First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems

The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness model, the greenness and radiation model and a light use efficiency model. The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation, root-mean-square error, and Bayesian information criterion. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models. The results of this study show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.Comment: Accepted manuscript; 12 pages, 4 tables, 9 figure

Spatio-temporal divergence in the responses of Finland's boreal forests to climate variables

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland's boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May-September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.Peer reviewe

Modelling carbon uptake in Nordic forest landscapes using remote sensing

Boreal forests and peatlands store over 30% of the global terrestrial carbon in their vegetation and soil. but changing climate can compromise the current carbon stock. Rising air temperatures, changing precipitation patterns and increased risk of natural disturbances can impact the ability of the boreal ecosystems to absorb and store carbon, reducing their effectiveness as carbon sinks. Reliable estimates of carbon fluxes between these ecosystems and the atmosphere are crucial for understanding the ecosystem response to climate change. This thesis focuses on developing remote sensing-based modelsof the vegetation carbon uptake i.e. gross primary production (GPP) in Nordic forests and peatlands, and upscaling the estimates from sites to landscape and regional levels.The results demonstrate that spectral vegetation indices EVI2 and PPI can capture the seasonal dynamics of GPP well. In general, other environmental variables that further helped to improve the results were air temperature, photosynthetically active radiation (PAR), and vapour pressure deficit(VPD) that expresses atmospheric demand for water. Another finding was that the spatial resolution of the satellite instrument had less influence on the accuracy of GPP estimates than the model formulation and selection of the input data. The result suggested that vegetation productivity can be monitored at various scales with high accuracy using satellite remote sensing data. Fine-scaleestimates are beneficial when monitoring individual forest stands or spatially heterogeneous ecosystems like peatlands.Various model formulations were tested to estimate GPP with remotely sensed data. The site-specific calibration gave more accurate results, but the single parameter set per ecosystem type was more applicable for upscaling GPP for a larger area. In addition, we found that PPI performed well andprovided a useful tool for estimating GPP at local and regional scales. Despite the good agreement with the eddy covariance-derived GPP, the models could be further improved to capture the spatial heterogeneity between the sites by adding e.g. soil moisture data. Finally, we applied a PPI-based model to estimate annual GPP in Sweden’s forests and peatlands with a 10-meters spatial resolution. This thesis highlights that satellite remote sensing has a great potential for monitoring variations changes in vegetation carbon uptake in Nordic forest and peatland ecosystems

Analyzing Vegetation Trends with Sensor Data from Earth Observation Satellites

Abstract This thesis aims to advance the analysis of nonlinear trends in time series of vegetation data from Earth observation satellite sensors. This is accomplished by developing fast, efficient methods suitable for large volumes of data. A set of methods, tools, and a framework are developed and verified using data from regions containing vegetation change hotspots. First, a polynomial-fitting scheme is tested and applied to annual Global Inventory Modeling and Mapping Studies (GIMMS)–Normalized Difference Vegetation Index (NDVI) observations for North Africa for the period 1982–2006. Changes in annual observations are divided between linear and nonlinear (cubic, quadratic, and concealed) trend behaviors. A concealed trend is a nonlinear change which does not result in a net change in the amount of vegetation over the period. Second, a systematic comparison between parametric and non-parametric techniques for analyzing trends in annual GIMMS-NDVI data is performed at fifteen sites (located in Africa, Spain, Italy, and Iraq) to compare how trend type and departure from normality assumptions affect each method’s accuracy in detecting long-term change. Third, a user-friendly program (Detecting Breakpoints and Estimating Segments in Trend, DBEST) has been developed which generalizes vegetation trends to main features, and characterizes vegetation trend changes. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST is tested and evaluated using both simulated NDVI data, and actual NDVI time series for Iraq for the period 1982-2006. Finally, a decision-making framework is presented to help analysts perform comprehensive analyses of trend/change in time series of satellite sensor data. The framework is based on a conceptual model of the main aspects of trend analyses, including identification of the research question, the required data, the appropriate variables, and selection of efficient analysis methods. To verify the framework, it is applied to four case studies (located in Burkina Faso, Spain, Sweden, and Senegal) using Moderate-resolution Imaging Spectroradiometer (MODIS)–NDVI data for the period 2000–2013. Each of the case studies successfully achieved its research aim(s), showing that the framework can achieve the main goal of the study which is to advance the analysis of nonlinear changes in vegetation. The methods developed in this thesis can help to contribute more accurate information about vegetation dynamics to the field of land cover change research

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

Spatio-temporal analysis of North African forest cover dynamics using time series of vegetation indices – case of the Maamora forest (Morocco)

North African forest areas play several roles and functions and represent a heritage of great economic and ecological importance. As a result of global changes, that act independently or synergistically, these areas are currently undergoing a pronounced degradation and their productivity is decreasing due to several factors. This work aims to characterize spatio-temporal dynamics of vegetation within the Maamora forest. This forest is considered as the most extensive cork oak woodland in the world and is divided, from west to east, into five cantons A, B, C, D and E. The data, extracted between 2000–2021 from MODIS NDVI/EVI images of 250 m, were analyzed using statistical parameters with the Pettitt homogeneity and the Mann-Kendall trend tests, with their seasonal and spatial components, in order to better consider the vegetation distribution of this forest. Results show a clear temporal and spatial (inter-canton) variability of vegetation intensity, unrelated to the continental gradient. In fact, recorded mean values in cantons C and E are significantly higher than those of cantons B and D respectively. This is confirmed by both regressive and progressive trends, which were identified respectively from the months of March 2012 and October 2008, in the data series of cantons B and E successively. Spatially, the regressive dynamic remains generalized and affects more than 26.7% of the Maamora’s total area with extreme rates (46.1% and 14.0%) recorded respectively by the two aforementioned cantons. Similarly, all the stand types in canton B show the highest regressive rates, especially the cork oak regeneration strata (75.4%) and the bare lands (86.1%), which may explain the positive tendencies identified by the related series during the fall season. However, the cantons C and E record the lowest rates, respectively, for natural stands of cork oak and artificial plantations. These results highlight also the absence of a causal relationship between the contrasting vegetation dynamics of the Maamora and the climatic conditions, expressed here by the continental gradient. However, they do highlight the effects of other factors, particularly those of a technical nature

Responses of Land Surface Phenology to Wildfire Disturbances in the Western United States Forests

Land surface phenology (LSP) characterizes the seasonal dynamics in the vegetation communities observed for a satellite pixel and it has been widely associated with global climate change. However, LSP and its long-term trend can be influenced by land disturbance events, which could greatly interrupt the LSP responses to climate change. Wildfire is one of the main disturbance agents in the western United States (US) forests, but its impacts on LSP have not been investigated yet. To gain a comprehensive understanding of the LSP responses to wildfires in the western US forests, this dissertation focused on three research objectives: (1) to perform a case study of wildfire impacts on LSP and its trend by comparing the burned and a reference area, (2) to investigate the distribution of wildfire impacts on LSP and identify control factors by analyzing all the wildfires across the western US forests, and (3) to quantify the contributions of land cover composition and other environmental factors to the spatial and interannual variations of LSP in a recently burned landscape. The results reveal that wildfires play a significant role in influencing spatial and interannual variations in LSP across the western US forests. First, the case study showed that the Hayman Fire significantly advanced the start of growing season (SOS) and caused an advancing SOS trend comparing with a delaying trend in the reference area. Second, summarizing \u3e800 wildfires found that the shifts in LSP timing were divergent depending on individual wildfire events and burn severity. Moreover, wildfires showed a stronger impact on the end of growing season (EOS) than SOS. Last, LSP trends were interrupted by wildfires with the degree of impact largely dependent on the wildfire occurrence year. Third, LSP modeling showed that land cover composition, climate, and topography co-determine the LSP variations. Specifically, land cover composition and climate dominate the LSP spatial and interannual variations, respectively. Overall, this research improves the understanding of wildfire impacts on LSP and the underlying mechanism of various factors driving LSP. This research also provides a prototype that can be extended to investigate the impacts on LSP from other disturbances

Assessment of Land Degradation Patterns in Western Kenya : Implications for Restoration and Rehabilitation

Land degradation remains a major threat to the provision of environmental services and the ability of smallholder farmers to meet the growing demand for food. Understanding patterns of land degradation is therefore a central starting point for designing any sustainable land management strategies. However, land degradation is a complex process both in time and space making its quantification difficult. There is no adequate monitoring of many of the land degradation issues both at national and local scale in Kenya. The objective of this study conducted between 2009 and 2012 was to assess the land degradation patterns in Kenya as a basis for making recommendations for sustainable land management. The correlation between vegetation and precipitation and the change in vegetation over the period 2001-2009 was assessed using 250 m resolution Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index (MODIS/NDVI) and time-series rainfall data. The assessment at national levels revealed that, irrespective of the direction of change, there was a significant correlation between vegetation (NDVI) and annual precipitation for 32% of the land area. The inter-annual change in vegetation cover, depicted by the NDVI slope, was between -0.067 and +0.068. A negative NDVI slope (indication of degradation) was observed for areas around Lake Turkana and several districts in eastern Kenya. Positive NDVI trends were observed in Wajir and Baringo, which are located in the dry land areas, showing that the vegetation cover was increasing over the years. NDVI difference between the baseline (2001-2003) and end line (2007-2009) showed an absolute change in NDVI of -0.42 to +0.48. But the relative change was between -74% for the degrading areas and +238% for the improving areas with most of the dramatic positive changes taking place in the drylands. Relative to the baseline, 21% of the land was experiencing a decline in the vegetation cover, 12% was improving, while 67% was stable. Classification of Landsat imagery for the period 1973, 1988 and 2003 showed that there were significant changes in land use land cover (LULC) in the western Kenya districts with the area under agricultural activities increasing from 28% in 1973 to 70% in 2003 while those under wooded grassland decreasing from 51% to 11% over the same period. Detailed field observations and measurements showed that over 55% of the farms sampled lacked any form of soil and water conservation technologies. Sheet erosion was the most dominant form of soil loss observed in over 70% of the farms. There was a wide variability in soil chemical properties across the study area with values of most major properties being below the critical thresholds needed to support meaningful crop production. Notable was the high proportion (90%) of farms with slightly acidic to strongly acidic (pH Erfassung und Bewertung verschiedener Erscheinungsformen von Landdegradation in West Kenia: Konsequenzen für Restaurierungs- und Rehabilitierungsmaßnahmen Landdegradation stellt eine der größten Gefahren für die Bereitstellung von Umweltdienstleistungen dar und für die Kleinbauern hinsichtlich des wachsenden Bedarfs an Nahrungsmitteln. Die Entwicklung nachhaltiger Landnutzungsstrategien beginnt daher mit dem Erkennen und Verstehen von Landdegradationsmustern. Die komplexen Prozesse der Landdegradation über Raum und Zeit erschweren jedoch eine Quantifizierung. Bisher existiert in Kenia kein adäquates Monitoring der Landdegradation, weder auf nationaler noch auf lokaler Ebene. Das Ziel des von 2009 bis 2012 durchgeführten Studie war die Erfassung von Landdegradationsmustern in Kenia, um Empfehlungen für nachhaltige Landmanagementstrategien geben zu können. Die Korrelation zwischen Vegetation und Niederschlag und der Vegetationsveränderungen im Zeitraum 2001 bis 2009 wurde mittels einer MODIS/NDVI (Moderate Resolution Imaging Spectroradiometer (250 m-Auflösung) - Normalized Difference Vegetation Index) ermittelt. Die Untersuchungen auf nationaler Ebene ergaben, dass, unabhängig von der Richtung des Änderungsprozesses, eine signifikante Korrelation zwischen Vegetation (NDVI) und jährlicher Niederschlagsmenge für 32% der Landfläche besteht. Die Änderung der Vegetationsdecke über mehrere Jahre, dargestellt durch die NDVI-Linie, lag zwischen -0.067 und +0.068. Eine abfallende NDVI-Linie (als Indikator für Degradation) konnte für Flächen rund um Turkana See und in mehreren Distrikten Ost-Kenias beobachtet werden. Positive NDVI-Trends traten in den Trockengebieten Wajir und Baringo auf; dies deutet darauf hin, dass die Vegetationsdichte hier über die Jahre zunahm. Die Differenz des NDVI zwischen Ausgangswerten (2001-2003) und Endwerten (2007-2009) zeigte eine absolute NDVI-Veränderung von -0.42 bis +0.48. Die relative Veränderung war jedoch -74% für degradierende Flächen und +238% für Flächen mit zunehmender Vegetationsbedeckung, wobei die höchsten positiven Veränderungen in den Trockengebieten festgestellt wurden. Im Vergleich zu den Basisdaten fand auf 21% der Flächen eine Abnahme der Vegetationsbedeckung statt, 12% der Landflächen erfuhr eine Verbesserung und 67% verzeichnete keine Veränderungen. Die Klassifizierung der Landsat-Aufnahmen von 1973, 1988 und 2003 zeigte signifikante Veränderungen in der Landbedeckung bzw. Landnutzung in den Distrikten West Kenias . Der Anteil der landwirtschaftlich genutzten Fläche stieg von 28% im Jahre 1973 auf 70% in 2003 an, während der Flächenanteil der Baum- und Strauchsavanne im gleichen Zeitraum von 51% auf 11% abnahm. Detaillierte Felduntersuchungen ergaben, dass mehr als 55% der untersuchten Farmen keine Boden- oder Wasserschutzmaßnahmen durchführen. Bodenerosion stellte die Hauptursache von Bodenverlust dar und konnte bei über 70% der Farmen festgestellt werden. Die chemischen Bodeneigenschaften im Untersuchungsgebiet waren sehr variabel; viele der wichtigsten Bodeneigenschaften lagen unter den kritischen Grenzwerten, die für erfolgreichen Pflanzenbau notwendig sind. Auffällig war der hohe Anteil an Farmen (90%) mit leicht bis sehr sauren Böden (pH<5.5). In den Böden von über 55% der Farmen lag der organischer Kohlenstoffgehalt unter 2%. Potentieller Nährstoffvorrat und -aufnahme der Böden waren sehr variabel. Flächen, die als sehr fruchtbar klassifiziert wurden, hatten ein dreifach höheres Vorratspotential an Stickstoff und Phosphor im Vergleich zu Flächen mit geringer Fruchtbarkeit. Der geschätzte potenzielle Maisertrag der Böden lag zwischen 1.6 t/ha und 2.8 t/ha. Der aktuelle Ertrag lag mit weniger als 1 t/ha jedoch darunter. Insgesamt waren die Farmer der Meinung, dass die Produktivität der Landnutzung, Tierhaltung, und Forst- und Wasserressourcen gesunken sei. Durch die Kombination verschiedener Erfassungs- und Monitoringmethoden konnten verschiedene Aspekte der Landdegradation und damit wichtige Informationen für die Entwicklung nachhaltiger Landnutzungsstrategien erfasst werden. Um Bodennährstoffmangel und niedrige Bodenproduktivität positiv zu verändern, müsste ein integriertes Bodenmanagement zur Erhöhung der Bodenfruchtbarkeit umgesetzt werden

EUROSPEC : at the interface between remote-sensing and ecosystem CO2 flux measurements in Europe

Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicate the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as a bridge between EC measurements and remote-sensing data. In situ spectral measurements have already been conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of these measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global estimates of GPP over terrestrial ecosystems.Peer reviewe