Estimating and examining the sensitivity of different vegetation indices to fractions of vegetation cover at different scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

Understanding the information on land conditions and especially green vegetation cover is important for monitoring ecosystem dynamics. The fraction of vegetation cover (FVC) is a key variable that can be used to observe vegetation cover trends. Conventionally, satellite data are utilized to compute these variables, although computations in regions such as the tropics can limit the amount of available observation information due to frequent cloud coverage. Unmanned aerial systems (UASs) have become increasingly prominent in recent research and can remotely sense using the same methods as satellites but at a lower altitude. UASs are not limited by clouds and have a much higher resolution. This study utilizes a UAS to determine the emerging trends for FVC estimates at an industrial plantation site in Indonesia, which utilizes fast-growing Acacia trees that can rapidly change the land conditions. First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area. The data were used to develop general land use/land cover (LULC) information for the site. Multispectral data were converted to various vegetation indices, and within the determined resolution grid (5, 10, 30 and 60 m), the fraction of each LULC type was analyzed for its correlation between the different vegetation indices (Vis). Finally, a simple empirical model was developed to estimate the FVC from the UAS data. The results show the correlation between the FVC (acacias) and different Vis ranging from R2 = 0.66–0.74, 0.76–0.8, 0.84–0.89 and 0.93–0.94 for 5, 10, 30 and 60 m grid resolutions, respectively. This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia

A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022

Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a +9.35 × 105 km2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (−2.19 × 105 km2), bare soil (−5.14 × 105 km2) and dark surfaces (−2.27 × 105 km2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (https://doi.org/10.57760/sciencedb.13287, Sun and Sun, 2023).</p

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Terrestrial vegetation-water interactions in observations and models

Im Zusammenhang mit dem globalen Klimawandel ist die Vegetation besonders wichtig, da sie die anthropogenen CO2-Emissionen aufnehmen und den Wasser- und Energiekreislauf regulieren kann. Während frühere Forschungsarbeiten wertvolle Einblicke in langfristige Veränderungen des Grüns der Vegetation und in Bezug auf die Reaktion der Vegetation auf steigende Temperaturen und atmosphärisches CO2 lieferten, sind die Wechselwirkungen zwischen Vegetation und Wasser noch immer nicht vollständig verstanden. Tatsächlich hat die Dynamik der Bodenfeuchte in der Wurzelzone einen grundlegenden Einfluss auf die Veränderung des Grüns und die Produktivität der Vegetation. Dennoch sind weder die die Empfindlichkeit der Vegetationsproduktivität gegenüber der Bodenwasserversorgung noch die funktionelle Reaktion der Vegetation (d. h. Photosynthese und Transpiration) auf Bodentrockenheitsepisoden auf globaler Ebene vollständig geklärt worden. Forschungsengpässe sind fehlende globale Beobachtungen von Vegetationsfunktion und Bodenwasservariabilität. Außerdem werden die statistischen Instrumente für die Analyse umfangreicher und vielschichtiger Daten nur unzureichend genutzt, was ein besseres Verständnis der globalen Reaktion der Vegetation auf Wasser verhindert. Gleichzeitig trägt eine bessere Kenntnis der Reaktion der Vegetation auf die Wasserversorgung zu einem besseren Verständnis des terrestrischen Wasserkreislaufs bei. Hydrologische Extremereignisse schädigen die Infrastruktur, können das menschliche Wohlergehen beeinträchtigen und treten Berichten zufolge in vielen Regionen der Welt immer häufiger und intensiver auf. Während ein Konsens über die Bedeutung meteorologischer Faktoren für die Regulierung des Wasserkreislaufs und der damit verbundenen Extremereignisse besteht, ist die Rolle der Vegetationsdynamik und -eigenschaften noch nicht ausreichend erforscht. Ihre stärkere Berücksichtigung in hydrologischen Studien bietet das Potenzial, die Prozesse, die hydrologische Extreme antreiben, genauer zu verstehen. Dadurch kann ein besseres Verständnis der Wechselwirkungen zwischen Vegetation und Wasser im Hinblick auf die Wasserempfindlichkeit der Vegetation und die Rückkopplung der Vegetation auf Klimaextreme die Genauigkeit der Landoberflächenmodellierung verbessern, was für die Verbesserung der Klimaprojektionen unerlässlich ist. Dank der jüngsten Entwicklungen im Bereich der Erdbeobachtung und der Anwendbarkeit leistungsfähiger statistischer Analysewerkzeuge ist es nun möglich, globale Wechselwirkungen zwischen Vegetation und Wasser mit noch nie dagewesener Genauigkeit zu untersuchen. In diesem Zusammenhang stützt sich diese Arbeit insbesondere auf (i) neuartige Datenprodukte wie sonneninduzierte Chlorophyllfluoreszenz oder globale Bodenfeuchte und Evapotranspiration, die aus der Hochskalierung von Stationsmessungen mit Algorithmen des maschinellen Lernens gewonnen wurden, (ii) längere Aufzeichnungen und aktualisierte Aufbereitungen etablierter Datenprodukte wie Blattflächenindex und terrestrische Wasserspeicherung und (iii) die Entwicklung erklärbarer Methoden des maschinellen Lernens, mit denen Informationen effizient aus multivariaten Datenströmen abgeleitet werden können und die darüber hinaus leicht implementier- und in ökohydrologischen Studien anwendbar sind. Basierend auf diesen Datensätzen und Werkzeugen, wird in dieser Arbeit die Empfindlichkeit der globalen Vegetation gegenüber der Bodenwasserversorgung über Raum und Zeit hinweg neu untersucht.:Summary 7 Zusammenfassung 11 1 Introduction 15 1.1 Motivation 16 1.2 Terrestrial vegetation and its relationship with water supply 18 1.2.1 Vegetation functioning 18 1.2.2 Hydro-meteorological drivers of evaporation and vegetation productivity 19 1.2.3 Vegetation structure and physiology 21 1.3 Terrestrial water cycle and its relationship with vegetation 24 1.3.1 Water balance 24 1.3.2 Vegetation regulating the water cycle 26 1.3.3 The relevance of vegetation on hydrological extremes 27 1.4 Advances in observations and models 30 1.4.1 Spaceborne remote sensing 30 1.4.2 Data-driven and physical-based models 34 1.5 Research questions and thesis outline 37 1.5.1 What is the relationship between vegetation productivity and water supply? 37 1.5.2 Can vegetation regulate hydrological extremes? 38 1.5.3 Can land surface models capture vegetation-water interplay? 40 1.5.4 Thesis outline 40 2 Global vegetation controls using multi-layer soil moisture 41 2.1 Introduction 42 2.2 Data and methods 43 2.3 Results and discussion 45 2.4 Conclusions 53 2.A Appendix 54 3 Widespread increasing vegetation sensitivity to soil moisture 70 3.1 Introduction 71 3.2 Data and methods 72 3.3 Results and discussion 78 3.4 Conclusions 85 3.A Appendix 86 4 The drought effect on vegetation physiology inferred from space 101 4.1 Introduction 102 4.2 Data and methods 104 4.3 Results and discussion 111 4.4 Conclusions 122 4.A Appendix 123 5 Drought propagation into the terrestrial water cycle 136 5.1 Introduction 137 5.2 Data and methods 139 5.3 Results and discussion 145 5.4 Conclusions 155 5.A Appendix 157 6 Drivers of high river flows in European near-natural catchments 171 6.1 Introduction 172 6.2 Data and methods 173 6.3 Results and discussion 179 6.4 Conclusion 184 6.A Appendix 186 7 Synthesis 193 7.1 What is the relationship between vegetation productivity and water supply? 194 7.2 Can vegetation regulate hydrological extremes? 197 7.3 Can land surface models capture the observed vegetation-water interplay? 199 7.4 Limitations 200 7.4.1 Difficulties in predicting SIF in tropical regions 200 7.4.2 Observing terrestrial photosynthesis and evaporation 201 7.4.3 Methods related to variable importance quantification 202 7.5 Outlook 202 7.5.1 Vegetation sensitivity to soil moisture and its implications 203 7.5.2 Vegetation functioning and related structure and physiology 203 7.5.3 Extreme events: floods and drought 204 References 206 Statement of authorship contributions 238 Acknowledgements 239 Curriculum Vitae 241 Scientific publications 242 IMPRS certificate 244In the context of global climate change, vegetation is particularly relevant as it can take up anthropogenic CO2 emissions and regulate water and energy cycling. While previous research provided valuable insights into long-term changes in vegetation greenness and in terms of the vegetation response to increasing temperature and atmospheric CO2, vegetation-water interactions are still not fully understood. In fact, root-zone soil moisture dynamics have a fundamental influence on modulating vegetation greenness and productivity. Nevertheless, neither the sensitivity of vegetation productivity to soil water supply nor the vegetation functional response (i.e., photosynthesis and transpiration) to soil drought episodes have been fully resolved at the global scale. Missing global observations of vegetation functioning and terrestrial water variability are bottlenecks, and statistical tools for analyzing large and multi-stream data are poorly exploited, preventing a better understanding of global vegetation water response. At the same time, a better knowledge of the vegetation response to the water supply in turn advances the understanding of the terrestrial water cycle. Hydrological extremes are damaging infrastructure and can affect human well-being, and have been reported to become more frequent and intense in many regions around the world. While a consensus exists regarding the importance of meteorological drivers for regulating the water cycle and related extreme events, the role of vegetation dynamics and characteristics is understudied. Its greater consideration in hydrological studies offers the potential to more accurately understand the processes driving hydrological extremes. Thereby, a better understanding on vegetation-water interactions in terms of vegetation water sensitivity and vegetation feedbacks on climate extremes can advance the accuracy of land surface modelling which is essential to improve climate projections. Thanks to recent developments in Earth observations and in the applicability of powerful statistical analyses tools, investigating global vegetation-water interactions is now possible with unprecedented accuracy. In this context, this thesis builds particularly on (i) novel data products such as Sun-induced chlorophyll fluorescence or global gridded soil moisture and evapotranspiration products obtained from upscaling station measurements with machine learning algorithms, (ii) longer records and updated processing of established data products such as leaf area index and terrestrial water storage, and (iii) the development of explainable machine learning methods which can efficiently derived information from multivariate data streams, and are furthermore implemented and readily applicable in ecohydrological studies. With these datasets and tools, this thesis revisits the sensitivity of global vegetation to soil water supply across space and time.:Summary 7 Zusammenfassung 11 1 Introduction 15 1.1 Motivation 16 1.2 Terrestrial vegetation and its relationship with water supply 18 1.2.1 Vegetation functioning 18 1.2.2 Hydro-meteorological drivers of evaporation and vegetation productivity 19 1.2.3 Vegetation structure and physiology 21 1.3 Terrestrial water cycle and its relationship with vegetation 24 1.3.1 Water balance 24 1.3.2 Vegetation regulating the water cycle 26 1.3.3 The relevance of vegetation on hydrological extremes 27 1.4 Advances in observations and models 30 1.4.1 Spaceborne remote sensing 30 1.4.2 Data-driven and physical-based models 34 1.5 Research questions and thesis outline 37 1.5.1 What is the relationship between vegetation productivity and water supply? 37 1.5.2 Can vegetation regulate hydrological extremes? 38 1.5.3 Can land surface models capture vegetation-water interplay? 40 1.5.4 Thesis outline 40 2 Global vegetation controls using multi-layer soil moisture 41 2.1 Introduction 42 2.2 Data and methods 43 2.3 Results and discussion 45 2.4 Conclusions 53 2.A Appendix 54 3 Widespread increasing vegetation sensitivity to soil moisture 70 3.1 Introduction 71 3.2 Data and methods 72 3.3 Results and discussion 78 3.4 Conclusions 85 3.A Appendix 86 4 The drought effect on vegetation physiology inferred from space 101 4.1 Introduction 102 4.2 Data and methods 104 4.3 Results and discussion 111 4.4 Conclusions 122 4.A Appendix 123 5 Drought propagation into the terrestrial water cycle 136 5.1 Introduction 137 5.2 Data and methods 139 5.3 Results and discussion 145 5.4 Conclusions 155 5.A Appendix 157 6 Drivers of high river flows in European near-natural catchments 171 6.1 Introduction 172 6.2 Data and methods 173 6.3 Results and discussion 179 6.4 Conclusion 184 6.A Appendix 186 7 Synthesis 193 7.1 What is the relationship between vegetation productivity and water supply? 194 7.2 Can vegetation regulate hydrological extremes? 197 7.3 Can land surface models capture the observed vegetation-water interplay? 199 7.4 Limitations 200 7.4.1 Difficulties in predicting SIF in tropical regions 200 7.4.2 Observing terrestrial photosynthesis and evaporation 201 7.4.3 Methods related to variable importance quantification 202 7.5 Outlook 202 7.5.1 Vegetation sensitivity to soil moisture and its implications 203 7.5.2 Vegetation functioning and related structure and physiology 203 7.5.3 Extreme events: floods and drought 204 References 206 Statement of authorship contributions 238 Acknowledgements 239 Curriculum Vitae 241 Scientific publications 242 IMPRS certificate 24

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Large-Scale Urban Impervous Surfaces Estimation Through Incorporating Temporal and Spatial Information into Spectral Mixture Analysis

With rapid urbanization, impervious surfaces, a major component of urbanized areas, have increased concurrently. As a key indicator of environmental quality and urbanization intensity, an accurate estimation of impervious surfaces becomes essential. Numerous automated estimation approaches have been developed during the past decades. Among them, spectral mixture analysis (SMA) has been recognized as a powerful and widely employed technique. While SMA has proven valuable in impervious surface estimation, effects of temporal and spectral variability have not been successfully addressed. In particular, impervious surface estimation is likely to be sensitive to seasonal changes, majorly due to the shadowing effects of vegetation canopy in summer and the confusion between impervious surfaces and soil in winter. Moreover, endmember variability and multi-collinearity have adversely impacted the accurate estimation of impervious surface distribution with coarse resolution remote sensing imagery. Therefore, the main goal of this research is to incorporate temporal and spatial information, as well as geostatistical approaches, into SMA for improving large-scale urban impervious surface estimation. Specifically, three new approaches have been developed in this dissertation to improve the accuracy of large-scale impervious surface estimation. First, a phenology based temporal mixture analysis was developed to address seasonal sensitivity and spectral confusion issues with the multi-temporal MODIS NDVI data. Second, land use land cover information assisted temporal mixture analysis was proposed to handle the issue of endmember class variability through analyzing the spatial relationship between endmembers and surrounding environmental and socio-economic factors in support of the selection of an appropriate number and types of endmember classes. Third, a geostatistical temporal mixture analysis was developed to address endmember spectral variability by generating per-pixel spatial varied endmember spectra. Analysis results suggest that, first, with the proposed phenology based temporal mixture analysis, a significant phenophase differences between impervious surfaces and soil can be extracted and employed in unmxing analysis, which can facilitate their discrimination and successfully address the issue of seasonal sensitivity and spectral confusion. Second, with the analyzed spatial distribution relationship between endmembers and environmental and socio-economic factors, endmember classes can be identified with clear physical meanings throughout the whole study area, which can effectively improve the unmixing analysis results. Third, the use of the spatially varying per-pixel endmember generated from the geostatistical approach can effectively consider the endmember spectra spatial variability, overcome the endmember within-class variability issue, and improve the accuracy of impervious surface estimates. Major contributions of this research can be summarized as follows. First, instead of Landsat Thematic Mapper (TM) images, MODIS imageries with large geographic coverage and high temporal resolution have been successfully employed in this research, thus making timely and regional estimation of impervious surfaces possible. Second, this research proves that the incorporation of geographic knowledge (e.g. phonological knowledge, spatial interaction, and geostatistics) can effectively improve the spectral mixture analysis model, and therefore improve the estimation accuracy of urban impervious surfaces

Small-Area Population Estimation: an Integration of Demographic and Geographic Techniques

Knowledge of detailed and accurate population information is essential to analyze and address a wide variety of socio-economic, political, and environmental issues and to support necessary planning practices for both public agencies and the private sector. However, such important data are generally only available once every decade through the National Census. Moreover, populations in some rapidly-developing areas may increase quickly, such that this ten-year frequency does not meet the needs of these areas. Therefore, a cost-effective method for population estimation is necessary. To address this issue, this research integrated geographic, sociological, and demographic theories and exploited remotely sensed imagery and geographic information system (GIS) datasets to derive better population estimates at the census block level, the finest level of the national census. Specifically, three new approaches have been proposed in this dissertation to assist in the improvement of small-area population estimation accuracy. First, existing remotely sensed and GIS data have been adopted to estimate two major components of a demographic framework, including the redistribution of newly built dwelling units from the aggregated geographic level to the census block level and the estimation of persons per household (PPH) at such a fine scale. Second, in addition to the use of existing data, new urban environmental indicators were also extracted and employed to improve population estimation. In particular, to implement the automatic enumeration for individual housing units, a new spectral index, biophysical composition index (BCI), has been proposed to derive impervious surface information, a desirable urban environmental parameter. Third, using the extracted high-resolution urban environmental information and GIS data, a new bottom-up method was developed for small-area population estimation at the census block level by incorporating these high-resolution data into the demographic framework. Analyses of the results suggest three major conclusions. First, existing GIS spatial factors, together with demographic information, can assist in improving the accuracy of small-area population estimation. Second, the BCI has a closer relationship with impervious surface area than do other popular indices. Moreover, it was shown to be the most effective index of the four evaluated for separating impervious surfaces and bare soil, which consequently might assist in more accurately deriving fractional land cover values. Third, the use of the new environmental indicators extracted from remote sensing imagery and GIS data and the integration of demographic and geographic approaches has significantly improved the estimation accuracy of housing unit (HU) numbers, PPH, and population counts at the census block level. Therefore, this research contributes to both the remote sensing and applied demography fields. The contribution to the remote sensing field lies in the development of a novel spectral index to characterize urban land for monitoring and analyzing urban environments. This index provided more significant separability between impervious surfaces and bare soil than did other existing indices. Moreover, three major contributions have been made in the field of applied demography: 1) the generation of accurate HU estimates using high-resolution remote sensing and GIS datasets, 2) the development of a model to derive an accurate PPH estimate, and 3) the improvement of small-area population estimation accuracy through the integration of geographic and demographic approaches

An integrated approach to grassland productivity modelling using spectral mixture analysis, primary production and Google Earth Engine

Thesis (MA)--Stellenbosch University, 2020.ENGLISH ABSTRACT: Grassland degradation can have a severe impact on condition, productivity and consequently grazing potential. Current conventional methods for monitoring and managing grasslands are time-consuming, destructive and not applicable at large-scale. These constraints could be addressed using a remote sensing (RS)-based approach, however, current RS-based approaches also have technological and scientific limitations in the context of grassland management. The inability of RS-based primary production models to discriminate between herbaceous and woody production at sub-pixel level poses constraints for use in grazing capacity (GC) calculation. The integration of fractional vegetation cover (FVC) is posed as a promising solution, specifically estimation using spectral mixture analysis (SMA). Current grassland monitoring approaches are limited by the technological constraints of traditional, desktop-based RS approaches, but the implementation of analysis in a Google Earth Engine (GEE) web application can address these limitations by providing dynamic, continuous productivity estimates. Field data collection and analysis of biophysical parameters were performed to establish crucial relationships between vegetation productivity and RS signals. Biophysical parameters obtained include FVC, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and grass dry matter (DM) production. An important outcome was the improvement of the normalised difference vegetation index (NDVI) and fAPAR regression relationship, achieved by scaling fAPAR using the proportion of green, living biomass. The relationship proved useful in subsequent vegetation productivity modelling. The potential of SMA for FVC estimation using medium resolution imagery (Landsat 8 and Sentinel-2) and relatively few field points, was explored. A linear spectral mixture model (LSMM) was calibrated, implemented and evaluated on accuracy and transferability. A number of bands and spectral indices were identified as core features, specifically the dry bare-soil index (DBSI). DBSI improved discrimination between bare ground and dry vegetation, a common challenge in semi-arid conditions. The calibrated LSMM performed well, with Sentinel-2 providing the most accurate results. The research proved the transferability of the LSMM approach, as accurate FVC estimates were obtained for both arid, dry season conditions and green, growing season conditions. The LSMM-estimated FVC was combined with primary production to improve GC calculation for grassland and rangelands. Annual grassland production was calculated using the Regional Biosphere Model (RBM). Although a water stress factor is a well-known source of uncertainty, the research found its inclusion crucial to the transferability of the model between different climatic conditions. FVC was used to determine the grazable primary production from RBM estimates, thus mitigating the effects of woody components on GC calculations. A comparison of model-estimated GC to the most recent national GC map showed good agreement. Slight discrepancies were likely due to the inability of the model to include species composition and palatability in GC calculations. The final FVC-integrated productivity model was implemented in a GEE web app to demonstrate the practical contribution of the research for continuous, dynamic, multi-scale and sustainable grassland management. Overall, the findings of the research provide valuable insights into improving RS-based modelling of grassland condition and productivity. Operationalisation of this research can aid in identifying potential degradation, highlighting regions vulnerable to food shortages and establishing sustainable productivity levels. Recommendations include investigating alternative methods for estimating water stress and exploring the incorporation of species composition in GC calculation using RS.AFRIKAANSE OPSOMMING: Agteruitgang van grasvelde kan 'n ernstige invloed op kondisie, produktiwiteit en gevolglik weidingspotensiaal hê. Huidige konvensionele metodes vir die monitering en bestuur van grasvelde is tydrowend, vernietigend en nie op groot skaal toepasbaar nie. Hierdie beperkinge kan met behulp van 'n afstandwaarnemings (AW)-gebaseerde benadering aangespreek word, maar huidige AW-metodes het egter ook tegnologiese en wetenskaplike beperkings, veral in die konteks van veldbestuur. Die onvermoë van AW-gebaseerde primêre produksiemodelle om tussen kruidagtige en houtagtige produksie op sub-pixelvlak te onderskei, hou beperkings in vir die berekening van drakapasiteit (DK). Die integrasie van fraksionele plantegroeibedekking (FPB) word aangebied as 'n belowende oplossing. Beraming van FPB deur gebruik te maak van spektrale mengselanalise (SMA) het veral potensiaal. Huidige benaderings vir die monitering van grasvelde word beperk deur die tegnologiese beperkings van tradisionele, rekenaargebaseerde AW-metodes, maar die implementering van analise in 'n Google Earth Engine (GEE) webtoepassing kan hierdie beperkings aanspreek deur dinamiese, deurlopende produktiwiteitsramings te verskaf. Velddata is ingesamel en analise van biofisiese parameters is uitgevoer om belangrike verwantskappe tussen plantproduktiwiteit en AW-seine te bepaal. Die biofisiese parameters sluit in FPB, blaaroppervlakte-indeks (BOI), fraksie van geabsorbeerde fotosinteties aktiewe bestraling (fAFAB) en droë materiaal (DM) produksie. Die verbetering van die genormaliseerde verskilplantegroei-indeks (NVPI) en fAFAB -regressie-verhouding, wat verkry is deur fAFAB te skaleer met behulp van die hoeveelheid groen, lewende biomassa was ‘n belangrike uitkoms. Die verwantskap was nuttig in die daaropvolgende modellering van plantegroei. Die potensiaal van SMA vir die bepaling van FPB deur middel van medium resolusiebeelde (Landsat 8 en Sentinel-2) met relatief min veldpunte is ondersoek. 'n Lineêre spektrale mengelmodel (LSMM) is gekalibreer, geïmplementeer en vir akkuraatheid en oordraagbaarheid geëvalueer. 'n Aantal bande en spektrale indekse is as kernkenmerke geïdentifiseer, spesifiek die droë kaal-grondindeks (DKGI). DKGI het die onderskeid tussen kaal grond en droë plantegroei, 'n algemene uitdaging in semi-droë landskappe, verbeter. Die gekalibreerde LSMM het goed gevaar, met Sentinel-2 wat die akkuraatste resultate gelewer het. Die navorsing het bewys dat die LSMM-benadering oorgedra kan word, aangesien akkurate FPB-ramings vir beide droë seisoen en groen, groeiseisoen toestande verkry is. Die LSMM-beraamde FPB is met primêre produksie ramings gekombineer om die DK-berekening vir grasveld te verbeter. Die jaarlikse grasveldproduksie is met behulp van die Streeks Biosfeer Model (SBM) bereken. Alhoewel 'n waterstresfaktor 'n bron van onsekerheid is, het die navorsing bevind dat dit die gebruik daarvan vir die oordraagbaarheid van die model tussen verskillende klimaatstoestande belangrik is. FPB is gebruik om die weibare primêre produksie volgens SBMramings te bepaal, en het die effekte van houtagtige komponente op DK-berekeninge verminder. 'n Vergelyking van die gemodelleerde DK met die nuutste nasionale DK-kaart het 'n goeie ooreenkoms getoon. Klein afwykings was waarskynlik te wyte aan die onvermoë van die model om spesiesamestelling en eetbaarheid by DK-berekeninge in te sluit. Die finale FPB-geïntegreerde produktiwiteitsmodel is in 'n GEE webtoep geïmplementeer om die praktiese bydrae van die navorsing vir deurlopende, dinamiese, meervoudige en volhoubare grasveldbestuur te demonstreer. In die geheel bied die bevindinge van die navorsing waardevolle insigte in die verbetering van die AW-gebaseerde modellering van veldtoestand en produktiwiteit. Operasionalisering van hierdie navorsing kan tot die identifisering van potensiële agteruitgang, die uitlig van streke wat kwesbaar is vir voedseltekorte en die bepaling van volhoubare produktiwiteitsvlakke bydra. Aanbevelings sluit in die ondersoek van alternatiewe metodes vir die beraming van waterstres en die gebruik van spesiesamestelling in DK-berekening met behulp van AW.Master

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Alpine sensor network system for high-resolution spatial snow and runoff estimation

Monitoring the snowpack is crucial for water management, flood control and hydropower optimization. Traditional regression methods often result in low accuracy runoff predictions.Existing ground-based real-time measurement systems are in majority installed at low elevations with poor physiographic representation. This thesis presents a system for better Snow Water Equivalent (SWE) and runoff estimation. The autonomous end-to-end Wireless Sensor Network (WSN) that leverages the Internet of Things (IoT) technology provides mountain hydrology measurements in near real-time. At its core lies an ultra-low power, radio channel-hoping, and self-organizing mesh secured with a rugged weather-sealed design, data replication and remote network health monitoring. Three WSNs are installed throughout the North Fork of the Feather River in Northern California upstream of the Oroville dam. Elevation, aspect, slope and vegetation determine network locations. Data show considerable spatial variability of snow depth, and that existing operational autonomous systems are non-representative spatially, with biases reaching up to 50%. Combined with existing systems, WSNs better detect precipitation timing and phase, monitor sub-daily dynamics of infiltration and surface runoff, and inform hydro power managers about actual ablation and end-of-season date across the landscape. A wet and dry year exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Elastic Net regression shows that dominant features at the sub-km2 scale are site-dependent and differ from the watershed scale. Based on the Nearest Neighbor (NN) with a Landsat assimilated historical product, explanatory variables consistently explain up to 90% of the variance in the watershed-scale SWE for both years. Lagged cross correlation of snowmelt with stream flow measurements show improvement of up to 100% compared to existing systems. Ensemble Optimal Interpolation (EnOI) update of background SWE fields from Landsat and LiDAR products provide accurate high resolution estimates of spatial SWE for areas with parsimonious sensors. Results show a minimum RMSE of 22% and 30% at 90 m and 50 m resolutions respectively. Compared with SNODAS, reduction in error is up to 55% and 80%, with LiDAR as reference