Observational Support for Regional Policy Implementation: Land Surface Change Under Anthropogenic and Climate Pressure in SALDi Study Sites

Abstract

South Africa is a vast, very diverse and dynamic country experiencing rapidly changing demands for the utilization of its natural resources. At the same time, global climate change and related processes affect the land’s agricultural utilization. The considerable expansion of high-resolution Earth observation systems providing high temporal resolution acquisitions of the land surface free of charge provides the opportunity to monitor land surface dynamics at an unprecedented temporal and spatial resolution. Based on the work in the SPACES II project South Africa Land Degradation Monitor (SALDi), we present examples highlighting the new observational opportunities potentially supporting regional policy implementation. Thus, the main objective of this chapter is to present applied examples from the six SALDi study sites spread across the country illustrating some of the new capabilities providing simultaneously a regional overview of land surface dynamics as well as high-resolution information on specific areas, for example a district, a municipality, a farm or a specific field plot.

1 Introduction

Land use and land cover reflect at any given time the utilization of the resources of a country. Utilization patterns and trends are dependent on underlying natural resources (e.g., the geology, soils, vegetation, climate and hydrology conditions) and the opportunities, conditions and conventions to exploit them. This leads to a specific land use pattern in specific regions which might change due to anthropogenic and/or climate pressure over time. Often, the changes seemingly introduced by external forcing like climate change are considered land degradation, while anthropogenic changes are often considered development (e.g. the transformation of rangeland to settlement areas or cultivated land into mining areas). This kind of obvious land use and land cover change is regularly monitored within the framework of repeated national land cover and land use assessments. Compared to this, the SPACES II project South Africa Land Degradation Monitor (SALDi) targets more subtle land cover changes which eventually might cause land degradation. Thus, the project addresses the dynamics and functioning of multi-use landscapes with respect to land use and land cover change, water fluxes and implications for habitats and ecosystem services.

An overarching goal of the project is to implement new and sustainable adaptive land degradation assessment tools. The project aims to advance current methodologies for multi-use landscapes by innovatively incorporating inter-annual and seasonal variability in a spatially explicit approach and takes advantage of the emerging availability of high spatio-temporal resolution Earth observation data (e.g. ESA Sentinels, DLR TanDEM-X, NASA Landsat), growing sources of in-situ data and advancements in modelling approaches.

In this chapter, we will provide observational support and discoveries on subtle land cover changes in the six SALDi study sites in support of regional policy implementation to identify areas under the threat of land degradation. The aim is to thus support the remedial actions. Following this introduction, we will first provide an overview of the six SALDi study sites and discuss the land degradation challenges at these sites. Subsequently, we will introduce the SALDi product catalog developed based on the high spatio-temporal resolution Earth observation data mainly acquired by the ESA Sentinels since 2017. Finally, we will provide examples of the observational support and discoveries found at each site. The examples have been chosen based on land surface processes (those perceived as most important) to highlight the opportunities to deal with observations on a regional and local scale at the same time. In addition, we try to highlight the advantages of time series data and analysis when dealing with land surface processes.

Fig. 29.1

Land cover in southern Africa, that is the SALDi SP2 WRF Hydro modelling domain, and location of SALDi study areas in South Africa. 1: Overberg, 2: Kai !Garib, 3: Sol Plaatje, 4: Mantsopa, 5: Bojanala Platinum, 6: Ehlanzeni (data source: ©ESRI 2013, simplified MODIS Land Cover from Friedl et al. 2010, compilation: J. Baade)

Fig. 29.2

Climate characteristics (1980–2020) for the SALDi study sites. 1: Overberg, 2: Kai !Garib, 3: Sol Plaatje, 4: Mantsopa, 5: Bojanala Platinum, 6: Ehlanzeni (data source: Funk et al. 2015, Harris et al. 2020, compilation: Z. Zhang)

2 SALDi Study Sites

In order to develop a broadly applicable set of products to support detection of land surface dynamics and eventually land degradation processes, we identified six study areas covering approximately 100 km × 100 km each (Fig. 29.1). The study sites cover the main climatic gradient from the semi-arid winter-rainfall region in the southwest across the central semi-arid year-round-rainfall region to the semi-humid summer-rainfall region in the northeast. The climate diagrams according to Walter and Lieth (1967) (Fig. 29.2) clearly identify average dry and wet months and highlight the great aridity in the SALDi study area No. 2: Kai !Garib, located at the boundary to Namibia. In addition, the selection of study areas considers physical (e.g. biomes, relief, soils and land types) and socio-economic (e.g. land tenure, farming practices) differences within South Africa. Within our study sites, protected areas (often National Parks) represent benchmark sites (Wiese et al. 2011), providing a foundation for Baseline Trend Scenarios (Diaz et al. 2015), against which climate-driven ecosystem service dynamics of multi-used landscape (cropland, rangeland, forests) are evaluated.

The analysis and comparison of the land cover in the six study areas (Fig. 29.3) provides evidence for the strong impact of climate conditions on the utilization of land. In the Overberg region in the Southwest (SALDi site 1), 45% of the area is cultivated and 40% covered by shrubland (i.e. mostly fynbos), while forested land¹ cover more than 75% of the area in Ehlanzeni in the Northeast (SALDi site 6). In Kai !Garib (SALDi site 2), shrubs (50%) and barren land (48%) characterize the landscape, while grassland (75%) represent the dominant land cover in Sol Plaatje (SALDi site 3). Compared to this, Mantsopa (SALDi site 4) and Bojanala Platinum (SALDi site 5) are characterized by a more diverse land cover. It is this diversity of land utilization in the different study sites which supports the aspiration to eventually provide overall results applicable to the whole of South Africa and beyond.

Fig. 29.3

Land cover characteristics in the six SALDi study areas. 1: Overberg, 2: Kai !Garib, 3: Sol Plaatje, 4: Mantsopa, 5: Bojanala Platinum, 6: Ehlanzeni (data source: simplified land cover from DFFE 2021, compilation: J. Baade)

In accordance with the different climatic conditions as well as the different land cover and land use conditions, which also reflect other basic conditions, such as relief, geological conditions, soils and climate, it is possible to identify dominant degradation processes and related land surface processes (Table 29.1) for the study areas in cooperation with local stakeholders. Within the framework of SALDi, these major degradation processes and related land surface processes are to be addressed based on the SALDi remote sensing product catalog (see below and Chap. 24, Fig. 24.2). Comparing the six sites shows that desertification, bush encroachment or invasive species and soil erosion are concerns in all six sites. Reservoir siltation as an off-site damage from soil erosion (Chap. 13) is a concern in all sites with sufficient rainfall to support reservoirs, that is Overberg, Mantsopa, Bojanala Platinum and Ehlanzeni. Salinization is mentioned in areas where irrigation is important, while gully erosion is of concern in areas with high relief and steep slopes. The related land surface processes and properties observable with a remote sensing approach include general vegetation dynamics, fire, bush encroachment, the temporal and spatial proportion of bare soil and possibly the formation of gullies (see Chap. 13, Fig. 13.5).

Table 29.1 Dominant land degradation processes in the SALDi project areas and related land surface processes (Biome classification acc. to Mucina and Rutherford 2006)

One difficulty in distinguishing climatic-driven land surface dynamics from anthropogenic land degradation in the sense of a continuous declining process is the high inter-seasonal variation of climate on a decadal scale in many parts of South Africa. This can be clearly illustrated by the more than 100-year long precipitation record from Bredasdorp in Overberg, Western Cape Province (Fig. 29.4). Based on this, the 5-year moving average of annual precipitation varies regularly and partly for several decades between 400 and over 500 mm. Under these conditions, it is rather difficult to attribute observations from a few years (~5 years) to either ‘normal’ land surface dynamics or land degradation.

Fig. 29.4

Precipitation variability (5 year moving average) 1910–2020, Zeekoevlei Farm, Bredasdorp, Overberg (data courtesy of P. Albertyn and E. Hahndiek, Nuwejaars SMA, compilation: J. Baade)

3 Description of SALDi Product Catalog

In the SALDi project, several products are developed from satellite data to be used as a basis for assessing land degradation in the project areas (Fig. 29.1). The product catalog comprises data derived mainly from Sentinel-1 radar and Sentinel-2 optical satellite time series, airborne and spaceborne lidar data and further earth observation sensors. It includes information products on temporal anomalies and breakpoints, on woody and vegetation cover, on surface moisture and a number of indices that can serve as proxies for land surface properties, for example bare area abundance, vegetation activity or moisture. A detailed overview over the SALDi product catalog is given in Table 29.2. At the top of the table, a number of basic data sets providing background information on the study sites are listed. The next section lists the analysis ready data and products related to the Sentinel-1 radar satellites followed by the Sentinel-2 optical data and products. To avoid reiterations within this book, products already described in Chap. 24 are not discussed in greater detail here.

Table 29.2 Overview of the SALDi Earth observation products provided in the SALDi data cube and the Digital Earth Africa (DEA^a) data cube, access to SALDi data cube will be granted on request

The spectral indices mentioned in Table 29.2 can be used as proxies for biophysical variables describing the status of the land surface concerning, for example, vegetation cover or moisture. They have proven to have a wide and growing range of applications (Tucker 1979; Qi et al. 1994; Diek et al. 2017). As they are mostly used to monitor land surface and vegetation conditions, they can provide early warning on droughts and famines. The performance of these indices differs in accordance with land cover (e.g. sparse vegetation vs. very dense vegetation) and various environmental conditions. Therefore, we provide in Table 29.3 some details on the most utilized spectral indices provided in the SALDi data cube.

Table 29.3 Overview of the spectral indices derived from Sentinel-2 data provided in the SALDi data cube

4 Regional Examples of Land Surface Change

Based on the SALDi Earth observation product catalog (Sect. 29.3), we present in the following examples of times series analysis of Sentinel-1 and Sentinel-2 data aimed at observations on relevant land surface dynamics, eventually on processes which might be or are considered land degradation. The examples cover all six SALDi study sites picking specific issues for each site. The presented examples and the interpretation are the outcome of project works including dedicated personnel and the exchange with local experts. It is hoped that the examples are not only scientifically interesting, but useful in solving everyday problems on the ground. One should note that although most presented analyses are easy to automate, it often needs an experienced person to evaluate the results and assess the implications of observed land surface dynamics.

4.1 Overberg

The SALDi study site 1, Overberg, is located in the winter rainfall region in the Southwest of the country. With about 450 mm of mean annual rainfall over the whole area, the land is largely used for mixed farming, consisting of winter grains and livestock (beef cattle and Merino sheep) and to a lesser degree for livestock farming (in particular dairy cattle). Especially, the NW around Robertson is well known for winegrowing which is expanding into the Southern Overberg area. According to an analysis of the South African National Land Cover (SANLC) data (DFFE 2021), about 43% of the land surface (5175 km² of 13,080 km²) is cultivated. Only about 1% of the cultivated area (50 km²) is under pivot irrigation, highlighting the importance of rainfed arable farming in the Overberg region. Most of the arable land is located on the undulating Southern Coastal Platform characterized by mudrock, shales and siltstone. These basic conditions make soil erosion by water a potentially relevant soil degradation process in the area (Chap. 13, Fig. 13.4). In addition to this, veld fires occurring during the summer fire season and flooding of valley bottoms and lowland wetlands during the winter rainfall season are important ecological process in this region shaping land surface dynamics.

Concerning the soil erosion issue, we are investigating the applicability of the Bare Soil Index (adapted BI acc. to Rikimaru et al. 2002) to assess the soil erosion hazard caused by sparse vegetation cover and the exposure of bare soil. For this, we derived the BSI for each year from 2017 through 2020 based on the available (cloud-free) Sentinel 2 optical acquisitions (10 m resolution) and calculated basic statistics, that is the minimum, maximum, mean, median, standard deviation and variance for every year. To advance the understanding of the signal, we distinguished between pivot irrigated and rainfed fields. As expected, irrigated fields often have a higher vegetation cover and thus a lower BSI. The same is true for not cultivated land, like shrubland and grassland, which shows higher vegetation cover and a lower BSI. However, the mean annual BSI or a mean BSI over several years (not shown) is not very selective. Instead, we provide in Fig. 29.5, showing an 85 km² section of the intensively utilized hilly Southern Coastal Lowlands (Partridge et al. 2010) northwest of Elim, the mean intra-annual range of the BSI, which is a proxy for the management intensity of the areas. Shrublands and wooded valley bottoms are characterized by rather continuous vegetation cover and a low range of BSI while harvest operations on cropland induce large changes in vegetation cover and the BSI. Furthermore, on cultivated areas where tillage operations and the growth of different crops in different years supports a high range of BSI, a time series analysis of the annual range might indicate areas which were not worked on, i.e. fallow in a particular year. Thus, a field-specific analysis of the BSI over a number of years might provide a tool to identify the abandonment of farmland being a potential indicator of land degradation, if land abandonment is not due to socio-economic developments as e.g. recently documented for sites in the Eastern Cape (de la Hay and Beinhart 2017).

Fig. 29.5

Multi-annual (2017–2020) mean annual range of the bare soil index (BSI) in a section of the SALDi Overberg study site along the Koue River close to Elim. The eastern half of the section is part of the Nuwejaar Special Management Area (SMA). Cultivated areas are characterized by high intra-annual dynamics due to tillage operations, while neighbouring shrubland areas and valley bottoms are characterized by low intra-annual change, i.e. continuous vegetation cover. A closer look depicts vegetated contour bunds representing effective soil conservation measures on most fields (data source: Contains modified Copernicus Sentinel data 2017–2020, compilation: A. Hirner, J. Baade)

We further analysed time series of a Sentinel-2 based vegetation index (Normalized Difference Vegetation Index, NDVI) for the entire Overberg Study area (13,000 km²) for abrupt land surface changes (breakpoints) between April 2018 and March 2020. Using the BEAST-breakpoint detection method (Zhao et al. 2019) allows not only to delineate the position of abrupt changes in space but also in time. Most of the identified abrupt changes can be related to fire events during the summer and are located in areas of (semi-)natural vegetation such as shrublands, grasslands and naturally wooded land (Fig. 29.6a–d). The largest veld fire scars identified were associated with the Riviersonderend and Bonnivale fire in December 2018 (Overberg FPA 2019) in the eastern part of the Riviersonderend Nature Reserve (Fig. 29.6c, e) and a veld fire east of Bredasdorp in the Overberg Test Range in September 2019. The presented Sentinel-2 based results at 10 m spatial resolution show good agreement with the established coarse resolution (500 m) MODIS burned area product (Fig. 29.6e, f). At the same time, they indicate that the higher resolution data is able to reduce the minimum mapping unit from 25 ha to less than 1 ha supporting post-fire management activities.

Fig. 29.6

Fire scars and their timing in the Overberg study area as detected by BEAST breakpoint analysis based on Sentinel-2 data for Apr/2018–Mar/2020. (a) Overview of breakpoint results for the entire Overberg site with zoom-ins (c & d) (data source: Contains modified Copernicus Sentinel data 2018–2020), (b) land cover of the Overberg site (LRI 2018), and (e & f) Burned areas as detected by MODIS Product MCD64A1 (Giglio et al. 2015, compilation: A. Hirner, U. Gessner)

During the winter rainfall season, culminating in June through August with on average >50 mm of rainfall per month (personal comm. E. Hahndiek), valley bottom areas and specially the coastal lowland wetlands are prone to regular flooding. Here, flooding is not only a hazard to infrastructure, but a very important ecological process sustaining valley bottom and other wetlands (Ellery et al. 2016) in the area. As flooding often coincides with overcast conditions, synthetic aperture radar (SAR) satellites, like Sentinel-1, penetrating clouds are a most useful instrument to map flooding extent and flooding frequency. This is based on the fact that (smooth) water surfaces are characterized by very low backscatter. To illustrate the capability and some limitations of this approach, we focus on an 815 km² area (excluding the ocean surface) located between Cape Agulhas and Bredasdorp (Fig. 29.7) and including parts of the Agulhas National Park and the Nuwejaars Wetlands SMA. Here we analysed the time series of Sentinel-1 VV backscatter images from July 2018 through November 2021, providing a total of over 100 raster images. Firstly, VV backscatter was subjected to adaptive speckle filtering using the Lee filter with a 5 by 5 window size, which preserves the local spatial variations and minimize classification error. Then, the speckle filtered linear intensity backscatter was converted into decibel (dB) for easier statistical evaluation. Using the method of grey-level thresholding (White et al. 2015) with a fixed threshold of −18.0 dB, we mapped surface water occurrence in every single image and finally added up the observations to provide a flooding frequency map showing areas covered by water, varying from rarely to constantly (Fig. 29.7). Comparison with higher spatial resolution optical imagery indicates, that the minimum mapping unit of water surfaces is about 2 ha, despite of a 20 m spatial resolution of the raster data. Another limitation results from the fact that wind causes surface roughness on water surfaces providing backscatter values which might be as high as from rough land surfaces. While still indicating the spatial extent of flooding, this clearly impedes a rigorous temporal flooding frequency analysis. Taking the ocean surface as a reference, pixels from the Ocean are classified as water on average in 40 out of 100 observations using the −18.0 dB threshold. The seemingly very wet coastal dune field west of Agulhas, highlights another textbook ready issue when analyzing SAR backscatter data. Keeping these uncertainties in mind, the map clearly shows the major pans and vleis in the region and their seasonal dynamics. Furthermore, a number of arable fields, especially in the Kars river valley bottom and prone to seasonal flooding and water logging are clearly visible. These fields have been claimed early last century in the framework of the channelling of the Kars river and are used for arable farming of wheat and canola or for fodder production and grazing.

Fig. 29.7

Multi-annual (2018–2021) flooding frequency map of the Cape Agulhas region, a section of the SALDi Overberg study site. A considerable part of the study area belongs either to the Agulhas National Park or is part of the Nuwejaar Special Management (SMA). The analysis provides evidence of the seasonal dynamics of the coastal pans and vleis as well as the valley bottom wetlands in the region (data source: contains modified Copernicus Sentinel data 2018–2021, compilation: J. Baade, S. Selvaraj)

4.2 Kai !Garib

The SALDi study site 2, Kai !Garib, centered around the Augrabies Falls National Park (AFNP) in the NW at the border with Namibia represents one of the driest parts of South Africa. With about 120 mm of mean annual rainfall and a similar amount of mean annual evapotranspiration the climate is arid around the year (Fig. 29.2). The area belongs to the Nama-Karoo biome and is characterized by sparse, grassy vegetation (Mucina and Rutherford 2006). Episodically water-filled, non-perennial valleys are characterized by rather dense bushes and trees. According to the DFFE (2021) land cover map 48% of the site is barren land, 50% is shrubland, and only slightly more than 1% is cultivated. The most prominent feature characterized by abundant riverine vegetation is the Orange River Valley, traversing the study area from East to West. Here, the perennial waters are the source for widespread irrigation agriculture, especially vineyards and orchards. While large parts of the Kai !Garib area have been rather stable over the past years, an ongoing expansion of irrigated areas along the Orange River can be observed.

Here we focus on the suitability of the digital South African National Land Cover data sets, available since 1990, to characterize land surface change by focusing on the development of irrigated areas in the Orange River valley in the vicinity of Augrabies. Over a period of 30 years, one needs to consider that mapping procedures changed due to technical developments, new methods and requirements. This concerns, for example, the recently increased spatial and spectral resolution. In brief, Table 29.4 provides an overview of the available data and provides insight into the changing land cover class definitions complicating the comparison over the years. A detailed quality check provides evidence for the 1995 land cover data set deviations from previous and following mapping principles. Hence, it was not considered for further analysis. Apart from this limitation, Fig. 29.8 indicates a noteworthy persistence of irrigated vines and orchards in the centre of the Orange River valley since 1990 (dark green colour) and the ongoing expansion of irrigated permanent cultures onto the higher slopes of the valley along the road from Kakamas (not shown) to Augrabies and further to the West as well as north of the river. Overall, the area of permanent orchards and vines has more than doubled from 21 km² to 49 km² in the analysed section over the past 30 years.

Table 29.4 South African National Land Cover data available for analysing the development of irrigated permanent vines and orchards in Kai !Garib, Overview (sources: GEOTERRAIMAGE 2015, 2016; LRI 2018; DEFF 2021)

Fig. 29.8

Persistence and expansion of irrigated areas in the Orange River Valley upstream of Augrabies Falls National Park between 1990 and 2020. The brownish colours indicate the year of establishment of irrigated areas and the green colours the persistence of the areas for the given period, e.g. since 1990, since 2014, etc. (projection: UTM34S, data source: ESA 2021, GEOTERRAIMAGE 2015, 2016; LRI 2018; DFFE 2021, compilation: J. Baade)

4.3 Sol Plaatje

The SALDi study site 3, Sol Plaatje, is located in the centre of the country at the border between the Northern Cape and the Free State provinces with Kimberley being located in the NE corner of the study site. Climate wise, the area is located in the summer rainfall zone (SRZ) and is characterized by a semi-arid climate based on an average annual rainfall amount of about 370 mm. Concerning vegetation, the study area is split into a northern part belonging to the Savanna Biome (Kimberley Thornveld) and the southern part belonging to Nama-Karoo Biome (Northern Upper Karoo) while along the rivers azonal, alluvial vegetation is present (Mucina and Rutherford 2006). According to the DFFE (2021) land cover map the study site is mainly characterized by grassland (77%); shrubland (8.7%), forested land (6.3%) and cultivated land (4.7%) are the other main land cover classes present in the region.

Here we focus on the role of land use and land tenure on the status of the land surface and vegetation cover, respectively. Figure 29.9 provides a detail of the Sentinel-1 radar-based Earth observation time series analysis for this area. It shows a mean backscatter image of the northern part of Mokala National Park (MONP), located about 50 km southwest of Kimberley and west of the N12 at Ritchie (Motswedimosa). The bright area in the lower center of the figure represents the steep rocky outcrops of dolerite forming vast mesas in the landscape. South of Ritchie a number of pivot irrigation fields are clearly discernible and to the West of Ritchie the incised valley of the Riet River is marked by rather light hues. Apart from this one can observed considerable variation in the grayscale reflecting differences in the density of the vegetation. Where straight lines, angular features or circles dominate the pattern, these differences can clearly be attributed to human activities, that is agriculture. Some of the angular features are located even within the boundaries of MONP representing the legacy of prior agricultural activities in this area, which was proclaimed a protected site in 2007 (SANParks 2017). Besides these feature, clear differences and patterns are visible in many areas. Within the MONP boundaries, these patterns correspond very well with the vegetation units identified by SANParks (2017, p. 138) based on optical imagery. At the same time, one can distinguish in places differences in hue on either side of the National Park boundary indicating slight differences in vegetation density due to slightly different management. This clearly highlights the potential of radar-based Earth Observation to depict differences in vegetation in a savanna setting.

Fig. 29.9

Sentinel-1 mean polarized (VV) backscatter for the period July 2018 to November 2021 (N = 208) for Mokala National Park and surroundings (projection: UTM35S, data source: contains modified Copernicus Sentinel data 2018–2021, vegetation units from SANParks (2017), compilation: C. Pathe, J. Baade)

4.4 Mantsopa

Located in the Highveld grassland ecosystem (1300–1700 m asl., Mucina and Rutherford 2006), the SALDi study site 4, Mantsopa, is characterized by plateau mountains with extensive rangeland, pastures and arable land at the lower altitudes. With a mean annual temperature of 16 °C and annual precipitation of 500–700 mm (cf. Fig. 29.2), the region is situated in the summer rainfall region. In addition to soil erosion (Le Roux et al. 2008), the loss of pristine vegetation cover, changes in plant composition and bush encroachment represent major land degradation processes in the area (Mucina and Rutherford 2006).

Here we present an example for the monitoring of bush encroachment by analyzing the dynamics of slangbos (Seriphium plumosum), also known as bankrupt bush, over the recent years. Slangbos has been documented to increasingly spread into pastures and fallow land (Snyman 2012; Avenant 2015) diminishing the productivity of agricultural land and is considered the main encroaching shrub species in the area. The study focuses on a 1375 km² section of the SALDi Mantsopa study area between Maseru in the SE and Sandspruit in the NW. This area is characterized by strong human induced land surface dynamics as is evident from the Sentinel-1 radar backscatter RGB composite demonstrating the inter-annual surface and vegetation dynamics in the area (Fig. 29.10). Arable land is clearly visible in mostly green hues due to high backscatter of predominantly maize in the growing season (fall). This seasonal effect is less pronounced for grassland (e.g. olive-green areas close to Sandspruit) while hill slopes and settlements scatter intensely throughout all seasons and are thus represented by gray to white hues.

Fig. 29.10

Sentinel-1 cross-polarized (VH) backscatter seasonal false-colour composite (R: summer, G: fall, B: winter) of the north-eastern Mantsopa study site (~1375 km²) for the period Dec. 2016 to June 2017. The white box indicates the location of the focus area shown in Fig. 29.12 (data source: contains modified Copernicus Sentinel data 2016–2017, compilation: K. Schellenberg)

Against this background, slangbos sprawl is analysed using a continuous time series of (1) the backscatter and (2) the interferometric phase coherence from the Sentinel-1 C-band radar system for the 6-year period from 2015 to 2020 (Fig. 29.11). To validate the findings based on high-resolution optical imagery, 45 sampling sites characterized by slangbos encroachment were selected in the NE part of the SALDi Mantsopa study site (Fig. 29.10). The sampled area covers in total about 122 ha and equals 1357 pixels of 30 × 30 m.

Fig. 29.11

Sentinel-1 (S-1) radar backscatter (blue) and coherence time series (orange) 2015–2019 for areas with observed slangbos encroachment. Both the aggregation to a 30 × 30 m pixel spacing and time series smoothing helped leveraging the high signal uncertainty inherent to imaging radar (compilation: K. Schellenberg)

In the time series analysis (Fig. 29.11), the smoothed S-1 VH backscatter shows an undulating progress over the course of a year peaking every summer. In addition, one can identify an overall strong inter-annual increasing trend for the sites with slangbos sprawl as evident from the increasing annual minima, mean and maxima. This trend can be attributed to the spreading of water-filled vegetation, that is slangbos. The seasonal amplitude of S-1 VH backscatter reflects the strong alternating wet and dry seasons, that is an increase of soil moisture and vegetation (structure and water content) coinciding with the beginning of the growing season. The rather low backscatter in spring 2015 and summer 2016 is a result of the poor vegetation development due to the strong droughts of the years 2014–2016 (Archer et al. 2017) clearly visible as well in optical data from that time (Urban et al. 2021). Featuring a rather similar undulating but lagged signal compared to the S-1 VH backscatter, the smoothed 14-day repeat-pass S1 VV coherence decreases with leaf development and increases with senescence of vegetation. Since coherence is related to vegetation properties, it is an additional and independent parameter for land surface structure.

Based on the observed trends of the two named radar signals, a slangbos occurrence map was prepared and discussed in detail for a number of sites in the Mantsopa study area (Urban et al. 2021). In addition to this, a detailed 65 km² focus area (Fig. 29.12) located between Sandspruit to the North and Marseilles to the South (see Fig. 29.10 for the location of the focus area) is presented here. The colours show surface areas (ground pixel size: 30 × 30 m) where the Sentinel-1 based slangbos detection model indicated the dominant presence of slangbos (>50% cover) in a given year. Areas with persistent slangbos cover are presented in white. Apart from some stable slangbos areas distributed throughout the focus area and often associated with escarpments, the mapping provides evidence of considerable land surface dynamics. While some areas like in the SW have obviously been cleared from slangbos at the beginning of the observation period, i.e. in 2016, others reached the status of being slangbos encroached only at the end of the period, i.e. in 2019.

Fig. 29.12

Sentinel-1 based slangbos occurrence map for the years 2015–2019 (projection: UTM35S). Colours indicate detection of slangbos coverage (>50%) in the given years. White hues indicate persistent occurrence of slangbos (data source: contains modified Copernicus Sentinel data 2015–2019, compilation: K. Schellenberg)

Scrutinizing the findings using high-resolution optical imagery, it was found that about 19% of slangbos-covered land surfaces were not detected, while about 15% of other surfaces were wrongly assigned to slangbos. It is anticipated that using ground-based mapping of slangbos instead of high-resolution optical imagery for model training will increase the overall accuracy. Taking the uncertainty into account, this remote sensing-based analysis suggests that about 6% of the specific study area (1375 km²) is subject to slangbos encroachment. In addition to this overall assessment of affected areas, the presented approach provides rather reliable information on the field sites of concern. Scattered small areas could represent other bushy vegetation.

4.5 Bojanala Platinum

The SALDi study site 5, Bojanala Platinum, is located in the summer rainfall region in the North of the country close to the border with Botswana. With about 550 mm of mean annual rainfall over the whole area, the land is largely used for livestock farming as indicated by the high proportion of grassland (42%) and forested land (36%) (DFFE 2021) representing often savannas, more specifically the Central Bushveld Bioregion (Mucina and Rutherford 2006). About 18% of the area is cultivated and less than 1% of the cultivated area is under pivot irrigation, highlighting the importance of rainfed arable farming in the Bojanala Platinum region. As visible in the South African National Land Cover map (LRI 2018) reproduced in Fig. 29.13, one can identify three major regions in the study area: the southern part is dominated by grassland and arable land, partly intensively used through pivot irrigation schemes. The central, rather mountainous part is cleanly dominated by forests and grasslands. In the north, the landscape is increasingly flat and dry and not suitable for arable farming and mostly covered by shrublands characteristic of a savanna type landscape.

Fig. 29.13

Maximum bare soil index (maxBSI) per year, based on Sentinel-2 time series for the years 2017–2021. Low BSI values indicate low and high BSI values high bare soil fractions. Westernmost part of the Pilanesberg National Park borders indicated by black Polygon in the NE of the maps (data source: land cover: LRI 2018, contains modified Copernicus Sentinel data 2017–2021, compilation: A. Hirner)

Here, we analyzed the dynamics of the relatively stable, natural vegetation types of Bojanala Platinum at interannual scale based on the Sentinel-2-derived BSI (Rikimaru et al. 2002). We computed the maximum BSI per year for 2017–2021 as an indicator of the maximum fraction of exposed soil for each year under investigation. In addition, we calculated mean values of maxBSI for the land cover types woodland, grassland and shrubland, according to the South African National Land Cover (LRI 2018) (Fig. 29.13) and compared them to annual CHIRPS precipitation sums (Funk et al. 2015).

Figure 29.13 shows relatively little variations of maxBSI in a relatively stable study site that is only affected by minor land use change. The most conspicuous differences are related to striking patterns of patches with high bare soil fractions (i.e. high maximum BSI values) which are discussed below (Fig. 29.15). Apart from these patches, there are general trends that demonstrate that even in this relatively stable landscape, the Sentinel-2-based BSI allows to reveal general temporal dynamics in the vegetation cover and hence changes in the condition of land cover types. The BSI values of 2018, which were affected by drought, are generally slightly higher and therefore, the corresponding image is darker red than in the other years. These subtle variations become particularly obvious when regarding the land-cover-based statistics of mean annual BSI (Fig. 29.14).

Fig. 29.14

Mean bare soil index (BSI) per land cover based on Sentinel-2 time series for the years 2017–2021. Land cover types were extracted from Land Resource International (LRI 2018). Lower BSI values show lower bare soil and higher vegetation cover fractions. In addition, annual precipitation sums (CHIRPS data, Funk et al. 2015) for the study site are shown in blue (compilation: U. Gessner)

Figure 29.14 shows the annual mean BSI for three major land cover classes of the Bojanala Platinum study site. For all years, the diagram depicts—as expected—lowest bare soil fractions in woodlands, followed by shrublands, and grasslands with the highest proportion of open soil. The 2018 and 2019 droughts lead to lower vegetation cover and thus to increased bare soil fractions for all three land cover types in 2018 and 2019, while the effect for woodland is less pronounced than for shrubland and grassland. Large fire scars can be observed as patches of high BSI values in all years. Their abundance, location, size and extent, however, vary from year to year. The drivers of fire are manifold and their interaction complex (Archibald et al. 2009). Therefore, the fire activity depicted in Fig. 29.15 is not necessarily associated with annual rainfall variation.

Fig. 29.15

Details of Sentinel-2 bare soil index (BSI) 2020 time series. The two main causes of distinctive patches of high BSI values are either fires or agricultural activities like tilling. All images show the BSI together with polygons of the MODIS burned area product (MDC64A1, Giglio et al. 2015) of 2020. Almost all dark red patches in inset b can be correlated to burned areas, whereas the patterns in inset c are mostly associated with fields of various types (data source: contains modified Copernicus Sentinel data 2017–2021, compilation: A. Hirner, F. Thonfeld)

A more detailed investigation regarding the cause of distinctive patches of high BSI was done by comparing them with the MODIS burned area product MCD64A1 (Giglio et al. 2015) for the years 2017–2021. Figure 29.15a shows the western part of the site together with MODIS burned area polygons. Most dark red patches are associated with these polygons and thus show that the BSI is sensitive to fresh burn scars. This is especially pronounced in the northwestern corner of the site, which is highlighted in Fig. 29.15b. During 2020 strong wildfires, roughly 50% of the Pilanesberg National Park (Pilanesberg Wildlife Trust 2020) were severely affected, and burned areas are evident also further to the west. Here most of the MODIS polygons clearly overlap the BSI patches. Particularly low values of maximum BSI can be seen in the south-western corner of Fig. 29.15b, where a water reservoir is located. At the north-eastern edge of Fig. 29.15b, open-cast mining areas are visible that show comparatively low maximum BSI values. There is hardly any vegetation cover there. However, the spectral signature of the exposed rock and the unweathered tailings differs from that of bare soil, resulting in lower BSI values.

A different situation is presented in the southern centre of Fig. 29.15a, which exhibits high BSI values but only a few MODIS burned area polygons. Zooming into this area (Fig. 29.15c) shows a dense pattern of arable fields, both rainfed and pivot irrigation. Some of the irrigated fields are seemingly almost always covered by vegetation and hence show some of the lowest BSI values encountered in the entire scene. However most of the rainfed fields and some pivot irrigation areas exhibit very high BSI values, indicating that the soil is exposed at least once a year, most probably during tilling. The high BSI values in this area are visible not only in 2020 but in all years covered in our investigation and confirm that this area is one of the few areas in the Bojanala Platinum site where intensive arable agriculture is practiced. Compared to this, the central part of Fig. 29.15a shows lower BSI maximum values. This region is predominantly covered by woody vegetation and hence less affected by intra-annual changes in vegetation cover. In contrast to cultivated land with large variation due to management (e.g. tillage), natural and semi-natural woody vegetation is characterized by less pronounced BSI dynamics.

4.6 Ehlanzeni

The SALDi study site 6, Ehlanzeni, is located in the summer rainfall region in the Northeast of the country. Covering the transition from the highveld in the western part of the study area to the lowveld in the central and eastern part, it is characterized by considerable diversity. Climate wise the difference in altitude results not only in considerable differences in mean air temperature, but also in precipitation amounts. Indigenous forests and plantation forest, as well as grassland is characteristic for the highveld areas, while plantations, mixed farming and extensive settlement areas are to be found in the lowveld with the exception of the part belonging to the Kruger National Park (KNP) and neighbouring private game reserves. Specially, the KNP, which has never been affected by European style agriculture and set aside as a protected area more than 100 years ago, can be considered a pristine benchmark site providing the opportunity to study land cover dynamics in semi-arid areas under near-natural conditions.

In this section, we present an example highlighting the differences of surface moisture conditions in the pristine benchmark site on the one hand and the neighbouring areas under human impact on the other. As an approach to support land management on a local and regional level, an example of multidimensional land surface change analysis in one of the most thriving local municipalities of South Africa, that is Bushbuckridge, is provided.

During 2015 and 2016, South Africa faced one of the most severe meteorological droughts due to exceptional ENSO (El Niño Southern Oscillation) conditions (Di Liberto 2016), causing a delay in the start and a strong reduction in the amount of rainfall during the summer rainfall season. Despite the Cape Town region dominating international headlines, according to Di Liberto (2016), the Mpumalanga province in northeastern South Africa was as severely impacted by this drought, despite it being less publicized.

Figure 29.16 shows a comparison of the mean bi-monthly surface moisture conditions expressed as Surface Moisture Index (SurfMI, Urban et al. 2018) at the end of the dry seasons 2016 and 2017 for the SALDi Ehlanzeni site. The approach is based on Sentinel-1 backscatter time series and a change detection algorithm developed by Wagner et al. (1999) and Naeimi et al. (2009). This radar-retrieved surface moisture data reflects the water content of either the vegetation or the soil surface depending on the surface cover dominating the Sentinel-1 backscatter. In combination with optical products on canopy cover and phenology information, it is possible to separate biophysical versus geophysical spatio-temporal patterns. The Bare Soil Index (BSI) (Rikimaru et al. 2002) derived from optical Sentinel-2 data was used to mask out areas with dense vegetation cover, that is areas with a BSI < 0.02 according to Diek et al. (2017). To avoid radiometric effects, slopes steeper than 15% (based on the Copernicus GLO-30 Digital Elevation Model, ESA 2021) were also masked out. Thus, the coloured areas in Fig. 29.16 represent pixels with only moderate slope and either no or sparse vegetation.

Fig. 29.16

Bi-monthly average of Sentinel-1 retrieved surface moisture index SurfMI at the end of the dry seasons 2016 and 2017 for SALDi project region Ehlanzeni illustrating the impact of the severe 2015/2016 drought and the recovery. White regions represent masked pixels due to either dense vegetation or sloped relief. Black polygons show protected areas, southern KNP is covering the upper right corner (data source: contains modified Copernicus Sentinel data 2016–2017, compilation: M. Urban, C. Pathe, J. Baade)

The surface moisture conditions at the end of the dry season 2016 and 2017 in the SALDi Ehlanzeni study region, dominated by the southern Kruger National Park (KNP), is used to (1) show the variation in surface moisture conditions at the end of the severe drought and (2) to document, 1 year later, the recovery of the area from the drought. At the end of the winter 2016, most areas inside and outside the KNP are characterized by exceptionally dry conditions. Hilly areas, for example around Pretoriuskop in the SW of the KNP) and the footslopes of the escarpment in the west, are characterized by slightly more moist conditions. Outside the Park, moist areas are observed most probably due to irrigation. Inside the Park, one can distinguish variations in the surface moisture conditions which coincide with geological and soil patterns. A good example is the North-South striking wide strip indicating very dry conditions close to the eastern boundary of the Park, which corresponds to the occurrence of basalts and the corresponding clayey soils as compared to the granitic rocks and sandy soils to the East and West of this feature (CGS 2019). The pattern visible in the western part of the Park strongly represents the distribution of thickets and woodlands in this area (Gertenbach 1983). Most interestingly, a year later, at the end of the 2017 dry season, these patterns are obliterated and it is postulated that the spatial variation of surface moisture conditions reflects spatial variations in rainfall amounts during the previous summer.

Located at the NW corner of the SALDi Ehlanzeni study area is the Bushbuckridge focus area of the EMSAfrica (Ecosystem Management Support for Climate Change in Southern Africa) project. This covers the Bushbuckridge Local Municipality as well as adjacent areas of the escarpment and highveld to the West and the lowveld to the East, that is the private game reserves like MalaMala and Sabie Sands as well as parts of the KNP (Fig. 29.17). This area is very diverse concerning natural resources and conditions as well as the utilization of the landscape making it a textbook example for a multi-use landscape.

Fig. 29.17

Change-Aftereffect-Trend (CAT) Analysis 2015–2021 derived from annual peak of the phenological season Tasseled Cap-Disturbance (TC-D) analysis at 10-m resolution. The white frame indicates the large-scale section shown in Fig. 29.18 (data source: contains modified Copernicus Sentinel data 2015–2021, compilation: P. Renner)

For this area, we provide an example of a land-use and land-cover (LULC) change and trend analysis based on Sentinel-2 optical earth observation data time series analysis for a period of 7 years starting in 2015. The targeted changes are savanna browning and greening trends, afforestation and deforestation, fire events, settlement expansion, as well as water level changes in reservoirs. To identify LULC change, all Sentinel-2 data acquired from 2015 to 2021 at a temporal resolution of 2–3 days and a spatial resolution of up to 10 m were analysed. To improve change detection, firstly, the annual peak of the phenological season (i.e. the growing season) acquisitions was identified.

Based on these, the Change-Aftereffect-Trend (CAT) analysis (Hird et al. 2016) developed in FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) (Frantz 2019) on the Tasseled Cap-Disturbance index (TC-D, Healey et al. 2005) was performed. The TC-D is an index created to highlight relevant vegetation changes, but due to its complex combination of multiple bands, other LULC changes can be accentuated as well. The bands used are combined into three sub-indices TC-Wetness, TC-Brightness, and TC-Greenness from which the TC-D is derived [TC-Disturbance = TC-Brightness − (TC-Greenness + TC-Wetness)]. Details of the workflow are documented in Renner (2022).

Fig. 29.18

Change-Aftereffect-Trend (CAT) Analysis 2015–2021 derived from annual peak of the phenological season Tasseled Cap-Disturbance (TC-D) analysis at 10-m resolution. Section focusing at the Inyaka Dam south of Bushbuckridge. For the color legend see Fig. 29.17 (data source: contains modified Copernicus Sentinel data 2015–2021, compilation: P. Renner)

In Fig. 29.17, the results of the CAT analysis, that is the most significant change from the time series, the average value after the change and the overall trend, are combined into an RGB composite. Overall, one can identify three broad, North-South striking zones with dominant change and trend patterns: The highveld in the West and including most part of the escarpment and the foothills, where blueish hues dominate, is characterized by a clear browning trend of the vegetation. Between Sabie, Graskop, Bushbuckridge and Hazyview forestry activities, that is clear-cuts and afforestation, are clearly detected by the dominant reddish hues. The centre of the area is characterized by bluish hues indicating browning and light greenish colours indicating stable conditions, for example on grassland, for roads and in settlements. In the East, that is the protected areas including KNP, yellow to greenish colours indicate a combination of sudden change and a trend towards greening despite rather sparse vegetation. This greening trend could however be mainly an artefact of the period of observation which started in the 2015/2016 drought.

The large-scale section focusing on the surroundings of the Inyaka Dam south of Bushbuckridge (Fig. 29.18) clearly shows the high spatial resolution of the analysis, providing the opportunity to identify spatial features in great detail. These include the road (R533) and the aisle of the power line crossing the dam, forest and other roads and settlement patterns in Bushbuckridge. In particular, boundaries between different land cover and land use areas are characterized by sharp delineations and become more noticeable and accessible to interpretation. In addition to the observations described above, it provides evidence for the application of the CAT-analysis to highly dynamic processes like the change of water levels in reservoirs. These are evident from the pinkish frame around the blue water body of the dam indicating a mixture of sudden significant change and a continuous drying trend. In the West, the CAT times series analysis of the boundary of the municipality, clearly marked in the field by a fire break, indicates marked differences in the management of forested areas on either side of the boundary. This can be attributed to more homogeneous forests plots and plot-wise management (clear-cuts and afforestation) west of the boundary. In Bushbuckridge, one can distinguish a pattern of light greenish colours indicating rather stable conditions and turquoise colours indicating change due to vegetation removal and exposure of bare soil in the framework of building activities.

5 Summary of Implementation Opportunities and Conclusions

This chapter showcased examples for Earth-observation-based support for regional policy implementations against the background of land surface change under anthropogenic and climate pressure. Based on long Sentinel-1 and Sentinel-2 time series assembled in the SALDi data cube, we present a wide range of application approaches to analyse land surface and vegetation dynamics. Table 29.5 provides an overview and summary of the surface processes and properties targeted, the methods implemented and an evaluation of possible obstacles. The table shows that all data and procedures needed for an analysis of the addressed processes are available. The necessary satellites exist and the data streams are guaranteed for decades to come. However, an operational retrieval of land surface dynamics and degradation indicators need hard- and software implementations as described in Sect. 24.4 (Chap. 24). Furthermore, expertise of analysts in the field of optical and radar remote sensing, support from local experts and in-situ data is essential. In addition, data on land properties as well as temporal changes on the surface (including, e.g. precipitation) facilitates the interpretation of the observations. Furthermore, to evaluate observed land surface trends, consistent long duration time series are needed against the background of the high inter-annual and decadal variation of precipitation characterizing semi-arid climates (e. g., Fig. 29.4).

Table 29.5 Summary of examples for observational support for regional policy implementations with respect to land surface processes and properties

As a conclusion, further research is indeed needed on validating and re-fining the existing procedures and products with respect to spatial and temporal detail and accuracy. For example investigations of radar-optical synergies have only started, and longer time-series will result in better pattern recognition. National requirements on thematic contents of the remote sensing retrieved maps have to be considered, too. A close and continuous dialogue between Earth observation experts and stakeholders is considered most beneficial for both scientific excellence and programmatic implementation for sustainable land management. The results presented showcase local to regional investigations and cannot—at this stage—be extrapolated to national assessments. Since the presented results are based on physical interactions, regularities exist (e.g. for slangbos monitoring). However, these need to be consolidated, as suggested above and adapted to specific regional concerns and requirements, especially in a diverse country like South Africa.