispectral Instrument (MSI), and Vegetation and Environment monitoring on a new MicroSatellite (Venμs). The study is inspired by the long standing challenge of lack of suitable satellite data with high temporal, spectral, and spatial resolutions for rangelands monitoring. Specifically, this study spectrally discriminated grasses grown under 1) different rangeland management practices as well as 2) different levels of application of each practice. Results of this study show that the spectral setup of HyspIRI, Sentinel 2 MSI, Venus, and Landsat 8 OLI yielded high accuracies of up to 92%, 82%, 83%, and 75% overall accuracy, respectively, in discriminating grass grown under different rangeland management practices. The high classification accuracies were exhibited by the use of vegetation indices and wavebands located in the red edge (HyspIRI: 700, 740, and 780 nm and Sentinel 2 MSI: bands 5, 6, and 7 8a) and NIR (HyspIRI: 700, 740, and 780 nm and Sentinel 2 MSI: band 8a) spectra, respectively. Results of this study illustrate that although simulated Sentinel 2 MSI data yields lower classification accuracies when compared to HyspIRI, it offers better classification accuracies with high agreements between training and testing datasets when compared to the HyspIRI data. Overall, the findings of this study underscore the potential of upcoming satellite missions in ensuring informed rangeland monitoring and management applications.

Exploring the potential of in situ hyperspectral data and multivariate techniques in discriminating different fertilizer treatments in grasslands

Abstract. Optimizing the productivity of native rangelands has received considerable attention in range management. Rangeland fertilizer application has emerged as a popular intervention for improving rangeland quality. To achieve optimal range quality from such intervention, there is a need for quick and accurate methods of assessing the effects of different fertilizer combinations. The utility of in situ hyperspectral data and multivariate techniques in distinguishing 12 complex ammonium nitrate, ammonium sulfate, lime, and phosphorus fertilizer combinations on a grassland is assessed. Partial least squares regression discriminant analysis (PLS-DA) and discriminant analysis (DA) classification results derived using hyperspectral grass reflectance that were (1) fertilized using 11 combinations of ammonium sulfate, ammonium nitrate, phosphorus, and lime and (2) unfertilized experimental plots were compared. Results illustrate the strength of in situ hyperspectral data and multivariate techniques in detecting and discriminating grasses with different fertilizer treatments. Specifically, four bands within the red edge (731 and 737 nm) and the shortwave infrared (1310 and 1777 nm) regions of the electromagnetic spectrum demonstrated a high potential for discriminating the effects of fertilizer treatments on grasslands. DA outperformed PLS-DA in discriminating complex combinations of ammonium nitrate, ammonium sulfate combined with lime and phosphorus, as well as unfertilized grasses. Overall, spectroscopy and DA offer great potential for discriminating complex fertilizer combinations.

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m2, R2 = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m2, R2 = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.

An assessment of gully erosion along major armoured roads in south-eastern region of South Africa: a remote sensing and GIS approach

An assessment of gully erosion along road drainage-release sites is critical for understanding the contribution of roads to soil loss and for informed land management practices. Considering that road-related gully erosion has traditionally been measured using field methods that are expensive, tedious and limited spatially as well as temporally, it is important to identify affordable, timely and robust methods that can be used to effectively map and estimate the volume of gullies along the road networks. In this study, gullies along major roads were identified from remotely sensed data sets and their volumes were estimated in a Geographic Information Systems environment. Also, the biophysical and climatic factors such as vegetation cover, the road contributing surface area, the gradient of the discharge hillslope and rainfall were derived from remotely sensed data sets using Geographic Information Systems techniques to find out whether they could explain the morphology of gullies that existed in this area. The results of this study indicate that hillslope gradient (R2 = 0.69, α = 0.00) and road contributing surface area (R2 = 0.63, α = 0.00) have a strong influence on the volume of gullies along the major roads in the south-eastern region of South Africa, as might have been expected. However, other factors such as vegetation cover (R2 = 0.52, α = 0.00) and rainfall (R2 = 0.41 and α = 0.58) have a moderately weaker influence on the overall volume of gullies. Overall, the findings of this study highlight the importance of using remote sensing and Geographic Information Systems technologies in investigating gully erosion occurrence along major roads where detailed field work remains a challenge.

Applying the Surface Energy Balance System (SEBS) remote sensing model to estimate spatial variations in evapotranspiration in Southern Zimbabwe

Accurate, reliable and continuous understanding of water utilisation by different land cover types in arid environments is critical for water loss accounting to ensure sustainable water management in the face of the changing climate. Remote sensing provides a lucrative alternative for mapping and estimating the spatial and temporal distribution of water loss across the catchment. The results of this study have shown that evapotranspiration (ET) can be accurately estimated in arid environments from remotely sensed data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) data, based on the Surface Energy Balance System (SEBS) algorithm. This study observed significant spatial and temporal variations in ET across the south western part of Zimbabwe. The findings from this study, therefore, underscore the importance of using cheap and readily available remotely sensed data for estimating and mapping the variations in ET in arid-environment areas found mainly in developing countries.

Cotton fields drive elephant habitat fragmentation in the Mid Zambezi Valley, Zimbabwe

Abstract In this study we tested whether cotton fields contribute more than cereal fields to African elephant ( Loxodonta africana ) habitat loss through its effects on woodland fragmentation in the Mid-Zambezi Valley, Zimbabwe. In order to test this hypothesis, we first mapped cotton and cereal fields using MODIS remotely sensed data. Secondly, we analysed the effect of the area of cotton and cereal fields on woodland fragmentation using regression analysis. We then related the fragmentation indices, particularly edge density with elephant distribution data to test whether elephant distribution was significantly related with woodland fragmentation resulting from cotton fields. Our results showed that cotton fields contributed more to woodland fragmentation than cereal fields. In addition, results showed that the frequency of the African elephant increased where cotton fields were many and small relative to cereal fields. We concluded that cotton fields are the main driver of woodland fragmentation and therefore elephant habitat in the Mid-Zambezi Valley compared with cereal fields.

Testing the capabilities of the new WorldView-3 space-borne sensor’s red-edge spectral band in discriminating and mapping complex grassland management treatments

ABSTRACT The majority of grasslands are overused and poorly managed, globally. The overuse of these grasslands has resulted in the adoption of numerous management treatments as interventions for optimizing their productivity. However, there are limited comprehensive frameworks and objective precedents for monitoring these grasslands and rangelands. In that regard, understanding the effect of such rangeland management treatments on grassland productivity is, therefore, a critical step towards their effective conservation and sustainable management. This study sought to test the capabilities of the WorldView-3 (WV-3) satellite data derivatives in characterizing grasslands administered with different rangeland management treatments (i.e. mowing, grazing, burning, fertilizer application, and control: no-treatment), using discriminant analysis. We compared the accuracies obtained based on WV-3 standard visible and near-infrared bands and vegetation indices (VIs), excluding and including the red-edge. Results illustrate that incorporating the strategically positioned red-edge band improves the classification accuracy of the four different rangeland management treatments from 65% to 70%. Furthermore, the overall accuracy was 73% when standard VIs were used and it increased to 78% when the red-edge VIs were added to standard VIs. Other than the red-edge derivatives, the results of this study showed that the yellow, red, NIR-1, and NIR-2 bands were the most influential. The utility of fine spatial resolution sensors such as the newly launched WV-3, with strategically positioned bands (red-edge), could offer detailed information essential for the sustainable management of grasslands.

Assessing the aftermath of the fast track land reform programme in Zimbabwe on land-use and land-cover changes

An understanding of the role of land policies as major drivers behind tree species diversity reduction in southern Africa is still rudimentary. This study, therefore, sought to assess the aftermath of the fast track land reform programme in Zimbabwe on land-use and land-cover changes. Specifically, we characterised the spatio-temporal changes in land-cover between 2000 and 2010 as a result of the land reform policies in Quagga Pan Ranch, Masvingo Province, Zimbabwe. Secondly, we assessed the effects of the fast track land reform programme of 2000 on tree species diversity. Finally, we attempted to establish whether there was selective logging in newly resettled areas and also sought to understand the reasons behind selective tree logging. Land-cover changes were characterised after classifying Landsat satellite images of 2000 and 2010. Tree species data were collected using quadrats in newly resettled and unsettled areas for determining the state of selective logging. Questionnaires and observations were used to understand the patterns of diversity loss. Results show significant changes in land-use and land-cover between 2000 and 2010 with an increase in agricultural areas and a decrease in woodlands, specifically in newly resettled areas. Significant differences (α < 0.05) were noted between tree species diversity in areas that have been resettled and in areas that have not been resettled. Tree species diversity is relatively very high in areas that have not been resettled and very low in areas that have been resettled. Colophospermum mopane is the most used tree species because its a cheap source of fuel and its durable in domestic uses. It can be concluded that agricultural expansion is necessary for sustaining livelihoods as long as proper conservation methods of the tree species and the environment are maintained.

The utility of earth observation technologies in understanding impacts of land reform in the eastern region of Zimbabwe

ABSTRACT Land tenure and land policies influence the spatial variations of land use/cover (LULC) at any given time or place. Thus, it is important to evaluate the role of land tenure policies on land cover changes. In this study, we evaluate the utility of Landsat Thematic Mapper (TM) images in understanding the impacts of the 2000 fast track land reform (FTLR) policy on LULC in the eastern region, Zimbabwe. Landsat images for the year 1995, 2000, 2005 and 2011 were classified using traditional image classification techniques (i.e. the maximum likelihood (ML) classifier) in a geographic information system (GIS) environment. Results indicate that forested areas drastically decreased by approx. 30% between the year 2000 and 2005 (during and after the FTLR), while croplands marginally increased by (approx. 30%) the results further showed that slight increase in bare lands (degraded lands) and disturbed lands. The observed LULC changes after FTLR were mostly induced by human activities resulting from changes in land tenure. Overall, the findings of this study underscores the importance of remotely sensed data in assessing the impact of FTLR on forest resources for purposes of informed and sustainable forest management.