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Spatiotemporal geomorphological mapping of intertidal areas is essential for understanding system dynamics and provides information for ecological conservation and management. Mapping the geomorphology of intertidal areas is very challenging mainly because spectral differences are oftentimes relatively small while transitions between geomorphological units are oftentimes gradual. Also, the intertidal areas are highly dynamic. Considerable challenges are to distinguish between different types of tidal flats, specifically, low and high dynamic shoal flats, sandy and silty low dynamic flats, and mega-ripple areas. In this study, we harness machine learning methods and compare between machine learning methods using features calculated in classical Object-Based Image Analysis (OBIA) vs. end-to-end deep convolutional neural networks that derive features directly from imagery, in automated geomorphological mapping. This study expects to gain us an in-depth understanding of features that contribute to tidal area classification and greatly improve the automation and prediction accuracy. We emphasise model interpretability and knowledge mining. By comparing and combing object-based and deep learning-based models, this study contributes to the development and integration of both methodology domains for semantic segmentation.

Tidal flat systems with a diverse benthic community (e.g., bivalves, polychaetes and crustaceans) is important in the food chain for migratory birds and fish. The geographical distribution of macrozoobenthos depends on physical factors, among which sediment characteristics are key aspects. Although high-resolution and high-frequency mapping of benthic indices (i.e., sediment composition and benthic fauna) of these coastal systems are essential to coastal management plans, it is challenging to gather such information on tidal flats through in-situ measurements. The Synoptic Intertidal Benthic Survey (SIBES) database provides this field information for a 500m grid annual for the Dutch Wadden Sea, but continuous coverage and seasonal dynamics are still lacking. Remote sensing may be the only feasible monitoring method to fill in this gap, but it is hampered by the lack of spectral contrast and variation in this environment. In this study, we used a deep-learning model to enhance the information extraction from remote-sensing images for the prediction of environmental and ecological variables of the tidal flats of the Dutch Wadden Sea. A Variational Auto Encoder (VAE) deep-learning model was trained with Sentinel-2 satellite images with four bands (blue, green, red and near-infrared) over three years (2018, 2019 and 2020) of the tidal flats of the Dutch Wadden Sea. The model was trained to derive important characteristics of the tidal flats as image features by reproducing the input image. These features contain representative information from the four input bands, like spatial texture and band ratios, to complement the low-contrast spectral signatures. The VAE features, the spectral bands and the field-collected samples together were used to train a random forest model to predict the sediment characteristics: median grain size and silt content, and macrozoobenthic biomass and species richness. The prediction was done on the tidal flats of Pinkegat and Zoutkamperlaag of the Dutch Wadden sea. The encoded features consistently increased the accuracy of the predictive model. Compared to a model trained with just the spectral bands, the use of encoded features improved the prediction (coefficient of determination, R2) by 10-15% points for 2018, 2019 and 2020. Our approach improves the available techniques for mapping and monitoring of sediment and macrozoobenthic properties of tidal flat systems and thereby contribute towards their sustainable management.

In Flanders (northern Belgium), wild boar (Sus scrofa) returned in 2006 after 50 years of absence and the population is increasing, both in abundance and geographic extent. In the absence of wild boar, Flanders’ landscape structure changed into a dense, mosaic-like pattern of agricultural, natural, and urban areas. The return of the wild boar increasingly leads to human–wildlife conflicts, mainly linked to damage in agriculture. Hence, there is a growing need for a time-efficient, standardized, and accurate method to assess crop damage. We present an Unmanned Aerial Vehicle-based method, using Geographic Object-Based Image Analysis and Random Forests to estimate the damaged area and associated yield losses, between 2015 and 2017, due to wild boar in individual fields in Flanders. Our approach resulted in an 84.50% overall accuracy in calculating damaged area for maize fields and 94.40% for grasslands. Damage levels ranged between 14.3% and 20.2% in maize fields and 16.5% to 25.4% in grasslands. Our method can provide objective base data for compensation schemes and guide management strategies based on damage assessments.

The Vietnamese Mekong delta is subsiding due to a combination of natural and human-induced causes. Over the past several decades, large-scale anthropogenic land-use changes have taken place as a result of increased agricultural production, population growth and urbanization in the delta. Land-use changes can alter the hydrological system or increase loading of the delta surface, amplifying natural subsidence processes or creating new anthropogenic subsidence. The relationships between land use histories and current rates of land subsidence have so far not been studied in the Mekong delta. We quantified InSAR-derived subsidence rates for the various land-use classes and past land-use changes using a new, optical remote sensing-based, 20-year time series of land use. Lowest mean subsidence rates were found for undeveloped land-use classes, like marshland and wetland forest (~6–7 mm yr−1), and highest rates for areas with mixed-crop agriculture and cities (~18–20 mm yr−1). We assessed the relationship strength between current land use, land-use history and subsidence by predicting subsidence rates during the measurement period solely based on land-use history. After initial training of all land-use sequences with InSAR-derived subsidence rates, the land-use-based approach predicted 65–92% of the spatially varying subsidence rates within the measurement error range of the InSAR observations (RMSE = 5.8 mm). As a result, the spatial patterns visible in the observed subsidence can largely be explained by land use. We discuss in detail the dominant land-use change pathways and their indirect, causal relationships with subsidence. Our spatially explicit evaluation of these pathways provides valuable insights for policymakers concerned with land-use planning in both subsiding and currently stable areas of the Mekong delta and similar systems.

Irrigated agriculture practiced by smallholders is essential for food security in East Africa. Insight in the spatio-temporal distribution of irrigated agriculture is required to optimize irrigation water use. Irrigation-mapping efforts in the complex smallholder-dominated agricultural landscape in the Horn of Africa so far are generally too coarse and often the extent of smallholder irrigated agriculture is underestimated. The arrival of Sentinel-2 (10-m resolution) considerably enhanced the prospect of analyzing agriculture at field level. The objective of this study is to demonstrate the feasibility to map spatio-temporal patterns of smallholder irrigated agriculture in the Horn of Africa using a novel method based on object-based image analysis and Sentinel-2 imagery. The method includes segmentation at field level and smart process-based rules on neighbouring objects and NDVI time series to distinguish irrigated agriculture from rainfed agriculture. The assumption is that irrigation is applied at field level, while a rainfall event is not restricted to field borders and that this information on the local context of irrigated agriculture can be exploited in an object-based approach. Monthly land-use maps on irrigated agriculture were produced for September 2016 to August 2017 at 10-m resolution field level (objects). Three different spatial-heterogeneity thresholds were used to describe the vegetation development of neighbouring objects and to assign crop growth to either rainfall or irrigation. This method is unique as it can discriminate irrigation- and rainfall-induced crop growth, even in the rainy season. The estimates of irrigated agriculture in the Horn of Africa range from 27.96 Mha to 37.13 Mha. This is 2.8 to 3.7 times higher than the current highest estimate, the Global Irrigated Area Map at 1000 m resolution, and 1.2 to 1.7 times higher than the Irrigated Area Map Asia (2000–2010) and Africa (2010) when including water-managed non-irrigated croplands. For the dry season (October–March), the estimates of irrigated agriculture range from 17.67 Mha to 23.72 Mha. The irrigation frequency, the number of time steps (months) with irrigation events in the studied year, varies strongly. Irrigated area with an irrigation frequency of 1 to 2 events has a mapped surface area of 22.57 Mha to 23.13 Mha. Irrigated area with an irrigation frequency of 3 or more events has a mapped surface area of 4.83 Mha to 14.56 Mha. The produced maps will provide valuable information for the development of irrigated agriculture and optimization of irrigation water use in the Horn of Africa. In addition, the portability of this method to other (semi-)arid regions seems feasible as the local context of irrigated agriculture, used in this study for irrigation classification, describes universal characteristics regarding irrigated agriculture. This is especially valuable in the context of food security and water availability for other large data-poor regions in low- and middle-income countries.

River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE = 29-42 cm), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.

For decades land-use and land-cover (LULC) conversions have had an important impact on land- and ecosystem degradation, accordingly (historical) LULC information is important for the assessment of such impacts. This information can be derived from black-and-white (B&W) aerial photography. Such photography is often visually interpreted, which is a very time-consuming approach. This study shows that machine learning can be applied on only brightness to derive LULC information. Cropland acreage is semi-automatically mapped by means of Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classification in two study sites in Ethiopia and in The Netherlands. The result is a thematic map with two classes: 1) agricultural cropland and 2) other types of land cover. Overall mapping accuracies attained are 90 % and 96 % for the two study areas respectively. This mapping method increases the timeline at which historical cropland expansion can be mapped purely from brightness information in B&W photography up to the 1930s.

In recent years, object-oriented image analysis has been widely adopted by the remote sensing community. Much attention has been given to its application, while the fundamental issue of scale, here characterized by spatial object-definition, seems largely neglected. In the case of vegetation parameters like aboveground biomass and leaf area index (LAI), fundamental objects are individual trees or shrubs, each of which has a specific value. Their spatial extent, however, does not match pixels in size and shape, nor does it fit the requirements of regional studies. Estimation of vegetation parameters consequently demands larger observation units, like vegetation patches, which are better represented by variably shaped objects than by square pixels. This study aims to investigate optimal object definition for biomass and LAI. We have data from 243 field plots in our test site in southern France. They cover a vegetation range from landes to garrigue to maquis, which is considered to be the climax vegetation in the area. A HyMap image covers the area. The image is subjected to a Minimum Noise Fraction (MNF) transformation, after which it is segmented with ten different heterogeneities. The result is ten object sets, each having a different mean object size. These object sets are combined with the original image with the mean band values serving as object attributes. Field observations are linked to the corresponding objects for each object set. Using Ridge regression, relations between field observations and spectral values are identified. The prediction error is determined for each object set by cross validation. The overall lowest prediction error indicates the optimal heterogeneity for segmentation. Results show that the scale of prediction affects prediction accuracy, that increasing the object size yields an optimum in prediction accuracy, and that aboveground biomass and LAI can be associated with different optimal object sizes. Furthermore, it is shown that the accuracy of parameter estimation is higher for object-oriented analysis than for per-pixel analysis.

The ARGUS system is a shore-based, optical video system offering a suitable remote sensing technique for the purpose of long-term, high-resolution monitoring of coastal morphodynamics. Ten-minute time-exposure (timex) images obtained by the ARGUS cameras during low tide show the intertidal morphology (bars, troughs and rips) by the differences between water, wet sand and dry sand, where dry sand represents bars, and wet sand and water represent troughs and rips. A semi-automatic object-oriented algorithm was developed for classification of intertidal beach in low-tide video images and was tested on 13 low-tide ARGUS images collected at Noordwijk aan Zee, The Netherlands. Because of the strong relation between the visual observations and object-oriented image analysis, the ARGUS images are subdivided in small homogeneous areas (i.e. objects) by segmentation. Maximum likelihood classification creates a model for each day using a random selection of the objects, which are manually labelled, and their accompanying variables. Of the three classes, class wet sand had a classification fit of 43.4% when compared to an in situ classification; class water was correctly classified for 90.1% and dry sand could be classified best (92.8%). By combining their cross-shore position and their classification, objects can be directly linked with the respective morphological features.

Objective comparison of classification performance of earth observation images, acquired at different spatial resolutions (e.g. NOAA-AVHRR, IRS-MOS, IRS-WiFS, Landsat-TM, IRS-LISS), is complicated because both class definition and training site selection are hampered by the inherent scale differences. This paper presents a new, generic method to compare the information content of such a set of images, the “Stained Glass Procedure”. It overcomes the stated problems by computing the scale-dependent, internal spectral variation in an image and by using this as an indicator for land cover information. The Stained Glass Procedure creates segments in the images and calculates the internal spectral variation in a high-spatial-resolution image for each segment. For each image from the set the average variance, weighted to area, is calculated. The Stained Glass Procedure can be used to predict the performance of sensors that are not available, yet, or to roughly determine the optimal spatial resolution for the classification of a specific area. The procedure was applied to images with pixel sizes ranging from 23 to 1100 m. Classification detail of Envisat-MERIS (300 m pixel size), not included in the image set, could be predicted accurately using the Stained Glass Procedure. The Stained Glass Procedure applies one procedure to all images, without any subjective decision during the analysis, thus offering a method to compare images with different pixel sizes in terms of classification detail that is truly objective.

The Medium Resolution Imaging Spectrometer (MERIS) is a payload component of Envisat-1. MERIS will be operated over land with a standard 15 band setting acquiring images with a 300 m spatial resolution. The red-edge position (REP) is a promising variable for deriving foliar chlorophyll concentration, which plays an important role in ecosystem processes. The objectives of this paper are: (1) to study which factors effect the REP of vegetation, (2) to study whether this REP can be derived from the MERIS standard band setting and (3) to show what REP represents at the scale of MERIS data. Two different data sets were explored for simulating the REP using MERIS bands: (1) simulated data using reflectance models and (2) airborne reflectance spectra of an agricultural area obtained by the airborne visible-infrared imaging spectrometer (AVIRIS). A “linear method”, assuming a straight slope of the reflectance spectrum around the midpoint of the slope, was a robust method for determining the REP and the MERIS bands at 665, 708.75, 753.75 and 778.75 nm could be used for applying the “linear method” for REP estimation. Results of the translation to the scale of MERIS data were very promising for applying MERIS at, for instance, the ecosystem level.

The potential of using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images for large areas is often limited by cloud cover. It could be increased when small clouds are replaced by estimated reflection and emission values. In this study seven replacement methods are compared, ranging from simple replacement to stratified co-kriging. Images of subsequent days serve as co-variable, enabling the use of spatial and temporal information. For validation, cloud-free pixels were replaced with four patterns of artificially clouded pixels. Co-kriging as a combination of both temporal and spatial information resulted in the best estimates, reducing the mean squared errors by 20-70%. Stratification of the image did not result in better cloud replacement. Once kriging options have been implemented in existing image processing packages, co-kriging will be an easy-to-use solution to missing values, provided that images of subsequent days of low cloud coverage are available. | The potential of using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images for large areas is often limited by cloud cover. It could be increased when small clouds are replaced by estimated reflection and emission values. In this study seven replacement methods are compared, ranging from simple replacement to stratified co-kriging. Images of subsequent days serve as co-variable, enabling the use of spatial and temporal information. For validation, cloud-free pixels were replaced with four patterns of artificially clouded pixels. Co-kriging as a combination of both temporal and spatial information resulted in the best estimates, reducing the mean squared errors by 20-70%. Stratification of the image did not result in better cloud replacement. Once kriging options have been implemented in existing image processing packages, co-kriging will be an easy-to-use solution to missing values, provided that images of subsequent days of low cloud coverage are available.

The European Mediterranean regions are expected to encounter drier summer conditions and warmer temperatures for the winter of +2 °C and of +5 °C for the summer in the next six decennia. As a result the natural vegetation will face harsher conditions due to lower water availability, longer summer droughts and higher temperatures resulting in plant stress conditions. To monitor vegetation conditions like stress and leaf area index dynamics in our study area in Mediterranean France we use earth observation techniques like imaging spectroscopy. To assist image analysis interpretation we carried out a laboratory experiment to investigate the spectral and visible response of Buxus sempervirens , a common Mediterranean species, to five different types of stress: drought, drought-and-heat, light deprivation, total saturation and chlorine poisoning. For 52 days plants were subjected to stress. We collected data on the visible and spectral signs, and calculated thirteen vegetation indices. The plant’s response time to different stress types varied from 10 to 32 days. Spectroscopic techniques revealed plant stress up to 15 days earlier than visual inspection. Visible signs of stress of the plants included curling and shrinking of the leaves, de-colouring of the leaves, leaves becoming breakable, opening up of the plant’s canopy and sagging of the branches. Spectral signs of stress occurred first in the water absorption bands at 1450 and 1940 nm, followed by reduced absorption in the visible wavelengths, and next by reduced reflectance in near infrared. Light deprivation did not result in any stress signs, while drought, drought and heat and chlorine poisoning resulted in significant stress. The spectral response did not show differences for different stress types. Analysis of the vegetation indices identified the Carter-2 ( R 695 / R 760 ), the Red–Green Index ( R 690 / R 550 ) and the Vogelman-2 ( R 734 − R 747 )/( R 715 + R 726 ) as the best performing ones to identify stress. The lab experiment shows that spectroscopic techniques are useful to detect stress status of plants and in an earlier stage than visible signs of stress become apparent, but that spectroscopy does not reveal the type of plant stress.

Plant growth in Mediterranean landscapes is limited by the typical summer-dry climate. Forests in these areas are only marginally productive and may be quite susceptible to modern climate change. To improve our understanding of forest sensitivity to annual and seasonal climatic variability, we use tree-ring measurements of two Mediterranean evergreen tree species: Quercus ilex L. and Arbutus unedo L. We sampled 34 stems of these species on three different types of substrates in the Peyne study area in southern France. The resulting chronologies were analysed in combination with 38 yr of monthly precipitation and temperature data to reconstruct the response of stem growth to climatic variability. Results indicate a strong positive response to May and June precipitation, as well as a significant positive influence of early-spring temperatures and a negative growth response to summer heat. Comparison of the data with more detailed productivity measurements in two contrasting years confirms these observations and shows a strong productivity limiting effect of low early-summer precipitation. The results show that tree-ring data from Q.ilex and A.unedo can provide valuable information about the response of these tree species to climate variability, improving our ability to predict the effects of climate change in Mediterranean ecosystems.

River-floodplain systems are amongst the most productive—but often severely impacted—aquatic systems worldwide. We explored the ecological response of fish to flow regime in a large river-floodplain system by studying the relationships between (1) discharge and inundated floodplain area, with a focus on spatial and temporal patterns in floodplain lake connectivity, and (2) flood volume and fisheries catch. Our results demonstrate a non-linear relationship between discharge and floodplain inundation with considerable hysteresis due to differences in inundation and drainage rate. Inundation extent was mostly determined by flood volume, not peak discharge. We found that the more isolated lakes (that is, lakes with a shorter connection duration to the river) are located at higher local elevation and at larger hydrological distance from the main rivers: geographical distance to the river appears a poor predictor of lake isolation. Although year-to-year fish catches in the floodplain were significantly larger with larger flood volumes in the floodplain, they were not in the main river, suggesting that mechanisms that increase catch, such as increased floodplain access or increased somatic growth, are stimulated by flooding in the floodplain, but not in the river. Fish species that profit from flooding belong to different feeding guilds, suggesting that all trophic levels may benefit from flooding. We found indications that the ecological functioning of floodplains is not limited to its temporary availability as habitat. Refugia can be present within the floodplain itself, which should be considered in the management of large rivers and their floodplain.

Soil surface crusting and sealing are frequent but unfavorable processes in Mediterranean areas. Soil crust and seals form on bare soil subject to high-intensity rainfall, resulting in a hard, impenetrable layer that impedes infiltration and hampers the germination and establishment of plants. The adverse consequences of overland flow and reduced fertility can lead to erosion and ongoing degradation. Therefore, information on the distribution of surface crusts and their physical properties is essential to combat the undesired effects of crust formation in e.g. soil erosion and soil compaction. We studied the occurrence of crusts in a study area in Mediterranean southern France. Our objectives were to compare the physical and hydrological properties of the crusts and underlying soil, to identify the spectral characteristics (400 to 2500 nm) of the crusted and non-crusted soil surfaces using high-resolution field spectra, and to investigate the potential of mapping crust occurrence using airborne, hyperspectral HyMap images. The differences in some physical properties between crusted and non-crusted surfaces are significant while others are only marginal. Crusting markedly reduces the infiltration capacity and crust strength varies between the different soil types. Spectral differences are small, mainly in albedo values (overall reflectance) and in absorption band depth and shape. Albedo differences range from 8 to 40%. Differences in absorption band features in the spectra of crusts and non-crusted surfaces are small. Sixty percent of the crusted soil surfaces showed stronger absorption features in the clay mineral absorption bands at 2200 nm than non-crusted soils. Increased absorption is due to a relative enrichment in fines. Spectral feature fitting and linear spectral unmixing algorithms were applied to airborne HyMap images to evaluate the possibilities of mapping surface crusts. Crusts could be mapped in fallow, agricultural fields, but the spectral response of natural badlands was too fragmented for crust mapping.

Bubonic plague, caused by the bacteria Yersinia pestis, persists as a public health problem in many parts of the world, including central Kazakhstan. Bubonic plague occurs most often in humans through a flea bite, when a questing flea fails to find a rodent host. For many of the plague foci in Kazakhstan the great gerbil is the major host of plague, a social rodent well-adapted to desert environments. Intensive monitoring and prevention of plague in gerbils started in 1947, reducing the number of human cases and mortalities drastically. However, the monitoring is labour-intensive and hence expensive and is now under threat due to financial constraints. Previous research showed that the occupancy rate of the burrow systems of the great gerbil is a strong indicator for the probability of a plague outbreak. The burrow systems are around 30 m in diameter with a bare surface. This paper aims to demonstrate the automatic classification of burrow systems in satellite images using object-oriented analysis. We performed field campaigns in September 2007 and May and September 2008 and acquired corresponding QuickBird images of the first two periods. User's and producer's accuracy values of the classification reached 60 and 86%, respectively, providing proof of concept that automatic mapping of burrow systems using high-resolution satellite images is possible. Such maps, by better defining great gerbil foci, locating new or expanding foci and measuring the density of great gerbil burrow systems could play a major role in a renewed monitoring system by better directing surveillance and control efforts. Furthermore, if similar analyses can separate occupied burrow systems from empty ones, then very-high-resolution images stand to play a crucial role in plague surveillance throughout central Asia.

Vegetation growth in semiarid, Mediterranean ecosystems is greatly dependent on moisture availability in the soil, as little precipitation is available during the growing season. Predicting the effects of climate change on vegetation development requires understanding of the exact relation between climate, moisture availability, and plant growth. Accurate moisture measurements in naturally vegetated areas are difficult because of high spatial variability and because of the coarse, shallow soils. In this study, we evaluated the possibilities of using Electrical Resistivity Tomography (ERT) to measure soil moisture availability and plant water use in a Mediterranean natural area. We found that ERT is a useful tool for measuring soil conditions, providing information on the spatial patterns within the soil and reaching depths otherwise inaccessible. In heterogeneous soils, we differentiated between lithological and moisture effects in the measurements using multitemporal data. Absolute calibration to moisture content was sometimes possible, but strongly location dependent. Based on the ERT measurements, we found that although the soils in the study area are shallow and rocky, plant roots penetrate deeply into the fractured and weathered bedrock, and vegetation subtracts water from depths down to 6 m and below. This information is important for understanding the plant–soil relations and modeling vegetation development. We conclude that ERT provides crucial information on soil moisture processes unavailable using any other currently available measurement method.

The Medium Resolution Imaging Spectrometer (MERIS) is one of the sensors carried by Envisat. MERIS is a fully programmable imaging spectrometer, however a standard 15-channel band set will be transmitted for each 300 m pixel (over land while over the ocean the pixels will be aggregated to 1200 m spatial resolution) covering visible and near-infrared wavelengths. Since MERIS is a multidisciplinary sensor providing data that can be input into ecosystem models at various scales, we studied MERIS's performance relative to the scale of observation using simulated datasets degraded to various spatial resolutions in the range of 6-300 m. Algorithms to simulate MERIS data using airborne imaging spectrometer datasets were presented, including a case study from DAIS (i.e. Digital Airborne Imaging Spectrometer) 79-channel imaging spectrometer data acquired on 8 July 1997 over the Le Peyne test site in southern France. For selected target endmembers garrigue, maquis, mixed oak forest, pine forest and bare agricultural field, regions-of-interest (ROI) were defined in the DAIS scene. For each of the endmembers, the vegetation index values in the corresponding ROI is calculated for the MERIS data at the spatial resolutions ranging from 6 to 300 m. We applied the NDVI, PVI, WDVI, SAVI, MSAVI, MSAVI2 and GEMI vegetation indices. Above-ground biomass (AGB) was estimated in the field and derived from the DAIS image and the MERIS datasets (6-300 m spatial resolution). The vegetation indices are shown to be constant with the spatial scale of observation. The strongest correlation between the MERIS and DAIS NDVI is obtained when using a linear model with an offset of 0.15 (r =0.31). A Pearson correlation matrix between AGB measured in the field and each spectral band reveals a modest but significant (p

European policy is currently aiming at reducing the environmental impact of agriculture. To evaluate the effect of the measures, a Europe-wide method is needed that provides standardized information for all countries. This paper presents the outline of a method intended to determine where and to what extent the environmental impact of agriculture has changed. It consists of three procedures: general change detection, determination of agricultural presence in the region, and change identification. The method offers standardized information on the location and extent of changes in environmental impact caused by agriculture. Changed regions can be located shortly after the growing season, which enables the adjustment of policy to the actual situation. Hence, it will provide a useful tool to evaluate and formulate policy measures intended to influence the environmental impact of agriculture.