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

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

Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories. Remote Sens

Abstract: The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests ™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77–82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led t

Mediterranean-wide Green Vegetation Abundance for Land Degradation Assessment Derived from AVHRR NDVI and Surface Temperature 1989 to 2005

NOAA AVHRR data stemming from the MEDOKADS archive and ranging from 1989 to 2005 was processed and decomposed into their fractions of the vegetated, non-vegetated and the so called ¿cold¿ endmember. Decomposition occurred via Linear Unmixing within a triangle spanned up by NDVI (y-axis) and surface temperature (x-axis), separately for each of the 612 10-day composites. Endmembers were derived statistically using percentiles and the inverse relationship between NDVI and Ts. The cold endmember was fixed at -20 degrees Celsius, the vegetated endmember at NDVI = 0.7, the latter was then empirically corrected for illumination effects. Linear Unmixing occurred for the whole Mediterranean area, separately for a western and eastern window. Outcomes are the vegetation abundance, soil abundance and ¿cold¿ abundance, indicating the individual coverage of a pixel by each of these. The vegetation abundance was re-scaled to the so-called Grenn Vegetation Fraction (GVF), re-distributing the ¿cold¿ abundance on vegetation and soil abundance proportionally. Unmixing led to a higher stability of GVF data in comparison to NDVI data with regard to atmospheric effects. The data was post-processed for missing values and outliers and it was filtered. The GVF shows close parallelism on several test sites in comparison to a re-scaled NDVI within the endmember limits. The positive effect of the cold abundance, which is amongst other accounting for negative effects from poor atmospheric conditions and which was used to improve the GVF, could be clearly shown. Comparison with high and low resolution SPOT data shows a linear relationship and higher values for GVF. Squared GVF values were found to be closely correlated with independently derived high and low resolution vegetation cover (fCover), confirming this relationship known from literature. Coefficients of determination (R2), slope and offset of linear relations between squared GVF on one side and the two validation data sets on the other side were 0.69, 0.91, 0.07 and 0.58, 1.27, 0.06, respectively. In addition to the ¿per se¿ value of the derived abundances, validation results indicate that squared GVF may be used as approximation for vegetation cover.JRC.H.7-Land management and natural hazard

The use of remote sensing to characterise hydromorphological properties of European rivers

Remote sensing (RS) technology offers unparalleled opportunities to explore river systems using RADAR, multispectral, hyperspectral, and LIDAR data. The accuracy reached by these technologies recently has started to satisfy the spatial and spectral resolutions required to properly analyse the hydromorphological character of river systems at multiple scales. Using the River Hierarchical Framework (RHF) as a reference we describe the state-ofthe- art RS technologies that can be implemented to quantify hydromorphological characteristics at each of the spatial scales incorporated in the RHF (i.e. catchment, landscape unit, river segment, river reach, sub-reach - geomorphic and hydraulic units). We also report the results of a survey on RS data availability in EU member states that provides the basis for a discussion on the current potential to derive RHF hydromorphological indicators from high-resolution multispectral images and topographic LiDAR at the national scale across Europe. This paper shows that many of the assessment indicators proposed by the RHF can be already derived by different RS sources and existing methodologies, and that EU countries have sufficient RS data at present to already begin their incorporation into hydromorphology assessment and monitoring, as mandated by WFD, which so far have been insufficiently addressed by the member states due to the demanding efforts it would require. With cooperation and planning, RS data can form a fundamental component of hydromorphological assessment and monitoring in the future to help support the effective and sustainable management of rivers, and this would be done most effectively through the establishment of multi-purpose RS acquisition campaigns and the development of shared and standardized hydromorphological RS databases updated regularly through planned resurveyed campaigns.JRC.H.1-Water Resource

JRC MARS Bulletin: Crop monitoring in Europe: February 2018

Winter conditions so far present no threat to winter cropsJRC.D.5-Food Securit

JRC MARS Bulletin - Crop monitoring in Europe, November 2018

Harvesting of root and tuber crops also affected In large parts of central Europe, persistently dry soil conditions, complicated field preparations and sowing operations, and limited plant emergence and early crop development. Rapeseed areas in Germany, eastern Poland and northern Czechia are expected to be significantly reduced. Soft wheat can still be (re)sown in some countries. Favourable conditions for the sowing and emergence of winter crops prevailed in most parts of western and northern Europe.JRC.D.5-Food Securit

Einsatz von Fernerkundungsdaten und bodengestützten Daten zur regionalen Ertragsvorhersage von Braugerste (<i>Hordeum vulgare </i>L.)

Ernteprognosen erlauben die frühzeitige Ableitung von Informationen zur Versorgungslage des Braugerstenmarktes, wodurch die Braugerstenindustrie in der Lage ist, ihre Einkaufspolitik zu optimieren. Braugerste wird u. a. aufgrund ihrer Ansprüche vorwiegend in typischen Anbaugebieten kultiviert, dadurch können über regionale Beobachtungen Informationen über Anbauumfang und Wachstum gewonnen werden. In dieser Arbeit wurden Untersuchungen zur Anbaufläche und zum Flächenertrag von Braugerste (Hordeum vulgare L.), die in Deutschland vorwiegend als Sommergerste angebaut wird, in zwei Regionen in Südwestdeutschland durchgeführt. Es wurden zwei verschiedene Versionen von Ertragsprognosemodellen entwickelt, die auf einem empirisch-statistischen Ansatz beruhen. Als Eingabedaten sind einerseits multitemporale Fernerkundungsdaten, andererseits bodengestützte Daten wie meteorologische, phänologische, pedologische, agrarstatistische und administrative Daten verwendet worden. Da das Ernteaufkommen sowohl von der Flächenausdehnung als auch vom Flächenertrag abhängig ist, wurde im ersten Schritt Sommergerste mittels überwachter multitemporaler Klassifikation bestimmt. Zum Einsatz kamen hierfür optische Fernerkundungsdaten (LANDSAT TM/ETM+), topographische Daten (Digitales Höhenmodell) und wissensbasierte Regeln für die Klassifikation. Letztere waren im Hinblick auf die spezielle phänologische Entwicklung der Kultur von Interesse und konnten zur Unterscheidung von ähnlichen Kulturen genutzt werden. Die Klassifikation erfolgte auf Basis einer pixelbasierten und objektorientierten Methode. In Flurstücken mit mehr als 2 ha konnten bis zu 73 % der Sommergersteflächen klassifiziert werden. Die einfache Version des Ertragsprognosemodells basiert auf linearen Korrelationen zwischen Fernerkundungsdaten (NOAA-AVHRR-NDVI-Maximalwertkompositen) und agrarstatistischen Daten. In der Prozessierung der Fernerkundungsdaten wurden zudem Landbedeckungsdaten (CORINE land cover) genutzt. In einer erweiterten Version des Ertragsprognosemodells wurden zusätzlich meteorologische Daten (Temperatur, Evapotranspiration) und pedologische Daten integriert. Die Prognoseergebnisse wurden maßgeblich von der eingesetzten NDVI-Integrationszeitspanne beeinflusst. Der mittlere Prognosefehler (Abweichung von berichtetem zu prognostiziertem Ertrag) lag bei einer NDVI-Integration über die Kornfüllungsdauer hinweg bei 7,0 % für das einfache und bei 6,4 % für das erweiterte ErtragsprognosemodellUse of Remote Sensing and Soilborne Data for Regional Yield Predictions of Malting Barley (Hordeum vulgare L.) Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient organisation of the respective purchasing policy of raw materials. Since malting barley, due to its special requirements, is predominantly cultivated in a limited set of growing regions, yield predictions can be limited to these regions of interest. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.), in Germany mostly grown as spring barley, are performed for typical growing regions in South-Western Germany. Multitemporal remote sensing data on one hand and ancillary data such as meteorological, phenological, pedological, agrostatistical and adminis-trative data on the other hand are used as input data for two versions of prediction models, which were based on an empirical-statistical modelling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing opti-cal Remote Sensing data (LANDSAT TM/ETM+). The classification algorithm is considering spectral data, topographical data (Digital Elevation Model) and expert knowledge input. The latter is important with regard to the particular phenological development of the observed crop, an expertise which was used to distinguish it from similar crops. A pixel-based and an object-oriented classification algorithm were used for classification. For field plots larger than 2 ha up to 73 % of the spring barley area were classified. The basic version of the yield estimation model was conducted by means of linear correlation of remote sensing data (NOAA-AVHRR NDVI Maximum Value Composites), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, evapotran-spiration) and soil data were incorporated. Yield predictions were significantly affected by the selected time span for NDVI integration. For NDVI time-integration across the corn-filling period, the mean deviation of reported and simulated yield was 7.0 % and 6.4 % for the basic and extended yield estimation model, respectively

Phenology related measures and indicators at varying spatial scales. Investigation of phenology information for forest classification using SPOT VGT and MODIS NDVI data - PART I: EXTRACTION AND ANALSYSIS OF PHENOLOGY INDICATORS

The main goal of the present work within the context of the EBONE objectives is to investigate if leaf phenology indicators as derived from SPOT and MODIS NDVI time series can provide useful information for the detection and mapping of forest habitats, with specific reference to the General Habitat Category scheme. The report is divided into three main parts. The first part focuses on a description of the Phenolo model. This includes the pre-processing and processing steps applied to extract leaf phenology indicators from SPOT and MODIS data, and a short analysis of the spatial distribution of a selection of phenometrics in test areas. The second part introduces two pilot habitat classification tests using the Random Forests™ approach and SPOT NDVI data. The last part focuses on investigating the intercalibration of GHCs with MODIS-derived phenometrics. Random Forests classifications were tested in a variety of configurations and accuracy checked using the JRC Forest Map 2006. A set of 31 leaf phenology indicators (phenometrics) was extracted using JRC Phenolo model from a time series of NDVI ten day Maximum Value composites of six years (MODIS) and eleven years (SPOT). The Phenolo model considers an annual cycle of vegetation leaf phenology as represented by one permanent component, or ‘background’ and a variable component, function of seasonal dynamics. Pre-processing involved substitution of no data, outlier analysis and filtering. NDVI time series processing involved the extraction of date and productivity phenometrics. The model, coded in IDL, provided fast calculations in a stable environment. The performance of the Random Forests classifications and the contribution of individual phenometrics were tested through the calculation of the Mean Decrease Accuracy parameter (MDA). Overall, the results suggest date phenometrics to be more important for forest habitat classification than productivity phenometrics, especially indicators defined around the Peak of Season point and the NDVI curve minima. Apart from areas with spatially and spectrally homogeneous forest habitat classes (Coniferous forests in Austria), the overall classification accuracy achieved with the Random Forests approach using MODIS-based phenology indicators is generally not satisfactory. We identified three main factors influencing these result: the spatial/spectral heterogeneity present in the GHC forest polygons and subsequently in the training pixels associated to these classes: the low number of training pixels available and the use of an independent dataset to calculate accuracy which was built uniquely on spectral information. The introduction of artificial data gaps within the MODIS NDVI time series did not influence significantly classification accuracy. On the basis of the investigation results, the following remarks were made: 1) the spatial scale of current EObased phenology data (250 m) is at the edge of an adequate resolution for effective habitat classification with respect to GHC categories and field data; 2) It is recommended to build a large dataset of GHC training pixels in order to take into account the high spectral variability present within single GHC classes and 3) Adequate classification accuracy assessment should be based on a reference dataset which takes into account as much as possible the elements of heterogeneity typical of the GHCs. The structural (height) characteristics of the life forms types considered in the General Habitat Category scheme are very valuable information which should be taken into account when using EO-derived information. For this reason, for the purpose of GHCs classification a strategy that integrates EO-based phenology indicators with EO derived information on vegetation structure, from e.g. LiDAR or high resolution radar, could potentially be more effective than only a phenology-based approach.JRC.H.1-Water Resource

Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories

The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for systematic collection of environmental data. The GEO-BON European Biodiversity Observation Network project (EBONE) has recently adopted a habitat classification scheme based on General Habitat Categories (GHC) designed with an Earth Observation-perspective. Here we explore the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHC. Thirty-one phenometrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests™ classifier in selected regions. Results indicate date phenometrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77-82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to mixed phenology signals; (ii) the GHC scheme classification design, which allows for parcels of heterogeneous covers, and (iii) the low number of the training samples available from field survey. A mapping strategy integrating EO-based phenology with vegetation height information is expected to be more effective than a purely phenology-based approach