10 research outputs found
Assessing the influence of DEM source on derived streamline and catchment boundary accuracy
Accurate DEM-derived streamlines and catchment boundaries are essential for hydrological modelling. Due to the popularity of hydrological parameters derived mainly from free DEMs, it is essential to investigate the accuracy of these parameters. This study compared the spatial accuracy of streamlines and catchment boundaries derived from available digital elevation models in South Africa. Two versions of Stellenbosch University DEMs (SUDEM5 and DEMSA2), the second version of the 30 m advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM2), the 30 and 90 m shuttle radar topography mission (SRTM30 and SRTM90 DEM), and the 90 m Water Research Commission DEM (WRC DEM) were considered. As a reference, a 1 m GEOEYE DEM was generated from GeoEye stereo images. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module. A reference catchment boundary was generated from the GEOEYE DEM and verified during field visits. Reference streamlines were digitised at a scale of 1:10 000 from the 1 m orthorectified GeoEye images. Visual inspection, as well as quantitative measures such as correctness index, mean absolute error, root mean squares error and figure of merit index were used to validate the results. The study affirmed that high resolution (<30 m) DEMs produce more accurate parameters and that DEM source and resampling techniques also play a role. However, if high resolution DEMs are not available, the 30 m SRTM DEM is recommended as its vertical accuracy was relatively high and the quality of the streamlines and catchment boundary was good. In addition, it was found that the novel Euclidean distancebased MAE and RMSE proposed in this study to compare reference and DEM-extracted raster datasets of different resolutions is a more reliable indicator of geometrical accuracy than the correctness and figure of merit indices.Keywords: hydrology, catchment delineation, digital elevation model, correctness index, figure of merit index, Euclidean distance inde
A synthesizing land-cover classification method based on Google Earth Engine : a case study in Nzhelele and Levhuvu catchments, South Africa
CITATION: Zeng, H. et al. 2020. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science. 30: 397–409. doi:10.1007/s11769-020-1119-yThe original publication is available at https://www.springer.com/journal/11769This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Google Earth Engine (GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) have little effect on final land-cover classification result.https://link.springer.com/article/10.1007/s11769-020-1119-yPublishers versio
Survey of community livelihoods and landscape change along the Nzhelele and Levuvhu river catchments in the Limpopo Province, South Africa
Abstract:Landscape change studies have attracted increasing interest because of their importance 29 to land management and sustainable livelihoods of rural communities. However, empirical studies 30 on landscape change and its drivers are often poorly understood, particularly, in small rural 31 communities in developing countries such as South Africa. The present study surveyed local 32 community livelihoods and perceptions of landscape change in the Nzhelele and Levuvhu river 33 catchments in Limpopo Province, South Africa. These areas have experienced land reform and are 34 also characterized by environmental degradation, poverty, inequality and environmental justice 35 concerns among other issues. Land cover maps derived from Landsat satellite imagery were used 36 for purposes of correlating and validating the survey data findings and results. The survey results 37 showed that education levels, working status and marital status have statistically significant effects 38 on community livelihoods (indicated by levels of income, p < 0.05). Maize, fruits and vegetables are 39 the main cultivated crop varieties in the study area, and these crops are mainly used for subsistence 40 to meet household self-consumption requirements.
Remote sensing of salt-affected soils
Thesis (PhD)--Stellenbosch University, 2013.ENGLISH ABSTRACT: Concrete evidence of dryland salinity was observed in the Berg River catchment in the Western
Cape Province of South Africa. Soil salinization is a global land degradation hazard that
negatively affects the productivity of soils. Timely and accurate detection of soil salinity is
crucial for soil salinity monitoring and mitigation. It would be restrictive in terms of costs to use
traditional wet chemistry methods to detect and monitor soil salinity in the entire Berg River
catchment. The goal of this study was to investigate less tedious, accurate and cost effective
techniques for better monitoring.
Firstly, hyperspectral remote sensing (HRS) techniques that can best predict electrical
conductivity (EC) in the soil using individual bands, a unique normalized difference soil salinity
index (NDSI), partial least squares regression (PLSR) and bagging PLSR were investigated.
Spectral reflectance of dry soil samples was measured using an analytical spectral device
FieldSpec spectrometer in a darkroom. Soil salinity predictive models were computed using a
training dataset (n = 63). An independent validation dataset (n = 32) was used to validate the
models. Also, field-based regression predictive models for EC, pH, soluble Ca, Mg, Na, Cl and
SO4 were developed using soil samples (n = 23) collected in the Sandspruit catchment. These
soil samples were not ground or sieved and the spectra were measured using the sun as a source
of energy to emulate field conditions. Secondly, the value of NIR spectroscopy for the prediction
of EC, pH, soluble Ca, Mg, Na, Cl, and SO4 was evaluated using 49 soil samples. Spectral
reflectance of dry soil samples was measured using the Bruker multipurpose analyser
spectrometer. “Leave one out” cross validation (LOOCV) was used to calibrate PLSR predictive
models for EC, pH, soluble Ca, Mg, Na, Cl, and SO4. The models were validated using R2, root
mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD) and the
ratio of prediction to interquartile distance (RPIQ). Thirdly, owing to the suitability of land
components to map soil properties, the value of digital elevation models (DEMs) to delineate
accurate land components was investigated. Land components extracted from the second version
of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two
versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM
(GEOEYE DEM) derived from GeoEye stereo-images were compared. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The land components
were visually inspected and quantitatively analysed using the slope gradient standard deviation
measure and the mean slope gradient local variance ratio for accuracy.
Fourthly, the spatial accuracy of hydrological parameters (streamlines and catchment
boundaries) delineated from the 5-m resolution SUDEM (L1 and L2), the 30-m ASTER GDEM2
and the 90-m SRTM was evaluated. Reference catchment boundary and streamlines were
generated from the 1.5-m GEOEYE DEM. Catchment boundaries and streamlines were extracted
from the DEMs using the Arc Hydro module for ArcGIS. Visual inspection, correctness index, a
new Euclidean distance index and figure of merit index were used to validate the results. Finally,
the value of terrain attributes to model soil salinity based on the EC of the soil and groundwater
was investigated. Soil salinity regression predictive models were developed using CurveExpert
software. In addition, stepwise multiple linear regression soil salinity predictive models based on
annual evapotranspiration, the aridity index and terrain attributes were developed using
Statgraphics software. The models were validated using R2, standard error and correlation
coefficients. The models were also independently validated using groundwater hydro-census data
covering the Sandspruit catchment. This study found that good predictions of soil salinity based on bagging PLSR using first
derivative reflectance (R2 = 0.85), PLSR using untransformed reflectance (R2 = 0.70), a unique
NDSI (R2 = 0.65) and the untransformed individual band at 2257 nm (R2 = 0.60) predictive
models were achieved. Furthermore, it was established that reliable predictions of EC, pH,
soluble Ca, Mg, Na, Cl and SO4 in the field are possible using first derivative reflectance. The R2
for EC, pH, soluble Ca, Mg, Na, Cl and SO4 predictive models are 0.85, 0.50, 0.65, 0.84, 0.79,
0.81 and 0.58 respectively. Regarding NIR spectroscopy, validation R2 for all the PLSR
predictive models ranged from 0.62 to 0.87. RPD values were greater than 1.5 for all the models
and RMSECV ranged from 0.22 to 0.51. This study affirmed that NIR spectroscopy has the
potential to be used as a quick, reliable and less expensive method for evaluating salt-affected
soils. As regards hydrological parameters, the study concluded that valuable hydrological
parameters can be derived from DEMs. A new Euclidean distance ratio was proved to be a
reliable tool to compare raster data sets. Regarding land components, it was concluded that
higher resolution DEMs are required for delineating meaningful land components. It seems probable that land components may improve salinity modelling using hydrological modelling
and that they can be integrated with other data sets to map soil salinity more accurately at
catchment level. In the case of terrain attributes, the study established that promising soil salinity
predictions could be made based on slope, elevation, evapotranspiration and terrain wetness
index (TWI). Stepwise multiple linear regressions soil salinity predictive model based on
elevation, evapotranspiration and TWI yielded slightly more accurate prediction of soil salinity.
Overall, the study showed that it is possible to enhance soil salinity monitoring using HRS, NIR
spectroscopy, land components, hydrological parameters and terrain attributes.AFRIKAANSE OPSOMMING: Konkrete bewyse van droëland sout is waargeneem in die Bergrivier opvanggebied in die Wes-
Kaap van Suid-Afrika. Verbrakking van grond is 'n wêreldwye probleem wat ‘n negatiewe
invloed op die produktiwiteit van grond kan hê. Tydige en akkurate herkenning van verandering
in grond soutgehalte is ‘n noodsaaklike aksie vir voorkoming. Dit sou beperkend wees in terme
van koste om konvensionele nat chemiese metodes te gebruik vir die opsporing en monitering
daarvan in die hele Bergrivier opvanggebied. Die doel van hierdie studie was om ondersoek in
te stel na minder tydsame, akkurate en koste-effektiewe tegnieke vir beter monitering.
Eerstens, is hiperspektrale afstandswaarnemings (HRS) tegnieke wat die beste in staat is
elektriese geleidingsvermoë (EG) in die grond te kan voorspel deur gebruik te maak van
individuele bande, 'n unieke genormaliseerde grond soutindeks verskil (NDSI), parsiële kleinste
kwadratiese regressie (PLSR) en afwyking in PLSR, is ondersoek. Spektrale reflektansie van
droë grondmonsters is gemeet deur gebruik te maak van 'n spektrale analitiese toestel: FieldSpec
spektrometer in 'n donkerkamer. Voorspellings modelle vir grond soutgehalte is bereken met
behulp van 'n toets datastel (n = 63). 'n onafhanklike validasie datastel (n = 32) is gebruik om die
modelle te evalueer. Daarbenewens is veld-gebaseerde regressie voorspellings modelle vir EG,
pH oplosbare Ca, Mg, Na, Cl and SO4 ontwikkel deur gebruik te maak van grondmonsters (n =
23) versamel in the Sandpruit opvangsgebied. Hierdie grondmonsters is nie gemaal of gesif nie
en die spectra is gemeet deur gebruik te maak van die son as ‘n bron van energie om veld
toestande na te boots. Tweedens, is die waarde van NIR spektroskopie vir die voorspelling van
die EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 met behulp van 49 grondmonsters geëvalueer.
Spektrale reflektansie van droë grondmonsters is gemeet deur gebruik te maak van die Bruker
NIR veeldoelige analiseerder . Kruisvalidering (LOOCV) is gebruik om PLSR voorspellings
modelle vir EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 te kalibreer. Hierdie modelle is
gevalideer: R2, wortel-gemiddelde-kwadraat fout kruisvalidering (RMSECV), verhouding van
voorspellings afwyking (RPD) en die verhouding van die voorspelling se inter-kwartiel afstand (RPIQ). Derdens is land komponente gekarteer vanweë die nut daat van tov grondeienskappe, en
die waarde van DEMs is ondersoek om akkurate land komponente af te baken. Land komponente
uit die tweede weergawe van die 30 m gevorderde ruimte termiese emissie en refleksie radio globale DEM (ASTER GDEM2), die 90-m ruimtetuig radar topografie sending DEM (SRTM
DEM), twee weergawes van die 5 m Universiteit van Stellenbosch DEMs (SUDEM L1 en L2) en
'n 5 m DEM (GEOEYE DEM) afgelei van GeoEye stereo-beelde, is vergelyk. Land komponente
is afgebaken met behulp van helling, gradiënt en aspek afgeleides van elke DEM. Die land
komponente is visueel geïnspekteer en kwantitatief ontleed met behulp van die helling gradiënt
standaardafwyking te meet en die gemiddelde helling-gradiënt-plaaslike variansie verhouding vir
akkuraatheid.
Vierdens, is die ruimtelike akkuraatheid van hidrologiese parameters (stroomlyn en
opvanggebied grense) geëvalueer soos afgelei vanaf die 5 m resolusie SUDEM (L1 en L2), die
30 m ASTER GDEM2 en die 90 m SRTM . Die verwysings opvanggebied grens en stroomlyn is
gegenereer vanaf die 1,5-m GEOEYE DEM. Opvanggebied grense en stroomlyn uit die DEMs is
bepaal deur gebruik te maak van die Arc Hydro module in ArcGIS. Visuele inspeksie,
korrektheid indeks, 'n nuwe Euklidiese afstand indeks en die indikasie-van-meriete indeks is
gebruik om die resultate te valideer. Laastens is die waarde van die terrein eienskappe om grond
southalte te modeleer ondersoek, gebaseer op die EG van die grond en grondwater. Grond
soutgehalte regressie voorspellings modelle is ontwikkel met behulp van CurveExpert sagteware.
Verder, stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings modelle
gebaseer op jaarlikse evapotranspirasie, die dorheids indeks en terrein eienskappe is ontwikkel
met behulp van Statgraphics sagteware. Die modelle is gevalideer deur gebruik te maak van R2,
standaardfout en korrelasiekoëffisiënte. Die modelle is ook onafhanklik bekragtig deur die
gebruik van grondwater hidro-sensus-data wat die Sandspruit opvanggebied insluit. Hierdie studie het bevind dat 'n goeie voorspelling van grond soutgehalte gebaseer op uitsak
PLSR met behulp van eerste orde afgeleide reflektansie (R2 = 0,85), PLSR deur gebruik te maak
van ongetransformeerde reflektansie (R2 = 0,70), 'n unieke NDSI (R2 = 0,65) en die
ongetransformeerde individuele band op 2257 nm (R2 = 0,60) voorspellings modelle verkry is.
Verder is vasgestel dat betroubare voorspellings van die EG, pH, oplosbare Ca, Mg, Na, Cl en
SO4 in die veld moontlik is met behulp van eerste afgeleide reflektansie. Die R2 van EG, pH,
oplosbare Ca, Mg, Na, Cl en SO4 is 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 en 0.58 onderskeidelik. Ten
opsigte van NIR spektroskopie het die validasie van R2 vir al die PLSR voorspellings modelle gewissel tussen 0,62-0,87. Die RPD waardes was groter as 1,5 vir al die modelle en RMSECV
het gewissel tussen 0,22-0,51. Hierdie studie het bevestig dat NIR spektroskopie die potensiaal
het om gebruik te word as 'n vinnige, betroubare en goedkoper metode vir die analise van soutgeaffekteerde
gronde. T.o.v. hidrologiese parameters, het die studie tot die gevolgtrekking
gekom dat waardevolle hidrologiese parameters afgelei kan word uit DEMs. 'n nuwe Euklidiese
afstand verhouding is bevestig as 'n betroubare hulpmiddel om raster datastelle te vergelyk. Ten
opsigte van grond komponente, is daar tot die gevolgtrekking gekom dat hoër resolusie DEMs
nodig is vir die bepaling van sinvolle land komponente. Dit lyk waarskynlik dat die land
komponent soutgehalte modellering hidrologiese modellering verbeter en dat hulle geïntegreer
kan word met ander datastelle vir meer akkurate kaarte op opvangsgebied skaal. In die geval van
die terrein eienskappe het, die studie vasgestel dat belowende grond soutgehalte voorspellings
gemaak kan word gebaseer op helling, elevasie, evapotranspirasie en terrein natheid indeks
(TWI). 'n stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings model wat
gebaseer is op elevasie, evapotranspirasie en TWI het effens meer akkurate voorspellings van die
grond soutgehalte gelewer. In geheel gesien, het die studie getoon dat dit moontlik is om grond
soutgehalte monitering te verbeter met behulp van HRS, NIR spektroskopie, land komponente,
hidrologiese parameters en terrein eienskappe.The Agricultural Research Council (ARC), Water Research Commission and the National
Research Foundation for funding
Remote sensing-based identification and mapping of salinised irrigated land between Upington and Keimoes along the lower Orange River, South Africa
Thesis (MA (Geography and Environmental Studies))--University of Stellenbosch, 2005.Salinisation is a major environmental hazard that reduces agricultural yields and
degrades arable land. Two main categories of salinisation are: primary and secondary
soil salinisation. While primary soil salinisation is caused by natural processes,
secondary soil salinisation is caused by human factors. Incorrect irrigation practices
are the major contributor to secondary soil salinisation. Because of low costs and less
time that is associated with the use of remote sensing techniques, remote sensing data
is used in this study to identify and map salinised irrigated land between Upington and
Keimoes, Northern Cape Province, in South Africa.
The aim of this study is to evaluate the potential of digital aerial imagery in
identifying salinised cultivated land. Two methods were used to realize this aim. The
first method involved visually identifying salinised areas on NIR, and NDVI images
and then digitizing them onscreen. In the second method, digital RGB mosaicked,
stacked, and NDVI images were subjected to unsupervised image classification to
identify salinised land. Soil samples randomly selected and analyzed for salinity were
used to validate the results obtained from the analysis of aerial photographs.
Both techniques had difficulties in identifying salinised land because of their inability
to differentiate salt induced stress from other forms of stress. Visual image analysis
was relatively successful in identifying salinised land than unsupervised image
classification. Visual image analysis correctly identified about 55% of salinised land
while only about 25% was identified by unsupervised classification. The two
techniques predict that an average of about 10% of irrigated land is affected by
salinisation in the study area.
This study found that although visual analysis was time consuming and cannot
differentiate salt induced stress from other forms; it is fairly possible to identify areas
of crop stress using digital aerial imagery. Unsupervised classification was not
successful in identifying areas of crop stress
A Scoping Review of Landform Classification Using Geospatial Methods
Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods
Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning
Remote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various environments and scales. Predicting susceptibility to LULC change is crucial for policy formulation and land management. However, the use of machine learning (ML) for modelling LULC change is limited. This study modelled LULC change susceptibility in the Okavango basin using ML techniques. Areas with high LULC change susceptibility are termed priority management areas (PMAs) in this study. Trajectories of LULC change between 1996 and 2020 are derived from existing LULC change maps of the Okavango basin. Overlay analysis is then used to detect patches of LULC change transitions. Three LULC transitional categories are adopted for modelling PMAs, namely 1) from natural to anthropogenic classes (Category A); 2) from anthropogenic to natural classes (Category B); and 3) from natural to another natural class (Category C). An ensemble of ML algorithms is calibrated with categories of LULC change and social-ecological drivers of change to produce maps showing the susceptibility of LULC change in the basin. Thereafter, thresholding is done on probability maps of susceptibility to LULC change based on the maximum sum of sensitivity and specificity (max SSS) to delineate PMAs. Results for trajectories of LULC change indicate that anthropogenic activities (croplands, built-up areas, and barelands) generally expanded, displacing natural areas (wetlands, woodlands, water, and shrubland) from 1996 to 2020. Regarding PMAs, anthropogenic-related PMAs (Category A ∼34 560 km2) covered a larger area compared to the natural ones (Categories B∼33 407 km2) and (Categories C∼15 040 km2). The findings of this study emphasize the value of ensemble ML modelling in identifying PMAs and guiding transboundary land use planning. Overall, this study highlights the role of anthropogenic activities in driving land use changes in Transboundary Drainage Basins (TDBs) and suggests a need to promote sustainable practices in predicted PMAs through comprehensive planning to ensure water availability in the Okavango basin
Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory
Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However, existing studies approach LULC change as distinct patches but ignore the connectivity between them. It is crucial to investigate approaches that can detect the spread pathways of LULC change to aid natural resource management and decision-making. This study aims to evaluate the utility of the Circuit Theory to detect the spread and connectivity of LULC change within the Okavango basin. Patches of LULC change sites that were derived from change detection of LULC based on the Deep Neural Network (DNN) for the period between 2004 and 2020 were used. The changed sites were categorized based on the nature of the change of the classes, namely Category A (natural classes to artificial classes), Category B (artificial classes to natural classes), and Category C (natural classes to natural classes). In order to generate the resistance layer; an ensemble of machine learning algorithms was first calibrated with social-ecological drivers of LULC change and centroids of LULC change patches to determine the susceptibility of the landscape to LULC change. An inverse function was then applied to the susceptibility layer to derive the resistance layer. In order to analyze the connectivity and potential spread pathways of LULC change, the Circuit Theory (CT) model was built for each LULC change category. The CT model was calibrated using the resistance layer and patches of LULC change in Circuitscape 4.0. The corridor validation index was used to evaluate the performance of CT modeling. The use of the CT model calibrated with a resistance layer (derived from susceptibility modeling) successfully established the spread pathways and connectivity of LULC change for all the categories (validation index > 0.60). Novel maps of LULC change spread pathways in the Okavango basin were generated. The spread pathways were found to be concentrated in the northwestern, central, and southern parts of the basin for Category A transitions. As for category B transitions, the spread pathways were mainly concentrated in the northeastern and southern parts of the basin and along the major rivers. While for Category C transitions were found to be spreading from the central towards the southern parts, mainly in areas associated with semi-arid climatic conditions. A total of 186 pinch points (Category A: 57, Category B: 71, Category C: 58) were detected. The pinch points can guide targeted management LULC change through the setting up of conservation areas, forest restoration projects, drought monitoring, and invasive species control programs. This study provides a new decision-making method for targeted LULC change management in transboundary basins. The findings of this study provide insights into underlying processes driving the spread of LULC change and enhanced indicators for the evaluation of LULC spread in complex environments. Such information is crucial to inform land use planning, monitoring, and sustainable natural resource management, particularly water resources
Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning
CITATION: Kavhu, B., Mashimbye, Z. E. & Luvuno, L. 2021. Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning. Remote Sensing, 13:5054, doi:10.3390/rs13245054.The original publication is available at https://www.mdpi.comAccurate land use and cover data are essential for effective land-use planning, hydrological
modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site,
managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often
difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes.
This study investigates the combined value of climate-based regionalization and integration of
spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover
classification using deep learning and machine learning approaches. Two experiments were set up,
the first entailing the integration of spectral bands with spectral indices and the second involving the
combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger
climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random
forest and extreme gradient boosting), and deep learning (neural network and deep neural network)
classifiers were used in this study. Supervised classification using a total of 5140 samples was
conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients
were used to validate the results. The study found that the integration of spectral bands with indices
improves the accuracy of land use/cover classification using machine learning and deep learning.
Post-feature selection combinations yield higher accuracies in comparison to combinations of bands
and indices. A combined integration of spectral indices with bands and climate-based regionalization
did not significantly improve the accuracy of land use/cover classification consistently for all the
classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization
significantly improved the accuracy for all classifiers investigated in this study. Findings of this study
will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs.https://www.mdpi.com/2072-4292/13/24/5054Publisher's versio
Assessing the influence of DEM source on derived streamline and catchment boundary accuracy
CITATION: Mashimbye, Z. E., De Clercq, W. P. & Van Niekerk, A. 2019. Assessing the influence of DEM source on derived streamline and catchment boundary accuracy. Water SA, 45(4):672-684, doi:10.17159/wsa/2019.v45.i4.7549.The original publication is available at http://www.wrc.org.zaAccurate DEM-derived streamlines and catchment boundaries are essential for hydrological modelling. Due to the popularity of hydrological parameters derived mainly from free DEMs, it is essential to investigate the accuracy of these parameters. This study compared the spatial accuracy of streamlines and catchment boundaries derived from available digital elevation models in South Africa. Two versions of Stellenbosch University DEMs (SUDEM5 and DEMSA2), the second version of the 30 m advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM2), the 30 and 90 m shuttle radar topography mission (SRTM30 and SRTM90 DEM), and the 90 m Water Research Commission DEM (WRC DEM) were considered. As a reference, a 1 m GEOEYE DEM was generated from GeoEye stereo images. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module. A reference catchment boundary was generated from the GEOEYE DEM and verified during field visits. Reference streamlines were digitised at a scale of 1:10 000 from the 1 m orthorectified GeoEye images. Visual inspection, as well as quantitative measures such as correctness index, mean absolute error, root mean squares error and figure of merit index were used to validate the results. The study affirmed that high resolution (<30 m) DEMs produce more accurate parameters and that DEM source and resampling techniques also play a role. However, if high resolution DEMs are not available, the 30 m SRTM DEM is recommended as its vertical accuracy was relatively high and the quality of the streamlines and catchment boundary was good. In addition, it was found that the novel Euclidean distance-based MAE and RMSE proposed in this study to compare reference and DEM-extracted raster datasets of different resolutions is a more reliable indicator of geometrical accuracy than the correctness and figure of merit indices.https://www.watersa.net/article/view/7549Publisher's versio