Remote sensing grass quantity under different grassland management treatments practised in the Southern African rangelands.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file

Assessing the Drivers of Wetland Changes in Areas Associated with Wildlife-Based Tourism Activities in Zimbabwe

The study assesses wetland land cover changes associated with high wildlife densities and tourism activities in Dete vlei, located in Sikumi protected forest, adjacent to Hwange National Park, Zimbabwe. The vlei is used for photographic safaris and is associated with high number of tourists visiting the wetland to see a variety of wildlife species congregated in it. On-screen digitization and analysis of SPOT images for the period of 1984–2013 was used to determine land cover changes in the wetland. Field data were collected through observations, measurements and semi-structured interviews with key informants. The results of the study showed that the spatial extent of bare areas increased in the lower section of the vlei after the establishment of salt licks and watering points meant to attract many wild animals during the dry season. In contrast, wetland conditions have been expanding in the upper section of the wetland without artificial salt licks and watering points. Tourists’ footpaths, road culverts, unplanned vehicles’ roads, to mention a few, contribute to erosional features evident in the wetland. The study recommends the introduction of wildlife-based tourism management strategies in seasonal wetlands to minimise degradation and possibly loss of wetlands

Crop monitoring in smallholder farms using unmanned aerial vehicles to facilitate precision agriculture practices: A scoping review and bibliometric analysis

In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools to monitor crops and guide PA practices to improve agricultural productivity and promote the sustainable and optimal use of critical resources. However, there is a need to understand how and for what purposes these technologies are being applied within smallholder farms. Using Biblioshiny and VOSviewer, 23 peer-reviewed articles from Scopus andWeb of Science were analyzed to acquire a greater perspective on this emerging topical research focus area

Establishing the link between urban land cover change and the proliferation of aquatic hyacinth (Eichhornia crassipes) in Harare Metropolitan, Zimbabwe

Urbangrowthisakeyprocessaffectingthefunctioningofnaturalecosystems,andconsequentlythegloballand-surface process. This work aimed at establishing the link between land cover changes around HarareMetropolitancityandtheproliferationofaquatichyacinth(Eichhornia crassipes)inLakeChivero.RemotelysensedLandsatseriesacquiredintheyear1973,1981,1994,1998,2008,2009and2014wasused.Imageclassificationwasimplementedtomaptheassociatedchangesovertimeusingdiscriminantanalysisalgorithm.Derivedthematiclandcovermapsshowedthatagriculturallandincreasedfrom2%in1973toa5%in1981reachingupto30%in2014,whereasthecity'slandareasignificantly(p<0.05)increasedbetween1973and1994.However,waterhyacinthconstantlyincreasedovertime.ThespatialandtemporalresolutionofLandsatimagesdetectedlandcoverchangesandtheproliferationofaquatichyacinth(Eichhorniacrassipes)intheLakeChiveroovertime

Assessing the prospects of remote sensing maize leaf area index using uav-derived multi-spectral data in smallholder farms across the growing season

Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images

Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an unmanned aerial vehicle (uav) platform

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle

Estimating and monitoring land surface phenology in rangelands: A review of progress and challenges

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment

Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: A case of KwaZulu-Natal, South Africa

Knowing the spatial and temporal suitability of neglected and underutilised crop species (NUS) is important for fitting them into marginal production areas and cropping systems under climate change. The current study used climate change scenarios to map the future distribution of selected NUS, namely, sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth (Amaranthus) and taro (Colocasia esculenta) in the KwaZulu-Natal (KZN) province, South Africa. The future distribution of NUS was simulated using a maximum entropy (MaxEnt) model using regional circulation models (RCMs) from the CORDEX archive, each driven by a different global circulation model (GCM), for the years 2030 to 2070. The study showed an increase of 0.1–11.8% under highly suitable (S1), moderately suitable (S2), and marginally suitable (S3) for sorghum, cowpea, and amaranth growing areas from 2030 to 2070 across all RCPs. In contrast, the total highly suitable area for taro production is projected to decrease by 0.3–9.78% across all RCPs. The jack-knife tests of the MaxEnt model performed efficiently, with areas under the curve being more significant than 0.8. The study identified annual precipitation, length of the growing period, and minimum and maximum temperature as variables contributing significantly to model predictions

A comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (uav)-based proximal and remotely sensed data

: Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients