Remote sensing based assessment of small wetlands in East Africa

Small wetlands in East Africa have in the past few decades become focal points of a broad spectrum of agricultural production and other land-uses. Climate change and population growth are the major factors attributing to increasing use and change of the wetlands. This study aimed at detecting the distribution and extent of small wetlands in Tanzania and Kenya, classifying them into different types, identifying their use patterns and quantifying changes that have taken place from 1976 to 2003. Field and aerial surveys were conducted; microwave (ALOS-PALSAR, ENVISAT-ASAR, and TerraSAR-X) and optical (LANDSAT and aerial photographs) data, were used to detect spatial distribution of the wetlands using automated and semi automated techniques. Time series LANDSAT images were applied in classification and change detection by post classification comparison (PCC), change vector analysis (CVA) and land use change mapper (LCM). Maps and socio-economic data were also gathered. Driving forces of change were determined qualitatively using group discussions with key informants. Two types of small wetlands were mainly identified, inland valleys located in the humid highlands and covering 87% of the total surveyed area as well as floodplains in sub-humid lowlands and semi-arid highlands covering the remaining 13%. Eight major land cover and uses were identified with accuracies between 82.76 and 95.17%. Cropland was a dominant land use occupying 57% of the inland valleys and 35% in the flood plains; others included open water, floating vegetation, permanent papyrus swamps, semi-natural vegetation, grazing, shrubs, settlements and bare land. The cover and uses are unevenly distributed between the types and sites. The major change detected was expansion of cropped land at the expense of natural vegetation. This accounted for 56% of the change in the highland flood plain and 52% in the lowland floodplain. Shrubs proliferated in all wetlands, which is indicated by more than 50% compared to their area coverage in 1976. Climate change, population increase, unemployment, market access, wetland physical access and insufficient knowledge on the use are among the proximate causes of the wetland changes. Underlying factors like poor enforcement of wetland law and policy in Kenya and lack of the same in Tanzania have accelerated these changes. Combinations of remote sensing data and image processing methods played an important role in achieving the objectives of the study. Optical data proved to be very useful in delineation of small wetlands while microwave data delineated larger areas. The spatial resolution of the images has also proved to be a key factor in studies of small wetlands. To ameliorate the wetlands, it is recommended that a balance is attained between the use and conservation. Policy formulation and law enactment in Tanzania and enforcement of the existing policy and law in Kenya is seen to support wise use. Awareness creation is also important to lessen the over and inappropriate utilization of the wetlands

Encoding remotely sensed time series data as two-dimensional images for urban change detection using convolution neural networks

Thesis (MSc)--Stellenbosch University, 2021.ENGLISH ABSTRACT: Urban expansion is the most pervasive form of land cover change in South Africa. A method that can effectively detect and indicate areas that have a higher probability of displaying urban change will therefore be a valuable asset to analysts. That is why it is critical to derive a rapid framework that can accurately map urban change. An alternative remote sensing approach that uses multi-temporal time series data and deep learning techniques has been proposed as a potential method for performing a successful urban change detection. The interdisciplinary scientific field of computer vision holds a framework for encoding time-series data as two-dimensional (2D) images for input to a convolution neural network (CNN). Traditional image classifications techniques and more recent studies that have deployed machine learning and deep learning classifiers (namely support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN), long short-term memory (LSTM) and CNN) have been used for urban land cover classification. In this study, a unique framework proposed within computer vision that exploits Gramian angular fields (GAF) and Markov transition fields (MTF) as the transformations for encoding time series data as 2D imagery prior to deep learning classification is investigated for urban change detection. Two main experiments were carried out, both of which utilised the proposed framework for performing an effective urban change detection. The first experiment used coarse resolution data derived from Pretoria using MODIS 500m and 250m normalised difference vegetation index (NDVI). The proposed framework was then deployed, and Gramian angular summation field (GASF), Gramian angular difference field (GADF), and MTF transformations used to encode the time series data. A concatenated encoded image containing the information from all three transformations was formed and was run alongside the three individual transformations. Multiple pre-trained CNN architectures (namely ResNet, DenseNet, InceptionV3, InceptionResNetV2, VGG and MobileNet) were used, from which an urban change detection was derived. It was established that the concatenated images yielded the highest accuracy at 91% and 93% for the 500m and 250m resolution datasets, respectively. The proposed framework was compared to a current state-of-the-art time series classifier (LSTM) to illustrate the effectiveness of encoding and processing deep learning classifiers. The results also outperformed that of other urban change detections studies conducted in South Africa. The second experiment made use of higher resolution Sentinel-2 data derived from a resampled 30m resolution NDVI product of Pretoria. Several investigations were made into the influencing elements that affect the performance of the urban change detection. These were the spatial and temporal resolutions, training data size and different classification schemes. Using the proposed Stellenbosch University https://scholar.sun.ac.za iv framework from the first experiment, the spatial and temporal resolutions were tested. The results showed that an increase in spatial or temporal resolution will have a positive effect on the performance. The 30m resolution dataset yielded a 4% increase over the 250m resolution data tested in the first experiment. Altering the time-series length (TSL) from 32 to 82, the accuracy increased from 96% to 98%, respectively. It was also illustrated that by increasing the amount of training data, one could improve the performance of the change detection. Multiple classifications were performed, and the accuracy assessed using a confusion matrix. It was established that a 70%+ minimum pixel probability and the majority ensemble classifier performed the best. The frameworks generalisability was tested at three different locations (Durban, Gqeberha, and Khayelitsha), and was able to generalise using the Durban dataset. However, the models were unable to generalise using the Gqeberha, and Khayelitsha datasets due to the diverse ecological and climatic properties. The experiments showed that deploying a computer vision framework of encoding multi-temporal time series data as two-dimensional images for an urban change detection using CNN classifications is, in fact possible, and proved to be one of the most effective urban change detection methods performed in South Africa. However, it is recommended that further research deploys a signature extension approach for training the models in order to improve the generalisability. Additional research into using Landsat8 and increased TSL datasets is also recommended.AFRIKAANSE OPSOMMING: Stedelike uitbreiding is die heersende vorm van grondbedekkingsverandering in Suid-Afrika. 'n Metode om gebiede met 'n groter waarskynlikheid van stedelike veranderinge te toon of effektief te kan kan opspoor en aandui, sal 'n waardevolle bate vir ontleders wees. Daarom is dit van kritieke belang om 'n minder tydrowende raamwerk op te stel wat stedelike verandering akkuraat kan karteer. 'n Alternatiewe afstandswaarnemingsbenadering wat multi-temporale tydreeksdata en diepleertegnieke gebruik, word voorgestel as 'n moontlike metode vir suksesvolle opsporing van stedelike veranderinge. Die interdissiplinere wetenskaplike veld van rekenaarvisie bevat 'n raamwerk vir die kodering van tydreeksdata as tweedimensionele beelde wat as invoer dien vir 'n konvolusionele neurale netwerk (CNN). Tradisionele beeldklassifikasietegnieke en meer onlangse studies wat masjienleer- en diepleerklassifiseerders (naamlik ondersteuningsvektormasjien (SVM), ewekansige woud (RF), k-naaste buurtklassifiseerder (kNN), lang-kort-termyn-geheue (LSTM) en CNN) word dikwels gebruik vir klassifikasie van stedelike grondbedekkings. In hierdie studie word 'n unieke raamwerk voorgestel wat binne rekenaarvisie ontwikkel is wat Gramian-hoekvelde (GAF) en Markov-oorgangsvelde (MTF) benut as ‘n transformasie in die kodering van tydreeksdata as tweedimensionele beelde voordat diepleerklassifikasie ondersoek word vir die opsporing van stedelike veranderinge . Twee eksperimente is uitgevoer, wat beide die voorgestelde raamwerk gebruik het vir opsporing van stedelike veranderinge. Die eerste eksperiment het gegewens gebruik van growwe resolusie wat uit Pretoria verkry is, met behulp van MODIS 500m en 250m genormaliseerde verskil plantegroei-indeks (NDVI) data. Die voorgestelde raamwerk is daarna ontplooi deur Gramian hoeksomvelde (GASF), Gramian hoekverskilvelde (GADF) en MTF transformasies te gebruik om die tydreeksdata te kodeer. 'n Saamgevoegde gekodeerde beeld wat al drie transformasies bevat, is gemaak en saam met die drie individuele transformasies analiseer. Veelvuldige vooraf-opgeleide CNN-argitekture (naamlik ResNet, DenseNet, InceptionV3, InceptionResNetV2, VGG en MobileNet) is gebruik, waaruit die stedelike verandering afgelei is. Daar is vasgestel dat die saamgevoegde beelde die hoogste akkuraatheid gelewer het met 91% en 93% vir die datastelle van onderskeidelik 500m en 250m. Die voorgestelde raamwerk is vergelyk met 'n huidige moderne tydreeksklassifiseerder (LSTM) om die doeltreffendheid van kodering en verwerking van 'n diepleerklassifiseerder te illustreer. Die resultate was ook beter as die van ander stedelike veranderingstudies in Suid-Afrika. Die tweede eksperiment het gebruik gemaak van Sentinel-2-data met 'n hoer resolusie, ook afgelei van 'n NDVI-produk vir Pretoria, verwerk na 30m. Verskeie ondersoeke is gedoen om vas te stel wat die faktore is wat die akkuraatheid van die opsporing van stedelike verandering beinvloed, byvoorbeeld, die ruimtelike en temporale resolusies, die grootte van die opleidingsdata en verskillende klassifikasie skemas. Met behulp van die voorgestelde raamwerk van die eerste eksperiment, is die effek van ruimtelike en temporale resolusies getoets. Die resultate het getoon dat 'n toename in ruimtelike of temporale resolusie 'n positiewe uitwerking op die akkuraatheid sal hê. Die datastel met 'n resolusie van 30m het 'n toename van 4% opgelewer in vergelyking met die resolusiedata van 250m wat in die eerste eksperiment getoets is. Deur die tydreekslengte (TSL) van 32 na 82 te verander, het die akkuraatheid toegeneem van 96% tot 98%. Die studie het ook aangedui dat die akkuraatheid van veranderingopsporing sou verbeter kon word deur die hoeveelheid opleidingsdata te vermeerder. Veelvuldige klassifikasie skemas is uitgevoer en die akkuraatheid met behulp van 'n verwarringsmatriks getoets. Daar is vasgestel dat 'n 70%+ minimum pixelwaarskynlikheid en die meerderheidsensemble-klassifiseerder die beste gevaar het. Die veralgemeenbaarheid van die raamwerke is op drie verskillende plekke (Durban, Gqeberha en Khayelitsha) getoets, maar kon slegs in Durban veralgemeen word. Die modelle kon nie stedelike verandering met Gqeberha- en Khayelitsha -datastelle optel nie weens die uiteenlopende ekologiese en klimaatseienskappe. Die eksperimente het getoon dat die implementering van 'n rekenaarvisie raamwerk vir die kodering van multi-temporale tydreeksdata as tweedimensionele beelde vir die opsporing van stedelike veranderinge met behulp van CNN-klassifikasies in werklikheid moontlik is en een van die mees doeltreffende opsporingstegnieke vir stedelike veranderinge in Suid-Afrika kan wees. Dit word egter aanbeveel dat verdere navorsing 'n uitbreidingsbenadering gebruik vir die opleidingsdata vir die modelle om die veralgemenbaarheid te verbeter. Bykomende navorsing oor die gebruik van Landsat8 en verhoogde TSL-datastelle word ook aanbeveel.Master

An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa

The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019

Advances in remote sensing applications for urban sustainability

Abstract: It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

Exploiting satellite SAR for archaeological prospection and heritage site protection

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian “Valley of the Kings” (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones