Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

Special Issue Microwave Remote Sensing.Abstract: During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). Keywords: Composite multispectral modeling; SAR; classification; SatPhenClass algorithm; minimum dataset; cereal yield; phenology; LAI-bridge; CAP; IACS; FLPISPeer reviewe

Cereal yield modeling in Finland using optical and radar remote sensing : Special Issue Global Croplands

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Spring wheat (Triticum aestivum L.) ideotype responses to elevated CO2 and temperature levels : A cereal yield modeling study using satellite information

The wheat (Triticum aestivum L.) ideotype concept is defined as the optimal wheat genotype with a maximum potential for grain yield under optimal growing conditions. The ideotype concept has been widely reviewed in agronomy research for a variety of crops. The wheat ideotype with optimum yielding capacity and with adaptation to elevated atmospheric CO2 concentrations should have rapid canopy closure at the tillering stage and a long grain-filling period, with high temperature sum requirements from anthesis to maturity. The CERES-Wheat modeling results using the non-limited Open Top Chamber (OTC) data (1992-1994) indicated, when using the CERES-Wheat potential, non-limiting model, that the simulated grain yield of high-latitude cv. Polkka increased under elevated CO2 conditions (700 ppm) to 142 % and to 161 % for the mid-European cv. Nandu, as compared with the reference level (ypot, 100%). The corresponding observed average 1992-1994 increase in OTC experiments was lower (112 % cv. Polkka). The elevated temperature (+ 3 °C) accelerated phenological development, especially during the generative phase, according to the CERES-Wheat model estimations. The yield of cv. Polkka decreased on average to 80.4 % (59 % cv. Nandu, vs. 84 % OTC observed) due to temperature increase from the simulated reference level (ypot, 100%). When modeling the elevated temperature and CO2 interaction, the increase in grain yield under elevated CO2 was reduced by the elevated temperature, accelerating phenological development, especially during the generative phase, resulting in a shorter grain-filling period. The combined CO2 and temperature effect increased cv. Polkka grain yield to 106 % (107 % for cv. Nandu) under non-limited growing conditions (vs. 102 % OTC observed) as compared with the simulated reference level (ypot,100 %). The modeling results from the CERES-Wheat crop model, ideotype and cultivation value models imply that with new high yielding mid-European ideotypes, the nonpotential baseline yield (yb) would be on average 5150 kg ha-1 (+ 108 %) vs. new highlatitude ideotypes (yb 4770 kg ha-1, 100%) grown under the elevated CO2(700ppm)×temperature(+3ºC) growing conditions projected for the year 2100 FINSKEN climate change scenario for southern Finland, with elevated CO2 (733 ppm) and temperature (+4.4 °C) levels. The Ideotype, Cultivation value, Mixed structural covariance, Path and yield component analysis results emphasized that especially grains/ear, harvest index (HI) and maximum 1000 kernel weight were significant factors defining the highest yield potential for high-latitude and mid-European spring wheat genotypes. In addition, the roles of flag leaf area and dry weight, especially during the generative phase after heading, were important factors defining the final grain yield potential for new highyielding wheat ideotypes. The 1989-2004 averaged cereal yield modeling results using optical and microwave satellite data from southern Finland with Vegetation Indices (VGI) and Composite Multispectral (CMM) models, suggest a non-potential baseline yield level (yb, kg ha-1) of 3950 kg ha-1 (R2 0.630, RMSE 9.1 %) for spring cereals (including spring wheat, barley (Hordeum vulgare L.), and oats (Avena sativa L.) cultivars), 4330 kg ha-1 (R2 0.630, RMSE 6.7 %) for winter cereals (winter wheat and rye (Secale cereale L.) cultivars) and 4240 kg ha-1 (R2 0.764, RMSE 6.6 %) for spring wheat cultivars grown in actual field conditions on farms in southern Finland. The modeled VGI and CMM yield estimates (yb) were compared with corresponding measured averaged yields in the 6 experimental areas in the Etelä-Pohjanmaa, Nylands Svenska and Häme Agricultural Advisory and Rural Development Centres (Growing zones I-III) in southern Finland. The combined modeling results from this study suggest that the 5 t ha-1 yield barrier will be surpassed with new high yielding mid-European and high-latitude optimal ideotypes introduced into cultivation after the 1990s, when also taking into account the elevated atmospheric CO2 and temperature effects, thereby increasing the average spring wheat non-potential yield levels by 1-6 % of high-latitude ideotypes (4-13 % for mid-European ideotypes) by 2100 in southern Finland. The extrapolation modeling results, combined with earlier sowing and elevated atmospheric CO2 (700 ppm) and temperature (+3 ºC) effects, suggest an average net increase of 30 million kg annually in spring wheat total production in Finland by 2100 using new high-latitude wheat ideotypes (60 million kg with new mid-European ideotypes) and assuming no changes in wheat cultivated area and land use. Currently the averaged annual spring wheat total production is on the 600 million kg level in Finland, varying significantly between years with changes in wheat total cultivation area in Finland.Tutkimuksessa arvioitiin kasvumalleilla nykyisten ja uusien kevätvehnägenotyyppien (alkuperä Keski-Eurooppa/Skandinavia) sekä potentiaalista, ympäristö ja kasvutekijöiden rajoittamatonta (ypot, kg ha-1) että non-potentiaalista (yb,kg ha-1) sadontuottokykyä tulevaisuuden kohonneiden ilmakehän CO2 ja keskilämpötilojen kasvuolosuhteissa Etelä-Suomessa vuosien 2000-2100 aikavälillä. Lisäksi tutkimuksessa arvioitiin mahdollisuuksia arvioida kevätviljojen ja erityisesti kevätvehnän non-potentiaalista satotasoa laajoilla viljelyalueilla Etelä-Suomen ja Pohjanmaan maaseutukeskuksissa (Etelä-Suomen ja Pohjanmaan kasvuvyöhykkeet I-IV) hyödyntäen sekä optisten että mikroaaltotaajuus kaukokartoitussatelliittien mittausdataaa (1998-2004). CERES wheat -kasvumallilla simuloitiin kohotettujen CO2:n (700 ppm, parts per million) ja lämpötilan(+3 °C) vaikutuksia kevätvehnälajike Polkan (Triticum aestivum L.) fenologiseen kehitykseen sekä biomassa- ja sadontuottomahdollisuuksiin optimaalisissa kasvuoloissa (potentiaalinen kasvumalli). Toinen simulointi suoritettiin kasvukauden aikaisten stressitekijöiden (sää, kuivuus, sadanta ja typpilannoitus) vaikuttaessa fenologiseen kehitykseen ja sadontuotantoon (non-potentiaalinen kasvumalli). Suomen ilmastonmuutos -tutkimusohjelman (SILMU) skenaarioiden mukaan Suomen kasvuolosuhteet tulevat muistuttamaan v. 2100 olosuhteita,jotka vallitsevat tällä hetkellä Tanskassa ja Pohjois-Saksassa. Tällöin keskilämpötila on kohonnut 3 °C ja ilmakehän CO2-taso kaksinkertaistunut vuoden 1990 keskimääräisestä 350 ppm tasota 700 ppm tasoon. CERES wheat -kasvumallituksen tulokset indikoivat kaksinkertaisen CO2-tason kohottavan Polkka lajikkeen satoa 142 % potentiaalisella mallilla (167 % non-potentiaalisella) laskettuna nykyisestä referenssitasosta (100 %, ambientti lämpötila, CO2 350 ppm). Kohotettu lämpötila (+3 °C) pienensi Polkan satoa 80,4 %:iin referenssitasosta (100 %, 6,16 t ha -1) potentiaalisella mallilla (76,8 % non-potentiaalisella mallilla referenssitasosta 4,49 t ha -1). Kohotettu lämpötila lyhensi kasvin kasvuaikaa kiihdyttämällä kasvua vegetatiivisessa ja generatiivisessa vaiheessa. Kasvuajan lyhentyminen puolestaan alensi Polkka kevätvehnän satoa. Simuloitaessa kohotettujen CO2-tason ja lämpötilan yhteisvaikutusta Polkan satoon, kiihdytti kohotettu CO2-taso vegetatiivisessa vaiheessa biomassan muodostumista ja generatiivisessa vaiheessa sadonmuodostusta. Toisaalta kohotettu lämpötila lyhensi kasvin generatiivista vaihetta ja pienensi CO2:n satoa kohottavaa vaikutusta. Tällöin kohotettu lämpötila aiheutti tähkän täystuleentumisen aikaisemmin ja sato jäi alhaisemmaksi (106 % potentiaalinen malli, 122 % non-potentiaalinen malli laskettuna ambientista lämpötila ja CO2 tasosta). Tulokset olivat samansuuntaiset Maatalouden tutkimuskeskuksessa v. 1992-1994 Polkka kevätvehnällä tehtyjen open top-kasvukammio (OTC) kokeiden kanssa (Hakala 1998). Ideotyyppi, Cultivation value, Mixed structural covariance, Path ja yield component mallitustulokset indikoivat jyvien lukumäärä/tähkä, satoindeksi (HI), ja 1000 siemenen paino olevan merkittäviä satokomponentteja, jotka vaikuttivat merkittävästi uusien (alkuperä Keski-Eurooppa/Skandinavia) korkea satoisten kevätvehnägenotyyppien loppusatoon. Vuosina 1989-2004 Etelä-Suomessa ja Pohjanmaalla suoritetuissa kenttäkokeissa verrattiin sekä optisten että mikroaaltosatelliittien antaman reflektanssi ja takaisinsironta informaation ennustavuutta kevätviljojen loppusatojen arvioinnissa käyttäen VGI (Vegetation Indices) ja CMM (Composite Multispectral Model) malleja.VGI ja CMM mallien estimoima non-potentiaalinen keskisatoestimaatti (yb) oli kevätviljoille (kevätvehnä, ohra, kaura) 3950 kg ha-1 (R2 0.630, RMSE 9.1 %) ja 4240 kg ha-1 (R2 0.764, RMSE 6.6 %) kevätvehnälle. VGI ja CMM mallien satoestimaatteja verrattiin Maa ja Metsätalousministeriön vuosittaisiin kevätviljojen keskimääräisiin inventaario-satotasoihin maaseutukeskuksissa sekä vastaaviin MTT maa- ja elintarviketalouden tutkimuskeskuksen virallisten lajikekokeiden satotuloksiin Etelä-Suomen ja Pohjanmaan kasvuvyöhykkeillä I-IV. Tutkimuksen yhteenvetona eri osajulkaisuista voidaan päätellä, että uusien korkeasatoisten kevätvehnien (alkuperä Keski-Eurooppa/Skandinavia, viljelykseen otto 1990 jälkeen) non-potentiaalinen keskimääräinen satotaso (yb) ylittää 5 t ha-1 tason 2050-2100 aikavälillä Etelä-Suomessa kun huomioidaan keskilämpötilan ja ilmakehän CO2 pitoisuuden kohoaminen yhdessä viljelytekniikan ja kasvinjalostuken keskimääräistä satotasoa kohottavat tekijät. Tutkimuksen perusteella arvioitiin uusien korkeasatoisten Skandinaavisten kevätvehnälajikkeiden lisäävän keskimäärin 30 miljoona kg/vuosi (+ 60 miljoonaa kg/vuosi käyttäen uusia korkeasatoisia Keski-Eurooppalaisia lajikkeita) Suomen kansallista kevätvehnän kokonaissatoa 2050-2100 aikavälillä yhdessä kohotetun CO2 ja lämpötilatason kanssa laskettuna Suomen keskimääräisestä 600 miljoonan kg/vuosi tuotantotasosta. Vuotuinen kevätvehnän kokonaistuotantotaso vaihtelee kuitenkin voimakkaasti vuosien välillä riippuen voimakkaast kevätvehnän kokoviljelyalasta Suomessa

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Land use optimization tool for sustainable intensification of high-latitude agricultural systems

Recent studies assessing agricultural policies, including the EU’s Agri-Environment Scheme, have shown that these have been successful in attaining some environmental goals. In Finland, however, the economic situation of farms has dramatically fallen and hence, the actions do not result in social acceptability. Sustainable intensification is a means to combine the three dimensions of sustainability: environmental, economic and social. Here we introduce a novel land use optimization and planning tool for the sustainable intensification of high-latitude agricultural systems. The main rationale for the development of the tool was to achieve a systematic and comprehensive conception for land allocation across Finland, where field parcels vary substantially in their conditions. The developed tool has a three-step scoring system based on seven physical characteristics (parcel size, shape, slope, distance to the farm center and waterways, soil type and logistic advantages) and the productivity of field parcels. The productivity estimates are based on vegetation indices derived from optical satellite data. The tool allocates virtually all >1 million field parcels in Finland either to sustainable intensification, extensification or afforestation. The tool is dynamic in the sense that its boundary values for land allocation can be fixed according to changes in social targets and supporting policies. Additionally, it can be applied year after year by acknowledging new available data, e.g., on vegetation indices and field parcel rearrangements between farms. Furthermore, it can be applied to all farm types and across Finland. It is a tool for land use planning, implementation and monitoring, but its thorough implementation calls for further development of policy instruments, which are currently more supportive towards land sharing than land sparing activities

Besoin en eau et rendements des céréales en Méditerranée du Sud : observation, prévision saisonnière et impact du changement climatique

Le secteur agricole est l'un des piliers de l'économie marocaine. En plus de contribuer à 15% au Produit Intérieur Brut (PIB) et de fournir 35% des opportunités d'emploi, il a un impact sur les taux de croissance. Ces dernières sont affectées négativement ou positivement par les conditions climatiques et la pluviométrie en particulier. Lors des années de sécheresse, caractérisées par une baisse de la production agricole, en particulier celle des céréales, la croissance de l'économie marocaine a été sévèrement affectée et les importations alimentaires du royaume ont augmenté de manière significative. Dans ce contexte, il est important d'évaluer l'impact de la sécheresse agricole sur les rendements céréaliers et de développer des modèles de prévision précoce des rendements, ainsi que de déterminer l'impact futur du changement climatique sur le rendement du blé et leurs besoins en eau. Le but de ce travail est, premièrement, d'approfondir la compréhension de la relation entre le rendement des céréales et la sécheresse agricole au Maroc. Afin de détecter la sécheresse, nous avons utilisé des indices de sécheresse agricole provenant de différentes données satellitaires. En outre, nous avons utilisé les sorties du système d'assimilation des données terrestres (LDAS). Deuxièmement, nous avons développé des modèles empiriques de la prévision précoce des rendements des céréales à l'échelle provinciale. Pour atteindre cet objectif, nous avons construit des modèles de prévision en utilisant des données multi-sources comme prédicteurs, y compris des indices basés sur la télédétection, des données météorologiques et des indices climatiques régionaux. Pour construire ces modèles, nous nous sommes appuyés sur des algorithmes de machine learning tels que : Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) et eXtreme Gradient Boost (XGBoost). Enfin, nous avons évalué l'impact du changement climatique sur le rendement du blé et ses besoins en eau. Pour ce faire, nous nous sommes appuyés sur cinq modèles climatiques régionaux disponibles dans la base de données Med-CORDEX sous deux scénarios RCP4.5 et RCP8.5, ainsi que sur le modèle AquaCrop et nous nous sommes basés sur trois périodes, la période de référence 1991-2010, la deuxième période 2041-2060 et la troisième période 2081-2100. Les résultats ont montré qu'il y a une corrélation étroite entre le rendement des céréales et les indices de sécheresse liés à l'état de végétation pendant le stade d'épiaison (mars et avril) et qui sont liés à la température de surface pendant le stade de développement en janvier-février, et qui sont liés à l'humidité du sol pendant le stade d'émergence en novembre-décembre. Les résultats ont également montré que les sorties du LDAS sont capables de suivre avec précision la sécheresse agricole. En ce qui concerne la prévision du rendement, les résultats ont montré que la combinaison des données provenant de sources multiples a donné des meilleurs résultats que les modèles basés sur une seule source. Dans ce contexte, le modèle XGBoost a été capable de prévoir le rendement des céréales dès le mois de janvier (environ quatre mois avant la récolte) avec des métriques statistiques satisfaisants (R² = 0.88 et RMSE = 0.22 t. ha^-1). En ce qui concerne l'impact du changement climatique sur le rendement et les besoins en eau du blé, les résultats ont montré que l'augmentation de la température de l'air entraînera un raccourcissement du cycle de croissance du blé d'environ 50 jours. Les résultats ont également montré une diminution du rendement du blé jusqu'à 30% si l'augmentation du CO2 n'est pas prise en compte. Cependant, l'effet de la fertilisation au CO2 peut compenser les pertes du rendement, et ce dernier peut augmenter jusqu'à 27%. Finalement, les besoins en eau devraient diminuer de 13 à 42%, et cette diminution est associée à une modification de calendrier d'irrigation, le pic des besoins arrivant deux mois plus tôt que dans les conditions actuelles.The agricultural sector is one of the pillars of the Moroccan economy. In addition to contributing 15% in GDP and providing 35% of employment opportunities, it has an impact on growth rates that are negatively or positively affected by climatic conditions and rainfall in particular. During drought years characterized by a decline in agricultural production and in particular cereal production, the growth of the Moroccan economy was severely affected and the kingdom's food imports increased significantly. In this context, it's important to assess the impact of agricultural drought on cereal yields and to develop early yield prediction models, as well as to determine the future impact of climate change on wheat yield and water requirements. The aim of this work is, firstly to further understand the linkage between cereal yield and agricultural drought in Morocco. In order to identify this drought, we used agricultural drought indices from remotely sensed satellite data. In addition, we used the outputs of Land Data Assimilation System (LDAS). Secondly, to develop empirical models for early prediction of cereal yields at provincial scale. To achieve this goal, we built forecasting models using multi-source data as predictors, including remote sensing-based indices, weather data and regional climate indices. And to build these models, we relied on machine learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boost (XGBoost). Finally, to evaluate the impact of climate change on the wheat yield its water requirements. To do this, we relied on five regional climate models available in the Med-CORDEX database under two scenarios RCP4.5 and RCP8.5, as well as the AquaCrop model and we based on three periods, the reference period 1991-2010, the second period 2041-2060 and the third period 2081-2100. The results showed that there is a close correlation between cereals yield and drought indices related to canopy condition during the heading stage (March and April) and which are related to surface temperature during the development stage in January -February, and which are related to soil moisture during the emergence stage in November -December. The results also showed that the outputs of LDAS are able to accurately monitor agricultural drought. Concerning, cereal yield forecasting, the results showed that combining data from multiple sources outperformed models based on one data set only. In this context, the XGBoost was able to predict cereal yield as early as January (about four months before harvest) with satisfactory statistical metrics (R² = 0.88 and RMSE = 0.22 t. ha^-1). Regarding the impact of climate change on wheat yield and water requirements, the results showed that the increase in air temperature will result in a shortening of the wheat growth cycle by about 50 days. The results also showed a decrease in wheat yield up to 30% if the rising in CO2 was not taken into account. The effect of fertilizing of CO2 can offset the yield losses, and yield can increase up to 27 %. Finally, water requirements are expected to decrease by 13 to 42%, and this decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions

Remote sensing environmental change in southern African savannahs : a case study of Namibia

Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s most iconic species and most of its livestock and rangeland, while sustaining the livelihoods of an important proportion of its human population. They provide essential ecosystem services and functions, ranging from forest, grazing and water resources, to global climate regulation and carbon sequestration. However, savannahs are highly sensitive to human activities and climate change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing anthropogenic disturbances in the form of agricultural intensification and urbanization, have resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened ecosystems are some of the least studied or protected, and hence should be given high conservation priority. Importantly, the scale of land degradation has not been fully explored, thereby comprising an important knowledge gap in our understanding of ecosystem services and processes, and effectively impeding conservation and management of these biodiversity hotspots. The primary drivers of land degradation include deforestation, triggered by the increasing need for urban and arable land, and concurrently, shrub encroachment, a process in which the herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These processes have significant repercussions on ecosystem service provision, both locally and globally, although the extents, drivers and impacts of either remain poorly quantified and understood. Additionally, regional aridification anticipated under climate change, will lead to important shifts in vegetation composition, amplified warming and reduced carbon sequestration. Together with a growing human population, these processes are expected to compound the risk of land degradation, thus further impacting key ecosystem services. Namibia is undergoing significant environmental and socio-economic changes. The most pervasive change processes affecting its savannahs are deforestation, degradation and shrub encroachment. Yet, the extent and drivers of such change processes are not comprehensively quantified, nor are the implications for rural livelihoods, sustainable land management, the carbon cycle, climate and conservation fully explored. This is partly due to the complexities of mapping vegetation changes with satellite data in savannahs. They are naturally spatially and temporally variable owing to erratic rainfall, divergent plant functional type phenologies and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to (i) quantify distinct vegetation change processes across Namibia, and (ii) develop methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite data spanning a range of spatial, temporal and spectral resolutions are integrated with field datasets to achieve these aims, which are addressed in four journal articles. Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in land cover classes over five decades throughout the Namibian Kalahari woodlands. The approach addresses issues implicit in change detection of savannahs by capturing the distinct phenological phases of woody vegetation and integrating multi-sensor, multi-source data. Vegetation extent was found to have decreased due to urbanization and small-scale arable farming. An assessment of the limitations leads to Chapter 4, which elaborates on the previous chapter by quantifying aboveground biomass changes associated with deforestation and shrub encroachment. The approach centres on fusing multiple satellite datasets, each acting as a proxy for distinct vegetation properties, with calibration/validation data consisting of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating the contribution of land management to ecosystem carbon changes. To identify whether biomass is declining across the country, Chapter 5 focuses on regional, moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS data are used to model observed fractional woody vegetation cover, a proxy for biomass. Trends in modelled fractional woody cover are then evaluated in relation to the predominant land-uses and precipitation. Negative trends slightly outweighed positive trends, with decreases arising largely in protected, urban and communal areas. Since precipitation is a fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as a function of precipitation, with residuals assumed to describe the fraction of NDVI not explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend, although extensive “greening” is unrelated to rainfall. NDVI amplitude, used as a proxy for vegetation density, indicates a widespread shift to a denser condition. In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and temporal limitations characteristic of the previous approaches. Results, together with those of the previous chapters, are synthesized and a synopsis of the main findings is presented. Vegetation loss is predominantly caused by demand for urban and arable land. Greening trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry and rangeland management, with precipitation presenting little influence. Despite prevalent greening, degradation processes associated with shrub encroachment, including soil erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment occurs regionally. This thesis successfully integrates multi-source data to map, measure and monitor distinct change processes across scales

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome