Extraction of phenological parameters from temporally smothed vegetation indices

Within the MARS Crop Yield Forecasting System (MCYFS; Royer and Genovese, 2004) of the European Commission vegetation indicators like NDVI, SAVI and fAPAR are operationally derived for daily, decadal and monthly time steps. Besides low resolution sensors as SPOT-VGT and NOAA-AVHRR, medium resolution data from TERRA/AQUA-MODIS or ENVISAT-MERIS are used at pan-European level. In case of available time-series, esp. NOAA AVHRR (since 1981) and SPOT-VGT (since 1998) difference values of the indicators (e.g. relative or absolute differences) and frequency analysis of the indicators (e.g. position in historical range or distribution) are calculated. The exploitation of the data is performed at full resolution, at grid level of the MCYFS or regional unmixed means (C-indicators) are used. Therefore a database has been set-up in order to provide the indicators based on a weighted average for each CORINE land cover class within an area of interest. The study aims to develop a strategy for an optimal use of the different sensors and thus derived indicators at different aggregation levels for the ingestion into the MCYFS. As a first step smoothing algorithms have to be applied to the time series to diminish noise effects. Therefore, existing methods as simple sliding windows, piecewise linear regression or fitting of polynomial functions are employed and compared. Thereafter the time-series analysis is performed with the aim to establish relationships between indicators profile features and the crop phenology.JRC.DDG.H.4-Monitoring agricultural resource

Statistical GNSS performance assessment as part of the ALEGRO Ground Based Augmentation System inside Research Harbour Rostock

In the Research Port Rostock the Institute of Communication and Navigation of the German Aerospace Center deployed and operates an experimental Ground Based Augmentation System (GBAS). The first prototype of this GBAS system was developed under the project acronym ALEGRO to support positioning and navigation of marine users by the provision of phase based differential GNSS service. In time the implemented GBAS System is limited on a one receiver station system but will be extended by a second station up the end of 2009 for GBAS integrity monitoring. For the development and operation of GBAS the assessment of Global Navigation Satellite System (GNSS) signal and positioning quality at reference station site has to be considered as one elementary task before augmentation and correction data are derived and provided to marine users. Based on the decomposition of GNSS related measurements like e.g. ranges, phases, amplitudes and Signal to Noise Ratios (SNR) different quality parameters are derived in real time. Related to the fact that GBAS are normally placed at reference locations with low multipath effects, the influence of the environment in form of shadowing and reflections should be strongly limited. Daily derived statistics of quality parameters like e.g. code and carrier phase noise, SNR and power noise are used to derive values ranges of their regular (undisturbed) behaviour and to describe dependencies between them. For this purpose inside the ALEGRO processing system a statistical processor system operates to determine statistical parameters from 24 h measurements and derived real time quality parameters. They are used to model the regular value ranges and to identify thresholds for integrity monitoring in a next step. Such data are foreseen to support the monitoring of the GBAS operational system itself and to tune the measuring models used in the algorithms dealing with the provision of augmentation data and with the prediction and verification of the expected positioning accuracy and integrity in the GBAS environmental field. The paper will discuss the results of a 1 month measurement campaign. Under regular GNSS operation and signal propagation conditions it will be demonstrated that the quality parameters are reproducible at successive days. On basis of selected measuring examples it will be shown that the exact quantitative knowledge and description of the reference behaviour enables the detection of signal disturbances during GBAS operation. Besides the investigation of single quality parameters a special attention has been given on the description of dependencies between various quality parameters and their relation to satellite elevation and signal strength

Integrity concepts for future maritime Ground Based Augmentation Systems

Global Navigation Satellite Systems (GNSS) require augmentation to achieve integrity and accuracy performance for high-precise safety of life applications. The current standard maritime GNSS augmentation system is a differential GPS (DGPS) beacon system, which provides correction data and integrity information according to the IALA-standard [IALA-R-121]. They are broadcasted in the 300 kHz radio-navigation band in accordance with ITU-R Recommendations [DIN EN 61108-4]. Even if such systems, also called Ground Based Augmentation Systems (GBAS), increase the accuracy and integrity of GNSS substantially, the performance reached by these systems is not sufficient to meet all International Maritime Organization (IMO) requirements, especially those for critical traffic areas like ports and for e.g. automatic docking manoeuvres [IMO A.915(22)]. In order to support the applicability of satellite navigation in such areas, the German Aerospace Centre (DLR) has started to develop a maritime GBAS that meets all IMO requirements. While the current IALA (International Association of Marine Aids to Navigation and Lighthouse Authorities) GBAS is a Code-based Differential GNSS (C-DGNSS), what means it broadcasts information concerning code corrections, our developments aim for multi-frequency Phase-based Differential GNSS (P-DGNSS). For this purpose DLR has installed an experimental maritime GBAS in the port of Rostock (Germany) enabling algorithm development in the ground and user subsystem as well as their validation. The ground subsystem consists of two independent stations. The first station is operating as reference station and the second one as integrity monitoring station. This is similar to the hardware architectural design of the current IALA Beacon DGNSS architecture [IALA-R-121], whereby the GBAS uses high-rate receivers to enable a fast signal assessment in real time. Moreover, the proposed software architecture consists of real time processor chains that enable a hierarchical assessment from single data types via satellite signals up to the used GNSS with respect to the supported P-DGNSS service. Each of the implemented processors provides quality parameters like code and phase noise, Signal to Noise Ratio (SNR), Horizontal Positioning Error (HPE). These are considered as suitable input data for the GBAS integrity monitoring and the conditional provision of augmentation data and integrity flags. Thus Performance Key Identifiers (PKI) must be specified for each quality parameter which allows distinguishing between the nominal and the disturbed behaviour of GNSS and GBAS according to different positioning performances. The GBAS is complemented by a statistical analysis, which is deriving statistical performance parameters with respect to real time quality parameter collected during the previous 24 hours. The statistical performance parameters are used in the first instance to gradually improve the measuring models by an auto-adaptive system and to specify PKIs described by valid value ranges and thresholds. Then they are employed to detect outliers in real time and to estimate protection levels. The proposed quality parameters and related PKIs have been derived from 20 Hz GPS raw data of four GBAS stations in Germany (Research Port Rostock, DLR in Neustrelitz, Braunschweig) and France (Toulouse). Based on examples it will be shown that the nominal signal behaviour at the reference station can be employed to detect signal disturbances during GBAS operation in real time. In addition to the investigation of the single performance key identifiers, special attention is paid to the description of dependencies between the various performance key identifiers

Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series

Reliable drought information is of utmost importance for efficient drought management. This paper presents a fully operational processing chain for mapping drought occurrence, extent and strength based on Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data at 250 m resolution. Illustrations are provided for the territory of Kenya. The processing chain was developed at BOKU (University of Natural Resources and Life Sciences, Vienna, Austria) and employs a modified Whittaker smoother providing consistent (de-noised) NDVI “Monday-images” in near real-time (NRT), with time lags between zero and thirteen weeks. At a regular seven-day updating interval, the algorithm constrains modeled NDVI values based on reasonable temporal NDVI paths derived from corresponding (multi-year) NDVI “climatologies”. Contrary to other competing approaches, an uncertainty range is produced for each pixel, time step and time lag. To quantify drought strength, the vegetation condition index (VCI) is calculated at pixel level from the de-noised NDVI data and is spatially aggregated to administrative units. Besides the original weekly temporal resolution, the indicator is also aggregated to one- and three-monthly intervals. During spatial and temporal aggregations, uncertainty information is taken into account to down-weight less reliable observations. Based on the provided VCI, Kenya’s National Drought Management Authority (NDMA) has been releasing disaster contingency funds (DCF) to sustain counties in drought conditions since 2014. The paper illustrates the successful application of the drought products within NDMA by providing a retrospective analysis applied to droughts reported by regular food security assessments. We also present comparisons with alternative products of the US Agency for International Development (USAID)’s Famine Early Warning Systems Network (FEWS NET). We found an overall good agreement (R2 = 0.89) between the two datasets, but observed some persistent (seasonal and spatial) differences that should be assessed against external reference information

Integrity as Part of E-Navigation

Introduction E-Navigation Strategy of IMO Understanding of Integrity E-Nav Core Element GNSS Positioning, Navigation and Timing Unit GNSS as basis sensor for dynamic AIS data Traffic Situation Awareness Integrity of Basis Sensors Integrity of Traffic Situatio

Smoothing Time Series of Satellite Derived Vegetation Indices for Global Monitoring of Agricultural Productivity and Food Security

A global observation capacity is required for agricultural production forecasts and food security alert systems. The European Commission¿s Joint Research Center (JRC) uses low resolution satellite imagery to map vegetation status. The derived maps are used for near real-time production forecasts as well as for the anticipation of food security problems. The daily imagery used by JRC covers the entire globe at 1 km spatial resolution. An uninterrupted time series is available since 1998. To highlight the response of the vegetation, red and near infrared spectral responses are combined into a widely used vegetation index; the normalized difference vegetation index (NDVI). Growth anomalies are detected at the pixel scale by comparing the actual NDVI with the long term average NDVI. Sensing the Earth surface is not trivial as the electromagnetic radiation, which carries the information about the surface status, is scattered and absorbed by the Earth atmosphere. In addition, clouds may (partly) obstruct the field of view of the sensor. Altogether these perturbations lead to NDVI signals which are far lower of what would have been observed under perfect measurement conditions. To eliminate the strongest perturbations, the daily imagery is generally analyzed as 10-days maximum value composite (MVC) imagery (Holben et al., 1986). In this simple pre-processing step, for a given pixel location, only the highest NDVI value is retained for each 10-day (dekadal) period, thus minimizing the mentioned perturbations. Nevertheless, even dekadal NDVI-MVC images still contain perturbations. Sharp edge lines may appear in regions where insufficient registrations were available for the compositing process. Missing values occur for example at higher latitudes during polar night. Clouds and/or atmospheric conditions with high aerosol load may persist longer than 10 days, leading to sub-optimal MVC outputs which are easily recognized as irregular dips. The oral presentation aims at presenting and comparing three different smoothing strategies: ¿ Best index slope extraction (BISE) algorithm (Viovy et al., 1992) ¿ Weighted least square regression (Swets et al., 1999) ¿ Savitzky-Golay polynomial filtering (Savitzky & Golay, 1964; Chen et al., 2004) The algorithms are currently used at JRC for minimizing the undesired atmospheric/cloud effects, with the ultimate goal to enhance the signal stemming from the land surface. All approaches work within gliding windows of variable size and have been adapted to deal with missing values. Advantages and disadvantages of the different methods will be presented in the context of agricultural production estimates and for deriving phenological indicators useful in global change studies.JRC.G.3-Monitoring agricultural resource

The Use of Remote Sensing Within the Mars Crop Yield Monitoring System of the European Commission

The objective of the Mars Crop Yield Forecasting Systems (MCYFS) is to provide precise, scientific, traceable independent and timely forecasts for the main crops yields at EU level. The forecasts and analysis are used since 2001 as a benchmark by analysts from DG ¿ Agriculture and Rural Development in charge of food balance estimates. The system is supported by the use of Remote Sensing data, namely SPOT-VEGETATION, NOAA-AVHRR, MSG-SEVIRI and MODIS TERRA and in the future METOP AVHRR too. So a broad spectrum from low to medium resolution data at pan-European level is covered and historical time series go back to 1981 for NOAA and 1998 for SPOT VEGETATION. In order to work with the data operationally, processing chains have been set-up to make the data consistent with our requirements concerning near real time delivery (3 days), spatial coverage (pan-Europe), projection and ten day time steps. Moreover tailored indicators like NDVI, fAPAR and DMP are derived. In case of available time-series, difference values of the indicators (e.g. relative or absolute differences) and frequency analysis of the indicators (e.g. position in historical range or distribution) are calculated. The data is explored at full resolution or unmixed related to landcover types and aggregated at administrative NUTS 2 level (profile analysis of time series). Special tools to inspect and distribute the data to external users have been developed as well. Furthermore, it is the objective to develop a strategy for an optimal use of the different sensors and thus derived indicators at different aggregation levels for the ingestion into the MCYFS. As a first step smoothing algorithms have to be applied to the time series to diminish noise effects and to retrieve continuous information. Thus, an algorithm based on Swets et al. (1999) is employed. Thereafter, so-called Chronos Key Indicators are derived from the smoothed time-series. Currently, a study is carried out to establish the link between these indicators and (1) state variables of the crop growth simulation (e.g., development stages), and (2) the forecasted yield/production.JRC.G.3-Agricultur

Near real-time vegetation anomaly detection with MODIS NDVI: timeliness vs. accuracy and effect of anomaly computation options

For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different types of NDVI anomalies are typically employed to assess the current state and seasonal development of crops and rangelands as compared to previous years. Timeliness and accuracy of such anomalies are critical factors to an effective monitoring. Temporal smoothing can efficiently reduce noise and cloud contamination in the time series of historical observations, where data points are available before and after each data point to be smoothed. With NRT data, smoothing methods are adapted to cope with the unbalanced availability of data before and after the most recent data points. These NRT approaches provide successive updates of the estimation of the same data point as more observations become available. Anomalies compare the current NDVI value with some statistics (e.g. indicators of central tendency and dispersion) extracted from the historical record of observations. With multiple updates of the same datasets being available, two options can be selected to compute anomalies, i.e. using the same update level for the NRT data and the historical records or using the most reliable update for the statistics. In this study we assess the accuracy of three commonly employed 1 km MODIS NDVI anomalies (standard scores, non-exceedance probability and vegetation condition index) with respect to (1) delay with which they become available and (2) option selected for their computation. We show that a large estimation error affects the earlier estimates and that this error is efficiently reduced in subsequent updates. In addition, with regards to the preferable option to compute anomalies, we empirically observe that it depends on the type of application (e.g. averaging anomalies value over an area of interest vs. detecting “drought” conditions by setting a threshold on the anomaly value) and the employed anomaly type. Finally, we map the spatial pattern in the magnitude of NRT anomaly estimation errors over the globe and relate it to average cloudiness.JRC.D.5-Food Securit

Integritätsmonitoring für ein maritimes, phasenbasiertes Ground Based Augmentation System (GBAS)

Im Rahmen des durch das Bundesministerium für Bildung und Forschung geförderten Projektes „Advanced Sailing Management System“ (DLR, FKZ: 50NA0735) wurde ein Integritätsmonitoringkonzept entwickelt, das in das experimentelle GBAS im Forschungshafen Rostock implementiert wurde und derzeit validiert wird. Ausgangspunkt dieser Entwicklung war das beim IALA DGNSS Beacon angewendete Integritätsmonitoringkonzept für code-basierten DGNSS (C-DGNSS). Im IALA Beacon DGNSS wird die Verwendbarkeit einzelner Satellitensignale für eine C-DGNSS-basierte Positionsbestimmung z. B. daran entschieden, ob die abgeleiteten Range- und Rangeratenkorrekturen innerhalb von spezifizierten Wertebereichen liegen oder nicht. Das Gesamtsystem IALA Beacon DGNSS ist nur dann als nutzbarer Service klassifiziert, wenn die Anzahl verwendbarer Korrekturwerte eine C-DGNSS-basierte Positionsbestimmung im Servicebereich ermöglicht, der zugeordnete HDOP-Wert (Horizontal Dilution of Precision) nicht größer als 7.5 ist sowie der mit den Korrekturen erzielbare Positionsfehler innerhalb der Genauigkeitsanforderungen für Küstenbereiche liegt. Die Übertragung des IALA DGNSS Beacon Konzeptes auf ein phasenbasiertes GBAS erfordert die Einführung neuer Qualitätskenngrößen als auch die Verwendung neuer Bewertungsverfahren. Diese werden kurz vorgestellt und im Rahmen des abgeleiteten Architekturdesigns erläutert. Besonderes Augenmerk wird dabei auf standort- und ausrüstungstechnische Einflüsse auf die Qualitätskenngrößen und ihre Nutzung im Integritätsmonitoringprozess gelegt. Der bestehenden RTCM3-Standard (Radio Technical Commission for Maritime Services), der die Datenübertragung von P-DGNSS-spezifischen Ergänzungsdaten spezifiziert, beinhaltet derzeit noch keine Spezifikation für die Übertragung von integritätsspezifischen Zusatzinformationen. Im Experimentalsystem findet der Informationstransport über die durch das DLR definierte RTCM-Nachricht 4083 statt, wobei das genutzte Format abschließend anhand von identifizierten Anforderungen kurz erläutert wird

Chapter 4: Agriculture

Although the focus of remote sensing has broadened over the years, agriculture is still important as shown by the large—and increasing—number of publications dealing with remote sensing and agriculture. Not surprisingly, most remote sensing scientific conferences have at least one session dealing with agriculture. The importance of remote sensing in agriculture stems from the fact that agricultural activities face specific challenges not common to other economic sectors. As a result, agricultural activities have to be monitored from local to global scales at high temporal frequency. In recent years, we observed an increased use of remote sensing data and related technologies in agricultural production systems. First, remote sensing data have found their entrance in precision farming aiming to increase agricultural efficiency. Second, remote sensing is also a very valuable tool for monitoring agricultural expansion (e.g., following deforestation). Finally, by providing timely, comprehensive, objective, transparent, accurate, and unbiased data, remotely derived information can eventually prevent excessive market speculation and resulting price spikes.JRC.H.4-Monitoring Agricultural Resource