Abel Ramoelo - Academia.edu (original) (raw)

Papers by Abel Ramoelo

Geocarto International, Sep 27, 2021

International Journal of Remote Sensing, Apr 18, 2023

Semiarid regions shaped as a mosaic of savanna-type rangelands, croplands, and other uses such as... more Semiarid regions shaped as a mosaic of savanna-type rangelands, croplands, and other uses such as livelihoods, or natural reserves, cover large areas in Southern Africa. They constitute an essential example of multiple uses of natural resources, combining a high environmental value with great importance in the rural economy and development. These systems are water-limited and highly sensitive to changes in climate, environmental conditions, and land management practices. Although the vegetation of these areas is adapted to variable climatic conditions and dry periods, the increase in drought intensity, duration, and frequency precipitate their degradation. In Southern Africa, recurrent droughts have strained rainfed agriculture and pasture production, decimating livestock and wildlife. During 2015 and 2016, South African savannas were subjected to a severe drought associated with a strong El Niño event. Open-source satellite time series provide vital information to assess water availability and long-term drought, to help design early warning and conservation strategies. In this work, we applied the TSEB (Two Source Energy Balance) model integrating MODIS-derived products (1 km) from 2000 to 2021 over the Kruger National Park (KNP) in South Africa. The model was previously validated over the Skukuza experimental site with good agreement. ET followed precipitation rates, although some years with low precipitation maintained average ET values. This may be caused by the ability of the trees to reach groundwater (deep fractured aquifers and alluvial aquifers can be found in the KNP). During some years (e.g., 2004, 2009), annual total ET was much higher than mean annual values. This may be caused by an extreme annual evaporative atmospheric demand and relatively high precipitation. The anomalies of the ratio of ET to reference ET were used as an indicator of agricultural drought on annual scales, and 2002/2003, 2007/2008 and 2015/2016 years stood out for their negative values. The approach helped to model drought over Kruger Park in the long term, providing an insight into the characteristics of the events.Acknowledgment: This work has been carried out through the project "DroughT impACt on the vegeTation of South African semIarid mosaiC landscapes: Implications on grass-crop-lands primary production" funded by the European Space Agency in the framework of the "EO AFRICA R&D Facility".

9th International Conference of the African Association of Remote Sensing and the Environment, El... more 9th International Conference of the African Association of Remote Sensing and the Environment, El Jadida Morocco, October 29 to November 2, 2012

Proceedings of SPIE, Nov 11, 2014

Sentinel-2 is intended to improve vegetation assessment at local to global scales. Today, estimat... more Sentinel-2 is intended to improve vegetation assessment at local to global scales. Today, estimation of leaf nitrogen (N) as an indicator of rangeland quality is possible using hyperspectral systems. However, few studies based on commercial imageries have shown a potential of the red-edge band to accurately predict leaf N at the broad landscape scale. We intend to investigate the utility of Sentinel-2 for estimating leaf N concentration in the African savanna. Grass canopy reflectance was measured using the analytical spectral device (ASD) in concert with leaf sample collections for leaf N chemical analysis. ASD reflectance data were resampled to the spectral bands of Sentinel-2 using published spectral response functions. Random forest (RF), partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) were used to predict leaf N using all 13 bands. Using leave-one-out cross validation, the RF model explained 90% of leaf N variation, with a root mean square error of 0.04 (6% of the mean), which is higher than that of PLSR and SMLR. Using RF, spectral bands centered at 705 nm (red edge) and two shortwave infrared bands centered at 2190 and 1610 nm were found to be the most important bands in predicting leaf N.

Flux tower data are in high demand to provide essential terrestrial climate, water and radiation ... more Flux tower data are in high demand to provide essential terrestrial climate, water and radiation budget information needed for environmental monitoring and evaluation of climate change impacts on ecosystems and society in general. They are also intended for calibration and validation of satellite-based earth observation and monitoring efforts, such as for example assessment of evapotranspiration from land and vegetation surfaces using surface energy balance approaches. Surface energy budget methods for ET estimation rely to a large extend on the basic assumption of a surface energy balance closure, assuming the full conversion of net solar radiation reaching the land surface into soil heat conduction and turbulent fluxes, i.e. the sensible (or convection) and latent heat components of the energy balance. In this paper, the Skukuza flux tower data were analysed in order to verify their use for validation of satellite-based evapotranspiration methods, under development in South Africa. Data series from 2000 until 2014 were used in the analysis. The energy balance ratio (EBR) concept, defined as the ratio between the sum of the turbulent convective and latent heat fluxes and radiation minus soil heat was used. Then typical diurnal patterns of EB partitioning were derived for four different seasons, well illustrating how this savannah-type biome responds to weather conditions. Also the particular behaviour of the EB components during sunrise and sunset conditions, being important but usually neglected periods of energy transitions and inversions were noted and analysed. Annual estimates and time series of the surface energy balance and its components were generated, including an evaluation of the balance closure. The seasonal variations were also investigated as well as the impact of nocturnal observations on the overall EB behaviour. Introduction The net solar radiation (Rn) reaching the earth's surface determines the amount of energy available for transformation into energy balance components, i.e. latent (LE), sensible (H) and ground (G) heat fluxes, including heat stored by the canopy and the ground. Energy partitioning on the earth's surface is a function of interactions between biogeochemical cycling, plant physiology, the state of the atmospheric boundary layer and climate (Wilson et al., 2002). How the turbulent fluxes (sensible and latent heat fluxes) are partitioned in an ecosystem plays a critical role in determining the hydrological cycle, boundary layer development, weather and climate (Falge et al., 2005). Understanding the partitioning of energy, particularly the turbulent fluxes, is important for water resource management in (semi) arid regions, where potential evapotranspiration far exceeds precipitation. Eddy covariance (EC) systems are currently the most reliable method for measuring carbon, energy and water fluxes, and they have become a standard technique in the study of surface-atmosphere boundary layer interactions. Hence, they provide a distinct contribution to the study of environmental, biological and 1

International journal of applied earth observation and geoinformation, Apr 1, 2018

Remote sensing applications in biodiversity research often rely on the establishment of relations... more Remote sensing applications in biodiversity research often rely on the establishment of relationships between spectral information from the image and tree species diversity measured in the field. Most studies have used normalized difference vegetation index (NDVI) to estimate tree species diversity on the basis that it is sensitive to primary productivity which defines spatial variation in plant diversity. The NDVI signal is influenced by photosynthetically active vegetation which, in the savannah, includes woody canopy foliage and grasses. The question is whether the relationship between NDVI and tree species diversity in the savanna depends on the woody cover percentage. This study explored the relationship between woody canopy cover (WCC) and tree species diversity in the savannah woodland of southern Africa and also investigated whether there is a significant interaction between seasonal NDVI and WCC in the factorial model when estimating tree species diversity. To fulfil our aim, we followed stratified random sampling approach and surveyed tree species in 68 plots of 90m X 90m across the study area. Within each plot, all trees with diameter at breast height of >10cm were sampled and Shannon index-a common measure of species diversity which considers both species richness and abundance-was used to This article was published in JAG: https://www.sciencedirect.com/science/article/pii/S0303243417302568 quantify tree species diversity. We then extracted WCC in each plot from existing fractional woody cover product produced from Synthetic Aperture Radar (SAR) data. Factorial regression model was used to determine the interaction effect between NDVI and WCC when estimating tree species diversity. Results from regression analysis showed that (i) WCC has a highly significant relationship with tree species diversity (r 2 = 0.21; p < 0.01), (ii) the interaction between the NDVI and WCC is not significant, however, the factorial model significantly reduced the error of prediction (RMSE = 0.47, p <0.05) compared to NDVI (RMSE = 0.49) or WCC (RMSE = 0.49) model during the senescence period. The result justifies our assertion that combining NDVI with WCC will be optimal for biodiversity estimation during the senescence period.

International journal of applied earth observation and geoinformation, Jun 1, 2015

Indigenous forest biome in South Africa is highly fragmented into patches of various sizes (most ... more Indigenous forest biome in South Africa is highly fragmented into patches of various sizes (most patches < 1 km 2). The utilization of timber and non-timber resources by poor rural communities living around protected forest patches produces subtle changes in the forest canopy which can be hardly detected on a timely manner using traditional field surveys. The aims of this study were to assess: (i) the utility of very high resolution (VHR) remote sensing imagery (WorldView-2, 0.5 to 2 m spatial resolution) for mapping tree species and canopy gaps in one of the protected subtropical coastal forests in South Africa (the Dukuduku forest patch (ca.3200 ha) located in the province of KwaZulu-Natal) and (ii) the implications of the map products to forest conservation. Three dominant canopy tree species namely, Albizia adianthifolia, Strychnos spp. and Acacia spp., and canopy gap types including bushes (grass/shrubby), bare soil and burnt patches were accurately mapped (overall accuracy = 89.3 ±2.1%) using WorldView-2 image and support vector machine classifier. The maps revealed subtle forest disturbances such as bush encroachment and edge effects resulting from forest fragmentation by roads and a power-line. In two stakeholders' workshops organised to assess the implications of the map products to conservation, participants generally agreed amongst others implications that the VHR maps provide valuable information that could be used for implementing and monitoring the effects of rehabilitation measures. The use of VHR imagery is recommended for timely inventorying and monitoring of the small and fragile patches of subtropical forests in Southern Africa.

International journal of applied earth observation and geoinformation, Dec 1, 2015

Land use and climate change could have huge impacts on food security and the health of various ec... more Land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) and above-ground biomass are some of the key factors limiting agricultural production and ecosystem functioning. Leaf N and biomass can be used as indicators of rangeland quality and quantity. Conventional methods for assessing these vegetation parameters at landscape scale level is time consuming and tedious. Remote sensing provides a bird-eye view of the landscape, which creates an opportunity to assess these vegetation parameters over wider rangeland areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The estimation of above-ground biomass has been hindered by the signal saturation problems using conventional vegetation indices. The objective of this study is to monitor leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and random forest technique in the northeastern part of South Africa. Series of field work to collect samples for leaf N and biomass were undertaken in March 2013, April or May 2012 (end of wet season) and July 2012 (dry season). Several conventional and red edge based vegetation indices were computed. Overall results indicate that random forest and vegetation indices explained over 89% of leaf N concentrations for grass and trees, and less than 89% for all the years of assessment. The red edge based vegetation indices were among the important variables for predicting leaf N. For the biomass, random forest model explained over 84% of biomass variation in all years, and visible bands including red edge based vegetation indices were found to be important. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring.

The performance of PROSPECT-5 radiative transfer model for predicting leaf chlorophyll from refle... more The performance of PROSPECT-5 radiative transfer model for predicting leaf chlorophyll from reflectance measurements made with the Analytical Spectral Device (ASD) spectrometer was investigated using numerical inversion techniques. The reflectance data of various spectral regions in the visible to shortwave infrared (SWIR) were assessed i.e. the full range (400-2500 nm), VNIR (400-1060 nm), visible (400-700 nm), red (600-700 nm), red-red edge (600-760 nm) and red-edge (670-760 nm). Among the spectral regions, the red-edge region yielded the lowest root mean square error of prediction (RMSEP =10.3 μg/cm2) for a variety of plants including crops and wild plants (n = 463). It is therefore recommended that inversion of radiative transfer models to retrieve leaf chlorophyll content be limited to the red-edge region.

Remote Sensing, Jul 4, 2017

Understanding the spatio-temporal dynamics of land surface phenology is important to understandin... more Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface phenological response in the semi-arid savanna of Southern Africa. Various land surface phenological metrics for the green-up and senescing periods of the vegetation were retrieved from leaf index area (LAI) seasonal time series (2001 to 2015) maps for a study region in South Africa. Tree cover (%) data for 100 randomly selected polygons grouped into three tree cover classes, low (<20%, n = 44), medium (20-40%, n = 22) and high (>40%, n = 34), were used to determine the influence of varying tree cover (%) on the phenological metrics by means of the t-test. The differences in the means between tree cover classes were statistically significant (t-test p < 0.05) for the senescence period metrics but not for the green-up period metrics. The categorical data results were supported by regression results involving tree cover and the various phenological metrics, where tree cover (%) explained 40% of the variance in day of the year at end of growing season compared to 3% for the start of the growing season. An analysis of the impact of rainfall on the land surface phenological metrics showed that rainfall influences the green-up period metrics but not the senescence period metrics. Quantifying the contribution of tree cover to the day of the year at end of growing season could be important in the assessment of the spatial variability of a savanna ecological process such as the risk of fire spread with time.

Remote Sensing, Sep 16, 2016

Separation of savanna land cover components is challenging due to the high heterogeneity of this ... more Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna.

Journal of Applied Remote Sensing, Aug 7, 2015

Journal of Agribusiness and Rural Development, Apr 4, 2022

Large-scale farming relies on favourable land tenure systems. However, conflicting land tenure is... more Large-scale farming relies on favourable land tenure systems. However, conflicting land tenure is affecting agribusiness development in Sub-Saharan Africa. A key question is whether Côte d'Ivoire, the world's leading producer of cocoa, has been spared from the challenge of rampant land tenure facing commercial farming. This paper is a reflection on the consequences of legal pluralism on the development of agribusiness. Through a case study of a region of southeastern Cote d'Ivoire, it intends to demonstrate that the coexistence of neo-customary and bureaucratic forms of land tenure constitutes a major obstacle for agribusiness development. Qualitative methods were employed, including individual interviews and focus group discussions. The results reveal that land tenure systems are intricately linked to the complexity of agribusiness development. The study further finds that land tenure systems are a source of conflict between agribusiness developers and smallholders. Hence, agribusiness finds it difficult to grow due to land tenure systems, which cause immense hardships for agribusiness developers in South Comoé. The case of the South Comoé region, therefore, articulates a compelling need for policymakers to consolidate the land tenure system which has failed to secure land for agribusiness development.

Environmental Monitoring and Assessment, Jun 10, 2021

South Africa is a custodian of an immense wealth of natural and biodiversity resources in Africa.... more South Africa is a custodian of an immense wealth of natural and biodiversity resources in Africa. Natural resources are continually changing in different South African biospheres based on anthropogenic and non-anthropogenic causes. Land use activities like agriculture, cultivation, livestock rearing, commercial plantations, urbanisation and mining are among the major drivers of natural resource change and transformation. In this study, land cover change assessment was used to assess natural resource change in Vhembe biosphere and surroundings. To assess natural resource change in Vhembe biosphere, land use land cover change assessment was conducted using South African’s national land-cover dataset, generated from multi-seasonal Landsat 5 and Sentinel-2 images. The 72× class land cover map was re-classified into 12× classes to fit the study objectives. Eight out of twelve classes quantified in hectares: indigenous forests, thicket/dense bush, natural woodland, shrubland, grassland, water bodies and wetlands were categorised as natural resources for which the natural resource change assessment for this study was based. Assessment findings established that land use and its related activities have contributed substantially to natural resource change where cultivated commercial, natural woodland and built-up residential contributed the most significant upward change in hectarage and percentage, from 132,246.9 to 365,644.92 (ha)—percentage change of 176%; from 94,665.42 to 257,889.68 (ha)—percentage changes of 172% and from 74,070.27 to 147,701.88(ha)—percentage change of 99% respectively. Shrubland, thicket/dense bush and indigenous forests registered the highest downward changes from 263,070.6 to 977.72 (ha); from 338,723.7 to 23,166.92 and from 13,211.91 to 7402.92 (ha) with percentage changes of −100%, −93% and −44% respectively in Vhembe biosphere and the surroundings from 1990 to 2018. The study showed how natural resources are changing and the use of remote sensing for environmental monitoring and assessment in the Vhembe district.

Applied Geomatics, Feb 21, 2023

Woody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding ... more Woody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding vegetation dynamics. The lack of accurate calibration and validation datasets for reliable modelling of CC in the indigenous forests in South Africa contributes to uncertainties in carbon stock estimates and limits our understanding of how they might influence long-term climate change. The aim of this study was to develop a method for monitoring CC in the Dukuduku indigenous forest in South Africa. Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics of 2008, 2015, and 2018, polarimetric features, and Grey Level Co-occurrence Matrix (GLCMs) were used. Machine learning models Random Forest (RF) vs Support Vector Machines (SVM) were developed and calibrated using Collect Earth Online (CEO) data, a free and open-access land monitoring tool developed by the Food and Agriculture Organisation (FAO). The addition of GLCMs produced the highest accuracy in 2008, R 2 (RMSE) = 0.39 (36.04%), and in 2015, R 2 (RMSE) = 0.51 (27.82%), and in 2018, only SAR variables gave the highest accuracy R 2 (RMSE) = 0.55 (29.50). The best-performing models for 2008, 2015, and 2018 were based on RF. During the ten-year study period, shrubland and wooded grassland had the highest transition, at 6% and 13%, respectively. The observed changes in the different canopies provide valuable insights into the vegetation dynamics of the Dukuduku indigenous forest. The modelling results suggest that the CEO calibration data can be improved by integrating airborne LiDAR data.

Grass quality and quantity information plays a crucial role in understanding the distribution, de... more Grass quality and quantity information plays a crucial role in understanding the distribution, densities and population dynamics of herbivores (i.e. livestock and wildlife). Leaf nitrogen (N) and biomass (g/ m 2) are indicators of grass quality and grass quantity, respectively. The objective of the study is to estimate and map leaf N and biomass as an indicator of rangeland quality and quantity using vegetation indices derived from one RapidEye image taken at peak productivity. The study was undertaken in the northeastern part of South Africa, in a transect extending from protected areas such as Kruger National Park and a privately owned game reserve to the communal areas of Bushbuckridge. Field work was undertaken to collect data on biomass and grass samples for retrieving leaf N, in April 2010, same time with image acquisition. RapidEye image was atmospherically corrected using atmospheric correction software for flat surfaces (ATCOR 2). Environmental or ancillary data sets were also collected from various sources as to develop an integrated modeling approach with the remotely-sensed indices. Commonly used vegetation index such as simple ratio was used exploiting a new red-edge band embedded in the RapidEye sensor. Leaf N regression models were developed using simple regression. Biomass (g/m 2) prediction models were developed by applying bootstrapped stepwise regression using a combination of vegetation index and environmental or ancillary variables. Simple ratio (SR54) based on red-edge band produced higher grass N estimation accuracy. For the biomass estimation, vegetation indices produced poor results explaining less than 15% of variation. Biomass estimation was significantly improved to 27% of explained biomass variation by integrating vegetation index (SR54) and ancillary data. The latter approach is crucial because biomass is influenced by various environmental variables, which therefore play a crucial role in model development. The study demonstrated a potential of forage quantity and quality estimation using new high spatial remote sensing data with the red edge band. Integrating vegetation indices and ancillary data provides an opportunity to map grass biomass during peak productivity. Forage quality and quantity information is crucial for planning and management of grazing resources.

For grazing, biomass is the main indicator of rangeland quantity, which is crucial to determine t... more For grazing, biomass is the main indicator of rangeland quantity, which is crucial to determine the amount of food available for animals (grazers), including livestock. Livestock production in the rural communities of the world, including Africa, is the main source of income and hence livelihood. Biomass information during dry season is not only important for grazing but also for determining the fuel load for fire risk. During dry season, grazers are mainly limited by grass quantity than quality. Therefore, it is important to quantify the variability of biomass during dry season to inform decision makers on planning and management of the grazing systems. Remote sensing provides opportunity to successfully estimate biomass in natural and agricultural areas. The conventional approach makes use of the vegetation indices such as the normalized difference vegetation index (NDVI), which is a measure of vegetation greenness. The use of vegetation indices has been successful during wet periods where vegetation is green and photosynthetic active. During dry season, biomass estimation is always not plausible using vegetation indices. The aim of this study is to estimate dry biomass using the multi-scale remote sensing data in the savanna ecosystem. Field data was collected in August 2013, and concerted to the acquisition of the satellite image from RapidEye and Landsat 8. Random forest algorithm (RF) was used to predict biomass using the band reflectance data, from RapidEye and Landsat 8 respectively. The results show that RF combined with RapidEye explained over 85% of biomass variation, as compared to 81% explained by RF with Landsat 8 data. For regional assessment of biomass as an indicator of rangeland quantity, high spatial resolution data can be used for calibration and validation. This study demonstrates that dry season biomass can be estimated using remote sensing, and it is important for understanding grazing and feeding patterns of animals, including livestock and wildlife.