777 research outputs found

    Retrieval of biophysical parameters from multi-sensoral remote sensing data, assimilated into the crop growth model CERES-Wheat

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    This study investigated the possibilities and constraints for an integrated use of a crop growth model (CERES-Wheat) and earth observation techniques. The assimilation of information derived from earth observation sensors into crop growth models enables regional applications and may also help to improve the profound knowledge of the different involved processes and interactions. Both techniques can contribute to improved use of resources, reduced crop production risks, minimised environmental degradation, and increased farm income. Up to now, crop growth modelling and remote sensing techniquices mostly have been used separately for the assessment of agricultural applications. Crop growth models have made valuable contributions to, e.g., yield forecasting or to management decision support systems. Likewise, remote sensing techniques were successfully utilized in classification of agricultural areas or in the quantification of vegetation characteristics at various spatial and temporal scales. Multisensoral remote sensing approaches for the quantification biophysical variables are rarely realized. Normally the fusion of the data sources is based on the use of one sensor for classification purposes and the other one for the extraction of the desired parameters, based on the map classified previously. Pixel-based fusions between multispectral and SAR data is seldom realised for the assessment of quantitative parameters. The integration of crop growth models and remote sensing techniques by assimilating remotely sensed parameters into the models, is also still an issue of research. Especially, the integration of, e.g., multi-sensor biophysical parameter time-series for the improvement of the model performance, might feature a high potential. The starting point of the presented study was the question, if it is possible to derive the values of important crop variables from various remote sensing data? For the retrieval of these quantitative parameters by the use of various multispectral remote sensing sensors, intercalibration issues between the different retrieved vegetation indices had to be taken into account, in order to assure the comparability. Features influencing the vegetation indices are, e.g., the sensor geometry (like viewing- and solar-angle), atmospherical conditions, topography and spatial or radiometric resolution. However, the factors taken into account within this study are the spectral characteristics of the different sensors, like band position, bandwidth and centre wavelengths, which are described by the relative spectral response functions. Due to different RSR functions of the sensor bands, measured spectral differences occur, because the sensors record different components of the reflectance’s spectra from the monitored targets. These are then also introduced into the derived vegetation indices. The chosen cross-calibration method, intercalibrated the assessed Normalized Difference Vegetation Index and the Weighted Difference Vegetation Index between the various sensor pairs by regression, based on simulated multispectral sensors. Differences between the various assessed remote sensing sensors decreased form around 7% to below 1%. The intercalibration also had a positive impact on the later biophysical retrieval performance, producing sounder retrieval results. For the retrieval of the biophysical parameters empirical and semi-empirical models were assessed. The results indicate that the semi-empirical CLAIR model outperforms the empirical approaches. Not only for the Leaf Area Index retrieval, but also in the cases of all other assessed parameters. Concerning the other remote sensing data type used, the SAR data, it was analysed what potential different polarizations and incidence angles have for the extraction of the quantitative parameters. It became obvious that especially high incidence angles, as provided by the satellite Envisat ASAR, produce sounder retrieval results than lower incidence angles, due to a smaller amount of received soil signal. In the context of the assessed polarizations, sound results for the VV polarization could only be achieved for the retrieval of fresh biomass and the plant water content. For the ASAR sensor modelling fresh biomass and LAI using the HV polarization or the dry biomass using the ratio (HH/HV) was appropriate. As roughness aspects also have an influence on the retrieval performance from biophysical parameters using SAR data, the impact of soil surface and vegetation roughness was additionally considered. Best results were achieved, when also considering roughness features, however due to the need of regional modelling it is more appropriate not to consider them. For the calibration and re-tuning of crop growth models information about important phenological events such as heading/flowering is rather important. After this stage reproductive growth begins, whereby the number of kernels per plant is often calculated from plant weight at flowering and kernel weight is calculated from time and temperature available for dry matter distribution. By the use of the SAR VV time-series this important stage could be successfully extracted. Further methods for pixel-based fused biophysical parameter estimations, using SAR and multispectral data were analysed. By this approach the different features, being monitored of the two systems, are combined for sounder parameter retrieval. The assessed method of combining the multi-sensoral information by linear regression did not bring sound results and was outperformed by single sensor use, only taking into account the multispectral information. Only for the parameter fresh biomass, modelling based on the NDIV and the ASAR ratio slightly outperformed the single sensor modelling approaches. The complex combined modelling by the use of the CLAIR and the Water Cloud Model featured no valid results. For the combination, by using the CLAIR model and multiple regression slight improvements, in contrast to the single multispectral sensor use, were achieved. Especially, during late phenological stages, the assessed VV information improved the modelling results, in comparison to only using the CLAIR model. All the findings could finally be successfully applied for regional estimations. Only the roughness features could not be applied, due to the fact, that it is hard to regionally assess this needed model input parameter. Regional parameter on the basis of remote sensing data, is the major advantage of this technique, due to the large spatial overview given. The second main question was, if it is possible to integrate the crop variables gained from multisensoral data into a crop growth model, increasing the final yield estimation accuracy. Thus far, beneficial linkages between both techniques have been often limited to land use classification via remote sensing for choosing the adequate model and quantification of crop growth and development curves using biophysical parameters derived from remote sensing images for model calibration. Only a few studies actually considered the potentials of remote sensing for model re-initialization of growth and development characteristics of a specific crop, as the here studied winter wheat. Overall, the integration of remotely sensed variables into the crop growth model CERES-Wheat led to an improved final yield estimation accuracy in comparison to an automatic input parameter setting. The assessed final yield bias for the automatic input parameter setting summed up to 6.6%. When re-initializing the most sensitive input parameters (sowing date and fertilizer application date) by the use of remotely sensed biophysical variables the biases ranged from 0.56% overestimation to 5.4% understimation, in dependence of the data series used for assimilation. Whereby, it was assessed that the combined dense data series, considering SAR and multispectral information, slightly outperformed the performance of the full multispectral data series. However, when analysing the assimilation of the multispectral data series in further detail, it became clear that the actually information from the phenological stage ripening declines the modelling performance and thus the final yield estimation accuracy. When neglecting the information from this phenological stage the reduced multispectral data series performed as sound as the dense data series containing SAR and multispectral information. Thus, when the appropriate phenological stages are monitored by multispectral data, additional SAR information does not lead to a model improvement. However, when important dates are not monitored by multispectral images, e.g., due to cloud coverage, the additionally considered SAR information was not able to appropriatly fill these important multispectral time gaps. They even had a more negeative influence on the modelling performance. Overall, the best results could be obtained by assimilating a multispectral data series, covering the crop development during the important phenological stages stem elongation and flowering (without ripening stage), into the CERES-Wheat model. Finally, the integration of remote sensing data in the point-based crop growth model allowed it‘s spatial application for prediction of wheat production at a more regional scale. This approach also outperformed another evaluated method of direct multi-sensoral regional yield estimation. This study has demonstrated that biophysical parameters can be retrieved from remote sensing data and led, when assimilated into a crop growth model, to an improved final yield estimation. However, overall the SAR information did not really have a significant positive effect on the multi-sensoral biophysical parameter retrieval and on the later assimilation process. Thus, overall SAR information should only be considered, when multispectral data acquisitions are tremendously hampered by cloud coverage. The assessed assimilation of remote sensing information into a crop growth model had a positive effect on the final yield estimation performance. The analysed method, combining remote sensing and crop growth model techniques, was succsessfully demonstrated and will gain even more importance in the future for, e.g., decision support systems fine-tuning fertilizer regimes and thus contributing to more environmentally sound and sustained agricultural production

    Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

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    Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days

    Mapping of peanut crops in Queensland, Australia using time-series PROBA-V 100-m normalized difference vegetation index imagery

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    Mapping of peanut crops is essential in supporting peanut production, yield prediction, and commodity forecasting. While ground-based surveys can be used over small areas, the development of remote-sensing technologies could provide rapid and inexpensive crop area estimates with high accuracy over large regions. Some of these recent earth observation satellite systems, such as the Project for On-Board Autonomy Vegetation (PROBA-V), have the advantage of increased spatial and temporal resolution. With a study area located in the South Burnett region, Queensland, Australia, the primary aim of this study was to assess the ability of time-series PROBA-V 100-m normalized difference vegetation index (NDVI) for peanut crop mapping. Two datasets, i.e., PROBA-V NDVI time-series imagery and the corresponding phenological parameters generated from TIMESAT data analysis technique, were classified using maximum likelihood classification, spectral angle mapper, and minimum distance classification algorithms. The results show that among all methods used, the application of MLC in PROBA-V NDVI time series produced very good overall accuracy, i.e., 92.75%, with producer and user accuracy of each class ≄78.79  %  . For all algorithms tested, the mapping of peanut cropping areas produced satisfactory classification results, i.e., 75.95% to 100%. Our study confirmed that the use of finer resolution 100 m of PROBA-V imagery (i.e., relative to MODIS 250-m data) has contributed to the success of mapping peanut and other crops in the study area

    Monitoring of Plant Chlorophyll and Nitrogen Status Using the Airborne Imaging Spectrometer AVIS

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    Airborne hyperspectral remote sensing enables not only spatial monitoring of vegetation cover, but also the derivation of individual plant constituents such as chlorophyll and nitrogen content. These are important parameters for optimised agricultural management on a field basis through the possibility of spatially differentiated fertilisation and for hydrological and vegetation yield modelling. The use of existing airborne imaging spectrometers is cost-intensive. Moreover, it is difficult to obtain these sensors for multitemporal applications. The imaging spectrometer AVIS (Airborne Visible/Near Infrared Imaging Spectrometer) was built at the Chair of Geography and Geographical Remote Sensing of the Ludwig Maximilians University Munich, Germany, to overcome these difficulties. AVIS is designed as a cost-effective tool for environmental monitoring using commonly available components. AVIS enables the deployment of a hyperspectral sensor for both scientific research and educational purposes. It is based on a direct sight spectrograph coupled to a standard B/W CCD camera. The signal received by the CCD is read out and sent via a frame grabber card to a personal computer, where the data is stored on the hard disc together with additional GPS data. The radiometric, spectral and geometric properties of AVIS resulting from the calibration procedure are summarised in Table 7-1. Table 7-1: AVIS characteristics Parameter Description Spectral range 553-999nm Spectral resolution 6nm Spectral sampling rate / resampling 2nm / 6nm Number of used bands 74 SNR 45dB (year 1999), 47dB (year 2000) Spatial resolution 300 pixels per image line Spatial sampling rate 390 pixels per image line FOV 1.19rad IFOV across track 3.1mrad IFOV along track 2.98mrad One aim of this thesis was to test the potential of AVIS for the purpose of environmental monitoring, especially of the chlorophyll and nitrogen status of plants. The land cover types under investigation were grassland, maize ( Zea mays L.) and winter wheat ( Triticum aestivum L.). Within this scope, a total of 21 AVIS flights were carried out during the vegetation periods of the years 1999 and 2000. The AVIS data were preprocessed before analysis, including dark current and flat field correction, resampling as well as atmospheric correction and reflectance calibration. The test area chosen for the validation of the AVIS data is located in the northern Bavarian foothills, 25km southwest of Munich, Germany (48° 6’ N, 11° 17’ E). It is situated between the Ammersee in the west and the Starnberger See in the east. The municipalities Gilching and Andechs define the northern and southern borders respectively. Within this area, three water protection areas were chosen as test sites. In these test sites, most of the farmers are under contract to the local agricultural office “ Amt fĂŒr Landwirtschaft” resulting in detailed management data for each field. This data include useful information for the interpretation of ground and AVIS data. Two weather stations of the Bavarian network of agro-meteorological stations, namely No.72 (Gut HĂŒll) and No.80 (Rothenfeld), are located in the test area and provide information about precipitation, temperature and radiation. Ten and thirteen stands were selected as test fields in 1999 and 2000 respectively, including three fields each of maize and wheat in 1999 as well as three fields of maize and six fields of wheat in 2000. During both years, four meadows were investigated belonging to the same plant community ( Arrhenatherion elatioris). The meadows differ in the utilisation intensity (non-fertilised meadow with one cut, meadow with one cut, meadow with rotational grazing and meadow with four to five cuts). The ground truth campaigns included weekly measurements of plant parameters, such as height, dry and wet biomass, phenological stage, chlorophyll and nitrogen content, as well as a photographic documentation for each field. The chlorophyll and nitrogen measurements, which were derived from the sampling on ground, are available in contents per area [g/mÂČ] and in contents per mass ([mg/g] for chlorophyll and [%DM] for nitrogen). The former can be used to evaluate the photosynthetic capacity or productivity of a canopy, which is an important input parameter for hydrological or vegetation models; the latter may be an indicator for plant physiological status or level of stress, which is a valuable source of information for optimising field management. The relationship between chlorophyll and nitrogen based on the ground measurements showed that a differentiation of the land cover types is necessary for significant correlation. When the plant species are investigated separately, the chlorophyll and nitrogen content per area are always highly correlated, especially for chlorophyll a and total chlorophyll content (rÂČ≄0.8). For all investigated land cover types, the nitrogen and chlorophyll contents per mass are uncorrelated. For wheat, the results improve when the phenological state and the cultivar are considered (rÂČ>0.67). For maize, distinct variations in the chlorophyll content per mass during the vegetation period reduced correlation with these parameters. The use of a fitted chlorophyll trend curve instead of the original measurements does not lead to a significant improvement of the results. For grassland, no significant correlation above rÂČ=0.67 could be observed except for chlorophyll and nitrogen, both per area, where a decreasing strength of correlation could be monitored with increasing fertilisation level. These results lead to the conclusion that the chlorophyll and nitrogen contents per mass of the investigated land covers are decoupled when the compensation point for effective photosynthesis is exceeded. Beyond this limit the nitrogen in the plants is no longer incorporated into chlorophylls, but mainly into proteins, alkaloids and nucleic acids, whereas the proteins especially are used for internal storage of nitrogen. The derivation of the chlorophyll and nitrogen content of the plant leaves on a mean field basis was conducted using three hyperspectral spectral approaches, namely the hyperspectral NDVI (hNDVI), the Optimised Soil Adjusted Vegetation Index OSAVI as well as the relatively unknown Chlorophyll Absorption Integral CAI. The multispectral NDVITM was simulated as established reference. The results of the derivation of both chlorophyll and nitrogen content of plants with the investigated approaches depend strongly on a priori knowledge about the canopies monitored. In general, the use of contents per area rather than contents per mass has been found more suitable for the investigated remote sensing applications. A significant correlation between any index and the chlorophyll or nitrogen content for the whole sample size could not be derived. The optimal spectral approach for derivation is species-dependent, but also dependent on the cultivar. The chlorophyll and nitrogen level of the plants under observation as well as their temperature sensitivity mainly caused this dependence. The NDVITM, hNDVI and OSAVI became insensitive for high chlorophyll content above about 1g/mÂČ (1.5mg/g) chlorophyll a and 0.2g/mÂČ (0.4mg/g) chlorophyll b, respectively. A saturation of the indices was also found for nitrogen content above 2.5g/mÂČ. The saturation limit of nitrogen in percentage of dry matter could be rated at about 4%. The positive correlation between the indices and this parameter for wheat leads to insensitivity at values above this limit, while the negative correlation for maize results in saturation for values below 2.5%. The CAI is not affected by saturation as much as the other spectral approaches, leading to higher coefficients of determination, especially for contents per area. The CAI becomes insensitive at chlorophyll contents per area above 2g/mÂČ. The results lead to the assumption, that the flattening and narrowing of the chlorophyll absorption feature at 680nm most probably causes the saturation of the NDVITM, hNDVI and OSAVI. The ratios are directly affected by an increase in reflectance in the red wavelength region. The high correlations between the CAI and contents per area can be ascribed to the fact that the CAI is based on an integrated measurement over an area and therefore is less affected by an increase of reflectance in the red wavelengths. The CAI probably becomes insensitive at the point where the narrowing of the absorption feature leads to a shift of the red edge position towards the blue wavelength region. This saturation limit lies at approximately 2g chlorophyll per mÂČ. In contrast, the chlorophyll content per mass, which indicates the plant’s physiological status or level of stress, could be estimated more accurately using spectral indices such as hNDVI and OSAVI, especially for wheat. The low correlations derived for maize are caused by its higher temperature dependence, leading to daily variations in the chlorophyll content per mass. The chlorophyll and nitrogen contents of the grassland canopies could not be derived with the spectral approaches investigated. When the meadows were investigated separately, correlations could only be found between the CAI and the chlorophyll content per area for the most intensely utilised meadow (four to five cuts), which on the one side is characterised by the highest level of fertilisation, but on the other side is affected by the highest nutrient offtake. The low potential of the investigated indices can be mainly assigned to the fact that the chlorophyll and nitrogen values of the meadows mostly exceeded the saturation limits of the applied indices. The possibility of deriving chlorophyll and nitrogen accurately enough to map within field heterogeneities was discussed on the basis of a wheat field, which was analysed separately at three sampling points for chlorophyll and nitrogen content. The approaches found to be most suitable for the parameter estimation of wheat were applied. The CAI was used for the estimation of the chlorophyll content per area and mass as well as for the nitrogen content per area. The hNDVI was applied to estimate the canopy’s nitrogen content per mass. Both approaches were able to reproduce the chlorophyll contents of the different sampling points accurately enough to derive the differences between the measurement points when the saturation limits were not exceeded. Beyond these limits the index values decreased with increasing measurement values. The spatial pattern of the nutrient supply was discussed by comparing nitrogen pattern images, which were derived from CAI measurements acquired in 2000 with the yield measurement map of the same field. The phenological stage of stem elongation (EC 30) turned out to be most suitable for the derivation of the nitrogen pattern. On the one hand, the crop condition at these stages determine yield and on the other hand the nitrogen pattern images were able to map the inner field patterns of nitrogen supply. After anthesis the nitrogen images can map areas with different degrees of maturity. Therefore they can be used for the monitoring of maturity stages for the determination of the most favourable harvest date. As described here, AVIS is still in its early stages. It has the potential to become a costeffectiveAVIS2, which covers the spectral range of 400-900nm, has been in commercial use since 2001. tool for the monitoring of the environment. A modification of AVIS, namelyDie Arbeit mit hyperspektralen Fernerkundungssensoren ermöglicht nicht nur eine flĂ€chenhafte Aufnahme der Vegetationsdecke, sondern vor allem auch die Beurteilung des phĂ€nologischen und gesundheitlichen Zustandes der Pflanzen. Dies geschieht ĂŒber die Ableitung einzelner Pflanzeninhaltsstoffe, wie z. B. Chlorophyll und Stickstoff, beides bedeutende Parameter fĂŒr ein optimales Feldmanagement . Daneben spielen diese Pflanzeninhaltsstoffe eine bedeutende Rolle als Inputparameter fĂŒr hydrologische und pflanzenkundliche Modelle. Da sich derzeit noch keine operationell arbeitenden, satellitengestĂŒtzten Spektrometer im Orbit befinden, beschrĂ€nkt sich die flĂ€chenhafte Anwendung von hyperspektralen Fernerkundungssensoren auf den Einsatz flugzeuggetragener Spektrometer. Die Arbeit mit kommerziellen Sensoren, wie AVIRIS, DAIS, HYMAP oder ROSIS, ist aber mit einem hohen finanziellen Aufwand verbunden. Eine fĂŒr das Vegetationsmonitoring erforderliche multitemporale Anwendung wird sowohl durch die hohen Kosten als auch durch die limitierte VerfĂŒgbarkeit dieser Systeme erschwert. Diese EinschrĂ€nkungen gaben am Institut fĂŒr Geographie der Ludwig-Maximilians-UniversitĂ€t MĂŒnchen den Anlass fĂŒr die Entwicklung und den Bau eines institutseigenen flugzeuggetragenen abbildenden Spektrometers. Das vorrangige Ziel dabei war ein kostengĂŒnstiges System fĂŒr Forschung und Lehre. Diese Vorgaben fĂŒhrten zur Entwicklung des flugzeuggetragenen abbildenden Spektrometers AVIS (Airborne Visible/near Infrared imaging Spektrometer). Diese Arbeit beschĂ€ftigt sich sowohl mit der Kalibrierung als auch dem Einsatz von AVIS im Rahmen eines von der Deutschen Forschungsgemeinschaft DFG geförderten Projektes „Bestimmung des Stickstoffgehaltes von Vegetation – ein Beitrag zur deutschen BAHC Forschung“ (DFG MA 875 6). Die Kalibrierung von AVIS beinhaltet eine Beschreibung des Aufbaus mit den daraus resultierenden radiometrischen, spektralen und geometrischen Eigenschaften des Systems: AVIS ist ein Zeilenscanner, d.h. eine Bildzeile reprĂ€sentiert eine Aufnahme. Durch die Bewegung des Sensors ĂŒber der ErdoberflĂ€che hinweg entsteht durch die Aneinanderreihung mehrerer Aufnahmen ein Bildstreifen. Der Kern von AVIS ist ein direct sight Spektrograph, der zwischen ein Objektiv und eine Standard schwarz-weiß Videokamera montiert ist. Das einfallende Licht wird im Objektiv gebĂŒndelt und passiert dann den Spektrographen, wo es entlang einer spektralen Achse in verschiedene WellenlĂ€ngen dispergiert wird. Im Fall von AVIS wird fĂŒr jeden Bildpunkt einer Zeile die spektrale Information in 240 einzelnen WellenlĂ€ngen oder KanĂ€len abgebildet. Die Information wird auf dem CCD der Videokamera als elektrische Ladung registriert und ĂŒber eine Frame-Grabber-Karte auf der Festplatte eines angeschlossenen PCs gespeichert. Die Daten eines an AVIS gekoppelten GPS-GerĂ€tes, wie z.B. geographische LĂ€nge und Breite, Flughöhe ĂŒber NN und Zeitpunkt der Aufnahme, werden in einem header fĂŒr jede Bildzeile gespeichert. Die radiometrischen, spektralen und geometrischen Eigenschaften, welche sich aus der Kalibrierung von AVIS ergeben, sind in Tabelle 8-1 zusammengefasst. Tabelle 8-1: AVIS Spezifikationen Parameter Beschreibung Spektralbereich 553-999nm Spektrale Auflösung 6nm Spektrale Abtastrate / Resamplingrate 2nm / 6nm Anzahl verwendeter KanĂ€le 74 Signal-Rausch-VerhĂ€ltnis 45dB (Jahr 1999), 47dB (Jahr 2000) RĂ€umliche Auflösung 300 Pixel pro Bildzeile RĂ€umliche Abtastrate 390 Pixel pro Bildzeile FOV 1.19rad IFOV across track 3.1mrad IFOV along track 2.98mrad Der Einsatz von AVIS in der Vegetationsaufnahme, und hier speziell die Bestimmung des Chlorophyll- und Stickstoffgehaltes von Pflanzen, wird anhand drei verschiedener Landnutzungstypen erprobt, nĂ€mlich Mais ( Zea mays L.), (Winter-) Weizen ( Triticum aestivum L.) und GrĂŒnland. Dabei beschrĂ€nken sich die Untersuchungen auf die BlĂ€tter der Pflanzen. Die Untersuchung der Landnutzungstypen erfolgte wĂ€hrend der Vegetationsperioden der Jahre 1999 und 2000 in einem Testgebiet im nördlichen Alpenvorland, 25km sĂŒdwestlich von MĂŒnchen. Das Untersuchungsgebiet erstreckt sich von der Stadt Gilching im Norden bis zur Gemeinde Andechs im SĂŒden. Die westliche bzw. östliche Grenze bilden der Ammersee und der Starnberger See. Innerhalb dieses Untersuchungsgebietes wurden drei Wasserschutzgebiete gewĂ€hlt, in welchen die Testfelder liegen. Diese Gebiete zeichnen sich dadurch aus, dass die Mehrzahl der Landwirte vertraglich an das örtliche Landwirtschaftsamt gebunden ist. Diese VertrĂ€ge beinhalten u.a. die genaue Aufzeichnung der Bewirtschaftung im Rahmen der sog. Schlagkartei und stellen damit eine wertvolle Informationsquelle dar. Des weiteren ermöglichen zwei Wetterstationen des Bayerischen agrarmeteorologischen Messnetzes (Nr.72 „Gut HĂŒll“ und Nr.80 „Rothenfeld“) die Erfassung der meteorologischer Daten innerhalb des Untersuchungsgebietes in einer stĂŒndlichen Auflösung. Im Jahr 1999 wurden insgesamt zehn Testfelder untersucht, wobei je drei Felder mit Winterweizen (Sorte Bussard) und Mais (Sorte Narval und Sortenmischung Bristol/Korus) einbezogen waren. Im Jahr 2000 wurden sechs Weizenfelder (Sorten Bussard und Capo) und drei Maisfelder (Sorte Magister) untersucht. Außerdem wurden ĂŒber beide Jahre hinweg vier Felder mit der Nutzung als permanentes GrĂŒnland bearbeitet (einschĂŒrig ungedĂŒngt, einschĂŒrig gedĂŒngt, vier- bis fĂŒnfschĂŒrig und MĂ€hweide). Im Laufe der Vegetationsperioden von 1999 und 2000 wurden im Untersuchungsgebiet insgesamt 21 AVIS ÜberflĂŒge durchgefĂŒhrt. Dabei wurden die Testgebiete aus einer Höhe von 4000ft bzw. 10000ft ĂŒber NN erfasst, was bei einer mittleren GelĂ€ndehöhe von 680m zu einer rĂ€umlichen Pixelauflösung von 3 bzw. 10m fĂŒhrt. Vor der quantitativen Auswertung der hyperspektralen Daten mussten die Rohdaten vorprozessiert werden. Dies beinhaltete folgende Korrekturen: a) die Korrektur des Dunkelstromes und den Ausgleich von InhomogenitĂ€ten des CCD’s (Flatfield); b) ein Resampling der ursprĂŒnglich 240 KanĂ€le mit einer Abtastrate von 2nm zu einem 80-kanaligem Datensatz mit einer Abtastrate von 6nm, welche der spektralen Auflösung von AVIS entspricht; c) AtmosphĂ€renkorrektur und Reflexionskalibrierung. Die bodengestĂŒtzte GelĂ€ndekampagne beinhaltete wöchentlich durchgefĂŒhrte Messungen verschiedener Pflanzenparameter wie Höhe des Triebes und der BlĂ€tter, feuchte und trockene Biomasse, phĂ€nologischer Zustand, Chlorophyll- und Stickstoffgehalt getrennt nach Blatt, StĂ€ngel und Frucht. Außerdem wurde jedes Feld zu Dokumentationszwecken wöchentlich fotografiert. Die Chlorophyll- und Stickstoffgehalte, welche von den bodengestĂŒtzten Messungen abgeleitet wurden, liegen in Gehalten pro FlĂ€che [g/mÂČ] und in Gehalten pro Masse (bei Chlorophyll [mg/g] und bei Stickstoff [% der trockenen Biomasse]). Mit Hilfe des Gehaltes pro FlĂ€che können Aussagen ĂŒber die photosynthetische ProduktivitĂ€t oder KapazitĂ€t eines Bestandes getroffen werden – ein wichtiger Eingabeparameter fĂŒr hydrologische oder vegetationskundliche Modelle. Gehalte pro Masse dagegen geben Aufschluss ĂŒber den physiologischen Zustand der Pflanzen sowie ĂŒber Auswirkungen von Stress oder Krankheiten – wichtige Informationen fĂŒr ein optimales Feldmanagement durch den Landwirt. Der in den Pflanzen befindliche Stickstoff weist im sichtbaren und nahen infraroten WellenlĂ€ngenbereich keine spezifischen Absorptions- oder Reflexionsmuster auf. Aufgrund seines engen Zusammenhanges mit dem Pflanzenchlorophyll (jedes ChlorophyllmolekĂŒl enthĂ€lt vier Stickstoffatome) wird sein Gehalt ĂŒber die Menge des Chlorophylls abgeleitet. Der erste Teil der Auswertungen beschĂ€ftigte sich deshalb mit dem Zusammenhang des Gehaltes an Chlorophyll und Stickstoff in den BlĂ€ttern. Dabei konnte bei der gemeinsamen Analyse der drei Landnutzungsarten kein signifikanter Zusammenhang zwischen dUntersuchung konnte ein signifikant hoher Zusammenhang (rÂČ≄0.67) zwischen dem Stickstoff und Chlorophyll gefunden werden, wenn beide Parameter in Gehalten pro FlĂ€che vorliegen. Dabei korreliert insbesondere Chlorophyll a stark mit dem Stickstoffgehalt bei den untersuchten Mais-, Weizen- und GrĂŒnlandpflanzen (rÂČ≄0.8). Dagegen konnten bei allen drei Landnutzungstypen keine signifikanten Beziehungen zwischen dem Chlorophyll- und Stickstoffgehalt pro Masse nachgewiesen werden. Im Fall von Weizen verbesserten sich die Ergebnisse nach der Trennung in die unterschiedlichen Sorten (rÂČ≄0.67). Eine Unterscheidung der Wachstumsphasen ergab ebenfalls eine Verbesserung der Ergebnisse, wenn die Zeiten vor und nach der BlĂŒte getrennt untersucht wurden (rÂČ≄0.67). Die untersuchten Maissorten sind dagegen durch auffĂ€llige Schwankungen im Chlorophyllgehalt pro Masse geprĂ€gt. Diese Schwankungen werden von den aktuell herrschenden Temperaturen im Untersuchungsgebiet beeinflusst. Der Mais als ursprĂŒnglich tropische Pflanze stellt bei Temperaturen unter 15° das Wachstum ein und reduziert seinen Stoffwechsel erheblich, was Auswirkungen auf den Gehalt an aktivem Chlorophyll in den Pflanzen hat. Bei steigenden Temperaturen erholt sich der Stoffwechsel und die Pflanzen beginnen wieder zu wachsen. Diese ErkĂ€ltungssymptome ebenso wie die Erholungszeiten sind bei den verschiedenen Maissorten unterschiedlich ausgeprĂ€gt. Diese TemperaturabhĂ€ngigkeit fĂŒhrt im Untersuchungsgebiet, in dem wĂ€hrend der Sommermonate des öfteren Temperaturen unter 15°C erreicht werden, zu Variationen im Chlorophyllgehalt pro Masse, welche die Beziehung zum Stickstoff vermindern. Bei der Analyse der GraslandflĂ€chen ergab sich, außer bei den oben bereits erwĂ€hnten Gehalten pro FlĂ€che, kein signifikanter Zusammenhang zwischen Chlorophyll und Stickstoff. Die Analyse dieser Resultate fĂŒhren zu dem Schluss, dass die Stickstoff- und Chlorophyllgehalte pro Masse der untersuchten Landnutzungsarten ab einem bestimmten Level, dem Kompensationspunkt, entkoppelt sind. Dieser Kompensationspunkt wird dann erreicht, wenn das in der Luft enthaltene CO2 limitierend auf die Photosyntheserate wirkt. Wird dieses Limit ĂŒberschritten, hat ein weiterer Aufbau von ChlorophyllmolekĂŒlen keine Erhöhung der Photosyntheserate der Pflanze zur Folge. Eventuell vorhandener pflanzenverfĂŒgbarer Stickstoff wird somit nicht mehr fĂŒr den Einbau in Chlorophylle verwendet, sondern vermehrt fĂŒr die Synthese von Speicherproteinen genutzt. Ein weiterer Schwerpunkt dieser A

    A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

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    Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

    Integrating Remote Sensing and Ecosystem Models for Terrestrial Vegetation Analysis: Phenology, Biomass, and Stand Age

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    Terrestrial vegetation plays an important role in global carbon cycling and climate change by assimilating carbon into biomass during the growing season and releasing it due to natural or anthropogenic disturbances. Remote sensing and ecosystem models can help us extend our studies of vegetation phenology, aboveground biomass, and disturbances from field sites to regional or global scales. Nonetheless, remote sensing-derived variables may differ in fundamental and important ways from ground measurements. With the growth of remote sensing as a key tool in geoscience research, comparisons to ground data and intercomparisons among satellite products are needed. Here I conduct three separate but related analyses and show promising comparisons of key ecosystem states and processes derived from remote sensing and theoretical modeling to those observed on the ground. First, I show that the Moderate Resolution Imaging Spectroradiometer (MODIS) greenup product is significantly correlated with the earliest ground phenology event for North America. Spring greenup indices from different satellites demonstrate similar variability along latitudes, but the number of ground phenology observations in summer, fall, and winter is too limited to interpret the remote sensing-derived phenology products. Second, I estimate aboveground biomass (AGB) for California and show that it agrees with inventory-based regional biomass assessments. In this approach, I present a new remote sensing-based approach for mapping live forest AGB based on a simple parametric model that combines high-resolution estimates of Leaf Area Index derived from Landsat and canopy maximum height from the space-borne Geoscience Laser Altimeter System (GLAS) sensor. Third, I built a theoretical model to estimate stand age in primary forests by coupling a carbon accumulation function to the probability density of disturbance occurrences, and then ran the model with satellite-derived AGB and net primary production. The validated remote sensing data, integrated with ecosystem models, are particularly useful for large-region vegetation research in areas with sparse field measurements, and will help us to explore the long-term vegetation dynamics

    Time Series Analysis of Noaa Avhrr Derived Vegetation Cover as a Means to Extract Proportions of Permanent and Seasonal Components at Pixel Level

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    The scope of this study was to find a simple and robust technique to analyze a 16 years time se-ries (totalling 576 decades) of NOAA-AVHRR derived Green Vegetation Fraction (GVF) for de-scribing the bio-physical properties of the observed vegetation canopy as a function of its compo-sition in terms of a seasonally changing vegetation component and a permanent vegetation com-ponent. The principal idea behind the analysis is to use a simple model of an annual vegetation growth cycle per pixel, which is fitted against the available time sequence of data, and interpret on one side the parameters of the fit and on the other side the residuals of the original versus the fitted data. For simplicity reasons this part is represented by a sinus curve with a fixed wavelength of one year. This model allows splitting of the timely resolved vegetation signal into two compo-nents in vegetation appearance. One represents a "permanent background" throughout the year, which is the off-set between the 0 level representing the absence of vegetation cover and the minimum of the modelled seasonal change. The second represents the difference between the maximum and the minimum vegetation cover modelled every year. This technique has been ap-plied to the entire Mediterranean region covered by a NOAA AVHRR time series. The derived pro-portions of permanent and seasonal vegetation components have been finally interpreted on the European CORINE land cover class ‘Olive grove’, assessing the variation of permanent and sea-sonal vegetation components as function of management intensity, leading to a distinction of dif-ferent olive grove management intensity classes within the limits of the CORINE class. The olive class has been chosen as test case because of its well known linkages between the evergreen component represented by the olive trees and the more or less pronounced presence of annual herbaceous understory.JRC.H.5-Rural, water and ecosystem resource

    GeoAI approach to Vineyard Yield Estimation

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    A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales
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