673 research outputs found

    The Economics of Desertification, Land Degradation, and Drought; Toward an Integrated Global Assessment

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    Land degradation has not been comprehensively addressed at the global level or in developing countries. A suitable economic framework that could guide investments and institutional action is lacking. This study aims to overcome this deficiency and to provide a framework for a global assessment based on a consideration of the costs of action versus inaction regarding desertification, land degradation, and drought (DLDD). Most of the studies on the costs of land degradation (mainly limited to soil erosion) give cost estimates of less than 1 percent up to about 10 percent of the agricultural gross domestic product (GDP) for various countries worldwide. But the indirect costs of DLDD on the economy (national income), as well as their socioeconomic consequences (particularly poverty impacts), must be accounted for, too. Despite the numerous challenges, a global assessment of the costs of action and inaction against DLDD is possible, urgent, and necessary. This study provides a framework for such a global assessment and provides insights from some related country studies.Agricultural Finance, Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use, Resource /Energy Economics and Policy,

    Leaf nitrogen determination using non-destructive techniques–A review

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    © 2017 Taylor & Francis Group, LLC. The optimisation of plant nitrogen-use-efficiency (NUE) has a direct impact on increasing crop production by optimising use of nitrogen fertiliser. Moreover, it protects environment from negative effects of nitrate leaching and nitrous oxide production. Accordingly, nitrogen (N) management in agriculture systems has been major focus of many researchers. Improvement of NUE can be achieved through several methods including more accurate measurement of foliar N contents of crops during different growth phases. There are two types of methods to diagnose foliar N status: destructive and non-destructive. Destructive methods are expensive and time-consuming, as they require tissue sampling and subsequent laboratory analysis. Thus, many farmers find destructive methods to be less attractive. Non-destructive methods are rapid and less expensive but are usually less accurate. Accordingly, improving the accuracy of non-destructive N estimations has become a common goal of many researchers, and various methods varying in complexity and optimality have been proposed for this purpose. This paper reviews various commonly used non-destructive methods for estimating foliar N status of plants

    Analysis of the spatial heterogeneity of land surface parameters and energy flux densities

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    This work was written as a cumulative doctoral thesis based on reviewed publications. Climate projections are mainly based on the results of numeric simulations from global or regional climate models. Up to now processes between atmosphere and land surface are only rudimentarily known. This causes one of the major uncertainties in existing models. In order to reduce parameterisation uncertainties and to find a reasonable description of sub grid heterogeneities, the determination and evaluation of parameterisation schemes for modelling require as many datasets from different spatial scales as possible. This work contributes to this topic by implying different datasets from different platforms. Its objective was to analyse the spatial heterogeneity of land surface parameters and energy flux densities obtained from both satellite observations with different spatial and temporal resolutions and in-situ measurements. The investigations were carried out for two target areas in Germany. First, satellite data for the years 2002 and 2003 were analysed and validated from the LITFASS-area (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study). Second, the data from the experimental field sites of the FLUXNET cluster around Tharandt from the years 2006 and 2007 were used to determine the NDVI (Normalised Difference Vegetation Index for identifying vegetated areas and their "condition"). The core of the study was the determination of land surface characteristics and hence radiant and energy flux densities (net radiation, soil heat flux, sensible and latent heat flux) using the three optical satellite sensors ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spektroradiometer) and AVHRR 3 (Advanced Very High Resolution Radiometer) with different spatial (30 m – 1 km) and temporal (1 day – 16 days) resolution. Different sensor characteristics and different data sets for land use classifications can both lead to deviations of the resultant energy fluxes between the sensors. Thus, sensor differences were quantified, sensor adaptation methods were implemented and a quality analysis for land use classifications was performed. The result is then a single parameterisation scheme that allows for the determination of the energy fluxes from all three different sensors. The main focus was the derivation of the latent heat flux (L.E) using the Penman-Monteith (P-M) approach. Satellite data provide measurements of spectral reflectance and surface temperatures. The P-M approach requires further surface parameters not offered by satellite data. These parameters include the NDVI, Leaf Area Index (LAI), wind speed, relative humidity, vegetation height and roughness length, for example. They were derived indirectly from the given satellite- or in-situ measurements. If no data were available so called default values from literature were taken. The quality of these parameters strongly influenced the exactness of the radiant- and energy fluxes. Sensitivity studies showed that NDVI is one of the most important parameters for determination of evaporation. In contrast it could be shown, that the parameters as vegetation height and measurement height have only minor influence on L.E, which justifies the use of default values for these parameters. Due to the key role of NDVI a field study was carried out investigating the spatial variability and sensitivity of NDVI above five different land use types (winter wheat, corn, grass, beech and spruce). Methods to determine this parameter not only from space (spectral), but also from in-situ tower measurements (broadband) and spectrometer data (spectral) were compared. The best agreement between the methods was found for winter wheat and grass measurements in 2006. For these land use types the results differed by less than 10 % and 15 %, respectively. Larger differences were obtained for the forest measurements. The correlation between the daily MODIS-NDVI data and the in-situ NDVI inferred from the spectrometer and the broadband measurements were r=0.67 and r=0.51, respectively. Subsequently, spatial variability of land surface parameters and fluxes were analysed. The several spatial resolutions of the satellite sensors can be used to describe subscale heterogeneity from one scale to the other and to study the effects of spatial averaging. Therefore land use dependent parameters and fluxes were investigated to find typical distribution patterns of land surface properties and energy fluxes. Implying the distribution patterns found here for albedo and NDVI from ETM+ data in models has high potential to calculate representative energy flux distributions on a coarser scale. The distribution patterns were expressed as probability density functions (PDFs). First results of applying PDFs of albedo, NDVI, relative humidity, and wind speed to the L.E computation are encouraging, and they show the high potential of this method. Summing up, the method of satellite based surface parameter- and energy flux determination has been shown to work reliably on different temporal and spatial scales. The data are useful for detailed analyses of spatial variability of a landscape and for the description of sub grid heterogeneity, as it is needed in model applications. Their usability as input parameters for modelling on different scales is the second important result of this work. The derived vegetation parameters, e.g. LAI and plant cover, possess realistic values and were used as model input for the Lokalmodell of the German Weather Service. This significantly improved the model results for L.E. Additionally, thermal parameter fields, e.g. surface temperature from ETM+ with 30 m spatial resolution, were used as input for SVAT-modelling (Soil-Vegetation-Atmosphere-Transfer scheme). Thus, more realistic L.E results were obtained, providing highly resolved areal information.Die vorliegende Arbeit wurde auf der Grundlage begutachteter Publikationen als kumulative Dissertation verfasst. Klimaprognosen basieren im Allgemeinen auf den Ergebnissen numerischer Simulationen mit globalen oder regionalen Klimamodellen. Eine der entscheidenden Unsicherheiten bestehender Modelle liegt in dem noch unzureichenden VerstĂ€ndnis von Wechselwirkungsprozessen zwischen der AtmosphĂ€re und LandoberflĂ€chen und dem daraus folgenden Fehlen entsprechender Parametrisierungen. Um das Problem einer unsicheren Modell-Parametrisierung aufzugreifen und zum Beispiel subskalige HeterogenitĂ€t in einer Art und Weise zu beschreiben, dass sie fĂŒr Modelle nutzbar wird, werden fĂŒr die Bestimmung und Evaluierung von Modell-ParametrisierungsansĂ€tzen so viele DatensĂ€tze wie möglich benötigt. Die Arbeit trĂ€gt zu diesem Thema durch die Verwendung verschiedener DatensĂ€tze unterschiedlicher Plattformen bei. Ziel der Studie war es, aus Satellitendaten verschiedener rĂ€umlicher und zeitlicher Auflösung sowie aus in-situ Daten die rĂ€umliche HeterogenitĂ€t von LandoberflĂ€chenparametern und Energieflussdichten zu bestimmen. Die Untersuchungen wurden fĂŒr zwei Zielgebiete in Deutschland durchgefĂŒhrt. FĂŒr das LITFASS-Gebiet (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study) wurden Satellitendaten der Jahre 2002 und 2003 untersucht und validiert. ZusĂ€tzlich wurde im Rahmen dieser Arbeit eine NDVI-Studie (Normalisierter Differenzen Vegetations Index: Maß zur Detektierung von VegetationflĂ€chen, deren VitalitĂ€t und Dichte) auf den TestflĂ€chen des FLUXNET Clusters um Tharandt in den Jahren 2006 und 2007 realisiert. Die Grundlage der Arbeit bildete die Bestimmung von LandoberflĂ€cheneigenschaften und daraus resultierenden EnergieflĂŒssen, auf Basis dreier optischer Sensoren (ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spectroradiometer) und AVHRR 3 (Advanced Very High Resolution Radiometer)) mit unterschiedlichen rĂ€umlichen (30 m – 1 km) und zeitlichen (1 – 16 Tage) Auflösungen. Unterschiedliche Sensorcharakteristiken, sowie die Verwendung verschiedener, zum Teil ungenauer DatensĂ€tze zur Landnutzungsklassifikation fĂŒhren zu Abweichungen in den Ergebnissen der einzelnen Sensoren. Durch die Quantifizierung der Sensorunterschiede, die Anpassung der Ergebnisse der Sensoren aneinander und eine QualitĂ€tsanalyse von verschiedenen Landnutzungsklassifikationen, wurde eine Basis fĂŒr eine vergleichbare Parametrisierung der OberflĂ€chenparameter und damit auch fĂŒr die daraus berechneten EnergieflĂŒsse geschaffen. Der Schwerpunkt lag dabei auf der Bestimmung des latenten WĂ€rmestromes (L.E) mit Hilfe des Penman-Monteith Ansatzes (P-M). Satellitendaten liefern Messwerte der spektralen Reflexion und der OberflĂ€chentemperatur. Die P-M Gleichung erfordert weitere OberflĂ€chenparameter wie zum Beispiel den NDVI, den BlattflĂ€chenindex (LAI), die Windgeschwindigkeit, die relative Luftfeuchte, die Vegetationshöhe oder die RauhigkeitslĂ€nge, die jedoch aus den Satellitendaten nicht bestimmt werden können. Sie mĂŒssen indirekt aus den oben genannten MessgrĂ¶ĂŸen der Satelliten oder aus in-situ Messungen abgeleitet werden. Stehen auch aus diesen Quellen keine Daten zur VerfĂŒgung, können sogenannte Standard- (Default-) Werte aus der Literatur verwendet werden. Die QualitĂ€t dieser Parameter hat einen großen Einfluss auf die Bestimmung der Strahlungs- und EnergieflĂŒsse. SensitivitĂ€tsstudien im Rahmen der Arbeit zeigen die Bedeutung des NDVI als einen der wichtigsten Parameter in der Verdunstungsbestimmung nach P-M. Im Gegensatz dazu wurde deutlich, dass z. B. die Vegetationshöhe und die Messhöhe einen relativ kleinen Einfluss auf L.E haben, so dass fĂŒr diese Parameter die Verwendung von Standardwerten gerechtfertigt ist. Aufgrund der SchlĂŒsselrolle, welche der NDVI in der Bestimmung der Verdunstung einnimmt, wurden im Rahmen einer Feldstudie Untersuchungen des NDVI ĂŒber fĂŒnf verschiedenen Landnutzungstypen (Winterweizen, Mais, Gras, Buche und Fichte) hinsichtlich seiner rĂ€umlichen VariabilitĂ€t und SensitivitĂ€t, unternommen. Dabei wurden verschiedene Bestimmungsmethoden getestet, in welchen der NDVI nicht nur aus Satellitendaten (spektral), sondern auch aus in-situ Turmmessungen (breitbandig) und Spekrometermessungen (spektral) ermittelt wird. Die besten Übereinstimmungen der Ergebnisse wurden dabei fĂŒr Winterweizen und Gras fĂŒr das Jahr 2006 gefunden. FĂŒr diese Landnutzungstypen betrugen die Maximaldifferenzen aus den drei Methoden jeweils 10 beziehungsweise 15 %. Deutlichere Differenzen ließen sich fĂŒr die ForstflĂ€chen verzeichnen. Die Korrelation zwischen Satelliten- und Spektrometermessung betrug r=0.67. FĂŒr Satelliten- und Turmmessungen ergab sich ein Wert von r=0.5. Basierend auf den beschriebenen Vorarbeiten wurde die rĂ€umliche VariabilitĂ€t von LandoberflĂ€chenparametern und FlĂŒssen untersucht. Die unterschiedlichen rĂ€umlichen Auflösungen der Satelliten können genutzt werden, um zum einen die subskalige HeterogenitĂ€t zu beschreiben, aber auch, um den Effekt rĂ€umlicher Mittelungsverfahren zu testen. DafĂŒr wurden Parameter und EnergieflĂŒsse in AbhĂ€ngigkeit der Landnutzungsklasse untersucht, um typische Verteilungsmuster dieser GrĂ¶ĂŸen zu finden. Die Verwendung der Verteilungsmuster (in Form von Wahrscheinlichkeitsdichteverteilungen – PDFs), die fĂŒr die Albedo und den NDVI aus ETM+ Daten gefunden wurden, bietet ein hohes Potential als Modellinput, um reprĂ€sentative PDFs der EnergieflĂŒsse auf gröberen Skalen zu erhalten. Die ersten Ergebnisse in der Verwendung der PDFs von Albedo, NDVI, relativer Luftfeuchtigkeit und Windgeschwindigkeit fĂŒr die Bestimmung von L.E waren sehr ermutigend und zeigten das hohe Potential der Methode. Zusammenfassend lĂ€sst sich feststellen, dass die Methode der Ableitung von OberflĂ€chenparametern und EnergieflĂŒssen aus Satellitendaten zuverlĂ€ssige Daten auf verschiedenen zeitlichen und rĂ€umlichen Skalen liefert. Die Daten sind fĂŒr eine detaillierte Analyse der rĂ€umlichen VariabilitĂ€t der Landschaft und fĂŒr die Beschreibung der subskaligen HeterogenitĂ€t, wie sie oft in Modellanwendungen benötigt wird, geeignet. Ihre Nutzbarkeit als Inputparameter in Modellen auf verschiedenen Skalen ist das zweite wichtige Ergebnis der Arbeit. Aus Satellitendaten abgeleitete Vegetationsparameter wie der LAI oder die Pflanzenbedeckung liefern realistische Ergebnisse, die zum Beispiel als Modellinput in das Lokalmodell des Deutschen Wetterdienstes implementiert werden konnten und die Modellergebnisse von L.E signifikant verbessert haben. Aber auch thermale Parameter, wie beispielsweise die OberflĂ€chentemperatur aus ETM+ Daten in 30 m Auflösung, wurden als Eingabeparameter eines Soil-Vegetation-Atmosphere-Transfer-Modells (SVAT) verwendet. Dadurch erhĂ€lt man realistischere Ergebnisse fĂŒr L.E, die hochaufgelöste FlĂ€cheninformationen bieten

    Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

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    Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat

    Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry

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    In California and other agricultural regions that are facing challenges with water scarcity, accurate estimates of crop evapotranspiration (ETc) can support agricultural entities in ongoing efforts to improve on-farm water use efficiency. Remote sensing approaches for calculating ETc can be used to support wide area mapping of crop coefficients and ETc with the goal of increasing access to spatially and temporally distributed information for these variables, and advancing the use of evapotranspiration (ET) data in irrigation scheduling and management. We briefly review past work on the derivation of crop coefficients and ETc data from satellite-derived vegetation indices (VI) and evaluate the accuracy of a VI-based approach for calculation of ETc using a well instrumented, drip irrigated sugar beet (Beta vulgaris) field in the California Central Valley as a demonstration case. Sugar beets are grown around the world for sugar production, and are also being evaluated in California as a potential biofuel crop as well as for their ability to scavenge nitrogen from the soil, with important potential benefits for reduction of nitrate leaching from agricultural fields during the winter months. In this study, we evaluated the accuracy of ETc data from the Satellite Irrigation Management Support (SIMS) framework for sugar beets using ET data from a weighing lysimeter and a flux station instrumented with micrometeorological instrumentation. We used the Allen and Pereira (A&P) approach, which was developed to estimate single and basal crop coefficients from crop fractional cover (fc) and height, and combined with satellite-derived fc data and grass reference ET (ETo) data as implemented within SIMS to estimate daily ETc from SIMS (ETc-SIMS) for the sugar beet crop. The accuracy of the daily ETc-SIMS data was evaluated against daily actual ET data from the weighing lysimeter (ETa-lys) and actual ET calculated using an energy balance approach from micrometeorological instrumentation (ETa-eb). Over the course of the 181-day production cycle, ETc-SIMS totaled 737.1 mm, which was within 7.7% of total ETa-lys and 3.7% of ETa-eb. On a daily timestep, SIMS mean bias error was −0.31 mm/day relative to ETa-lys, and 0.15 mm/day relative to ETa-eb. The results from this study highlight the potential utility of applying satellite-based fc data coupled with the A&P approach to estimate ETc for drip-irrigated crops

    Prediction of Early Vigor from Overhead Images of Carinata Plants

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    Breeding more resilient, higher yielding crops is an essential component of ensuring ongoing food security. Early season vigor is signi cantly correlated with yields and is often used as an early indicator of tness in breeding programs. Early vigor can be a useful indicator of the health and strength of plants with bene ts such as improved light interception, reduced surface evaporation, and increased biological yield. However, vigor is challenging to measure analytically and is often rated using subjective visual scoring. This traditional method of breeder scoring becomes cumbersome as the size of breeding programs increase. In this study, we used hand-held cameras tted on gimbals to capture images which were then used as the source for automated vigor scoring. We have employed a novel image metric, the extent of plant growth from the row centerline, as an indicator of vigor. Along with this feature, additional features were used for training a random forest model and a support vector machine, both of which were able to predict expert vigor ratings with an 88:9% and 88% accuracies respectively, providing the potential for more reliable, higher throughput vigor estimates
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