Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral Imagery

Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step.

Identification of the Optimal Season and Spectral Regions for Shrub Cover Estimation in Grasslands

Woody plant encroachment (WPE), the expansion of native and non-native trees and shrubs into grasslands, is a less studied factor that leads to declines in grassland ecosystem health. With the increasing application of remote sensing in grassland monitoring and measuring, it is still difficult to detect WPE at its early stages when its spectral signals are not strong enough. Even at late stages, woody species have strong vegetation characteristics that are commonly categorized as healthy ecosystems. We focus on how shrub encroachment can be detected through remote sensing by looking at the biophysical and spectral properties of the WPE grassland ecosystem, investigating the appropriate season and wavelengths that identify shrub cover, testing the spectral separability of different shrub cover groups and by revealing the lowest shrub cover that can be detected by remote sensing. Biophysical results indicate spring as the best season to distinguish shrubs in our study area. The earliest shrub encroachment can be identified most likely only when the cover reaches between 10% and 25%. A correlation between wavelength spectra and shrub cover indicated four regions that are statistically significant, which differ by season. Furthermore, spectral separability of shrubs increases with their cover; however, good separation is only possible for pure shrub pixels. From the five separability metrics used, Transformed divergence and Jeffries-Matusita distance have better interpretations. The spectral regions for pure shrub pixel separation are slightly different from those derived by correlation and can be explained by the influences from land cover mixtures along our study transect.

A Review of Terrestrial Carbon Assessment Methods Using Geo-Spatial Technologies with Emphasis on Arid Lands

Geo-spatial technologies (i.e., remote sensing (RS) and Geographic Information Systems (GIS)) offer the means to enable a rapid assessment of terrestrial carbon stock (CS) over large areas. The utilization of an integrated RS-GIS approach for above ground biomass (AGB) estimation and precision carbon management is a timely and cost-effective solution for implementing appropriate management strategies at a localized and regional scale. The current study reviews various RS-related techniques used in the CS assessment, with emphasis on arid lands, and provides insight into the associated challenges, opportunities and future trends. The study examines the traditional methods and highlights their limitations. It explores recent and developing techniques, and identifies the most significant RS variables in depicting biophysical predictors. It further demonstrates the usefulness of geo-spatial technologies for assessing terrestrial CS, especially in arid lands. RS of vegetation in these ecosystems is constrained by unique challenges specific to their environmental conditions, leading to high inaccuracies when applying biomass estimation techniques developed for other ecosystems. This study reviews and highlights advantages and limitations of the various techniques and sensors, including optical, RADAR and LiDAR, that have been extensively used to estimate AGB and assess CS with RS data. Other new methods are introduced and discussed as well. Finally, the study highpoints the need for further work to fill the gaps and overcome limitations in using these emerging techniques for precision carbon management. Geo-spatial technologies are shown to be a valuable tool for estimating carbon sequestered especially in difficult and remote areas such as arid land.

Mapping Seriphium plumosum encroachment and interaction with wildfire and environmental factors in a protected mountainous grassland

Accurate information on the distribution of invasive native species could provide important and effective procedures for managing savannah environment, especially in sensitive mountainous grasslands. The study detected and mapped Seriphium plumosum within a mountainous landscape and linked the georeferenced occurrence data with the corresponding site-specific environmental factors to predict the locations of unknown populations using a MaxEnt niche model. We also explored the relative contribution in terms of species interaction with its surrounding biophysical environment. The AUC value of 0.876 estimated for the species distribution is an indication of a good model fit. Our findings indicated that Seriphium plumosum preferred areas with higher temperature associated with recurrence fire events and limited soil moisture. It was concluded that the projected conditions of increasing temperature and fire events could promote widespread gain of niche space for Seriphium plumosum while at the same time altering community structure and composition, hydrological properties, and other vital ecosystem services in the study area.

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife.

Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.

Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery

Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producer’s and user’s accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier.

Determinants of woody encroachment and cover in African savannas

Savanna ecosystems are an integral part of the African landscape and sustain the livelihoods of millions of people. Woody encroachment in savannas is a widespread phenomenon but its causes are widely debated. We review the extensive literature on woody encroachment to help improve understanding of the possible causes and to highlight where and how future scientific efforts to fully understand these causes should be focused. Rainfall is the most important determinant of maximum woody cover across Africa, but fire and herbivory interact to reduce woody cover below the maximum at many locations. We postulate that woody encroachment is most likely driven by CO2 enrichment and propose a two-system conceptual framework, whereby mechanisms of woody encroachment differ depending on whether the savanna is a wet or dry system. In dry savannas, the increased water-use efficiency in plants relaxes precipitation-driven constraints and increases woody growth. In wet savannas, the increase of carbon allocation to tree roots results in faster recovery rates after disturbance and a greater likelihood of reaching sexual maturity. Our proposed framework can be tested using a mixture of experimental and earth observational techniques. At a local level, changes in precipitation, burning regimes or herbivory could be driving woody encroachment, but are unlikely to be the explanation of this continent-wide phenomenon.

Quantifying the woody component of savanna vegetation along a density gradient in the Kalahari Bushveld: a comparison of two adapted point-centered quarter methods

Bush encroachment is a serious problem in savanna rangelands of southern Africa. There is a strong interest in practical and reliable assessment methods to quantify related vegetation changes in the woody layer such as the widely applied point-centred quarter (PCQ) methods. Several variations of these distance methods exist but their results differ due to differences in sampling effort and methodological accuracy. The aim of this study was to compare the performance of two recently developed adapted PCQ methods. These methods were used to estimate density, productivity and diversity of the woody layer of a semiarid savanna along a degradation gradient in the Kalahari rangelands. It was found that both adapted PCQ methods (APCQ10 and APCQ20, with the APCQ20 method using less recording points but a larger sampling area and higher sampling intensity per recording point) provided similar results for density, phytomass, available browse and browsing capacity in open, dense and encroached savanna types. Significant differences between the methods were obtained in differentiating height classes, which were, however, largely restricted to the woody layer above 2 m in open savanna types. There, applying the APCQ20 method avoided an under-sampling of larger shrubs and trees and increased precision in data assessment. This was confirmed by a better representation of species frequency distributions, as well as the density, phytomass and diversity status of the woody layer. These differences disappeared as the woody vegetation became denser with the APCQ10 method providing similar results to that of the APCQ20 method in densely vegetated and encroached savanna types. From a practical point of view, the APCQ10 method has a range of advantages in dense vegetation, where restricted movement impedes effective data collection. It is concluded that the APCQ20 method should be used to quantify open savanna communities, whereas the APCQ10 method is more suitable in dense stands of >1200 tree equivalents ha–1. Overall, the two APCQ methods were effective for assessing and monitoring woody savanna layers for management purposes but, for research, their accuracy still needs to be investigated in comparison to other assessment methods.

Trends in soil erosion and woody shrub encroachment in Ngqushwa district, Eastern Cape Province, South Africa

Woody shrub encroachment severely impacts on the hydrological and erosion response of rangelands and abandoned cultivated lands. These processes have been widely investigated at various spatial scales, using mostly field experimentation. The present study used remote sensing to investigate spatial and temporal patterns of soil erosion and encroachment by a woody shrub species, Pteronia incana, in a catchment in Ngqushwa district, Eastern Cape Province, South Africa between 1998 and 2008. The extreme categories of soil erosion and shrub encroachment were mapped with higher accuracy than the intermediate ones, particularly where lower spatial resolution data were used. The results showed that soil erosion in the worst category increased simultaneously with dense woody shrub encroachment on the hill slopes. This trend is related to the spatial patterning of woody shrub vegetation that increases bare soil patches--leading to runoff connectivity and concentration of overland flow. The major changes in soil erosion and shrub encroachment analysed during the 10-year period took place in the 5-9° slope category and on the concave slope form. Multi-temporal analyses, based on remote sensing, can extend our understanding of the dynamics of soil erosion and woody shrub encroachment. They may help benchmark the processes and assist in upscaling field studies.

The historical ecology of Namibian rangelands: vegetation change since 1876 in response to local and global drivers

The influence of both local and global drivers on long-term changes in the vegetation of Namibia's extensive rangelands was investigated. Fifty-two historical photographs of the Palgrave Expedition of 1876 were re-photographed and used to document changes over more than 130 years, in grass, shrub and tree cover within three major biomes along a 1200km climatic gradient in central and southern Namibia. We showed that patterns of change are correlated with mean annual precipitation (MAP) and that below a threshold of around 250mm, vegetation has remained remarkably stable regardless of land-use or tenure regime. Above this threshold, an increase in tree cover is linked to the rainfall gradient, the legacies of historical events in the late 19th century, subsequent transformations in land-use and increased atmospheric CO(2). We discuss these findings in relation to pastoral and settler societies, paleo- and historical climatic trends and predictions of vegetation change under future global warming scenarios. We argue that changes in land-use associated with colonialism (decimation of megaherbivores and wildlife browsers; fire suppression, cattle ranching), as well as the effects of CO(2) fertilisation provide the most parsimonious explanations for vegetation change. We found no evidence that aridification, as projected under future climate change scenarios, has started in the region. This study provided empirical evidence and theoretical insights into the relative importance of local and global drivers of change in the savanna environments of central and southern Namibia and global savanna ecosystems more generally.