RapidEye Satellite Images Research Articles

GEOTECHNOLOGIES FOR THE CHARACTERIZATION OF SPECIALTY COFFEE ENVIRONMENTS OF MANTIQUEIRA DE MINAS IN BRAZIL

Abstract. Land-use/land-cover change (LUCC) is a major cause of environmental transformation. Distant drivers, often associated with emerging markets for specific products, are now being considered one of the main factors of LUCC and are gaining attention in land change science. Consumers show an increasing interest in local and quality food, certified for its origin and its environmental production standards. A kind of agricultural product certification, Geographic Indication, identifies a product as originating from a specific region where a given quality, reputation or characteristic is attributed to its geographical origin. Sustainable land use is potentially an indirect effect of Geographic Indication, as it requires better land management in order to preserve the natural resources associated with the unique characteristics of the certified product. Located in the southern region of the state of Minas Gerais in Brazil is the region of Mantiqueira de Minas, considered one of the most important regions for the production of specialty coffees in Brazil. In 2011, the region’s tradition and reputation were recognized with a Geographic Indication, the second given for coffee regions in the country. To explore the full potential of this area for producing coffees with higher quality and meet the growing demand of this new international market, which aggregates value at specialty coffees, it became fundamental to understand the coffee environments of the region, the “terroirs” where these coffees are obtained. Geotechnology can give a significant contribution in filling this gap. This work is part of a research project that made a detailed characterization of the region’s coffee agroecosystems. Geotechnologies were employed to map the areas occupied by coffee plantations by using RapidEye satellite images and SPRING and ArcGIS software. All the segments of the environment were characterized and mapped in detail and the relations with coffee quality were evaluated. The results showed that coffee occupies approximately 8% of the region’s total area and is mostly distributed in the hilly areas with higher elevations. They also proved that specialty coffees are more likely to be obtained in altitudes above 1100 m, also being correlated to genotype and to the post-harvest methods employed. These results provide information that allows a better understanding of the factors involved in the expression of coffee quality. They also provide the scientific basis required for obtaining a new Geographic Indication for the Mantiqueira de Minas region, this time a Denomination of Origin (DO), which will certainly aggregate value to the specialty coffees produced in this unique region.

GEOTECHNOLOGIES FOR THE CHARACTERIZATION OF SPECIALTY COFFEE ENVIRONMENTS OF MANTIQUEIRA DE MINAS IN BRAZIL

Land-use/land-cover change (LUCC) is a major cause of environmental transformation. Distant drivers, often associated with emerging markets for specific products, are now being considered one of the main factors of LUCC and are gaining attention in land change science. Consumers show an increasing interest in local and quality food, certified for its origin and its environmental production standards. A kind of agricultural product certification, Geographic Indication, identifies a product as originating from a specific region where a given quality, reputation or characteristic is attributed to its geographical origin. Sustainable land use is potentially an indirect effect of Geographic Indication, as it requires better land management in order to preserve the natural resources associated with the unique characteristics of the certified product. Located in the southern region of the state of Minas Gerais in Brazil is the region of Mantiqueira de Minas, considered one of the most important regions for the production of specialty coffees in Brazil. In 2011, the region’s tradition and reputation were recognized with a Geographic Indication, the second given for coffee regions in the country. To explore the full potential of this area for producing coffees with higher quality and meet the growing demand of this new international market, which aggregates value at specialty coffees, it became fundamental to understand the coffee environments of the region, the “terroirs” where these coffees are obtained. Geotechnology can give a significant contribution in filling this gap. This work is part of a research project that made a detailed characterization of the region’s coffee agroecosystems. Geotechnologies were employed to map the areas occupied by coffee plantations by using RapidEye satellite images and SPRING and ArcGIS software. All the segments of the environment were characterized and mapped in detail and the relations with coffee quality were evaluated. The results showed that coffee occupies approximately 8% of the region’s total area and is mostly distributed in the hilly areas with higher elevations. They also proved that specialty coffees are more likely to be obtained in altitudes above 1100 m, also being correlated to genotype and to the post-harvest methods employed. These results provide information that allows a better understanding of the factors involved in the expression of coffee quality. They also provide the scientific basis required for obtaining a new Geographic Indication for the Mantiqueira de Minas region, this time a Denomination of Origin (DO), which will certainly aggregate value to the specialty coffees produced in this unique region.

Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery

Crop monitoring using remotely sensed image data provides valuable input for a large variety of applications in environmental and agricultural research. However, method development for discrimination between spectrally highly similar crop species remains a challenge in remote sensing. Calculation of vegetation indices is a frequently applied option to amplify the most distinctive parts of a spectrum. Since no vegetation index exist, that is universally best-performing, a method is presented that finds an index that is optimized for the classification of a specific satellite data set to separate two cereal crop types. The η2 (eta-squared) measure of association – presented as novel spectral separability indicator – was used for the evaluation of the numerous tested indices. The approach is first applied on a RapidEye satellite image for the separation of winter wheat and winter barley in a Central German test site. The determined optimized index allows a more accurate classification (97%) than several well-established vegetation indices like NDVI and EVI (<87%). Furthermore, the approach was applied on a RapidEye multi-spectral image time series covering the years 2010–2014. The optimized index for the spectral separation of winter barley and winter wheat for each acquisition date was calculated and its ability to distinct the two classes was assessed. The results indicate that the calculated optimized indices perform better than the standard indices for most seasonal parts of the time series. The red edge spectral region proved to be of high significance for crop classification. Additionally, a time frame of best spectral separability of wheat and barley could be detected in early to mid-summer.

Evaluating the Woody Species Diversity by Means of Remotely Sensed Spectral and Texture Measures in the Urban Forests

There is an urgent need to obtain an accurate data on biological diversity and its temporal variation, in order to adopt an appropriate protection strategy to manage the urban forest remnants on sustainable manner for future generations. RapidEye Satellite images with spatial resolution of 5 × 5 m (orthorectified pixel size), ASTER VNIR images with spatial resolution of 15 × 15 m and Landsat-8 OLI images with spatial resolution of 30 × 30 m were used to evaluate the woody species diversity in urban forest remnants. Pearson Correlation Coefficient (PCC) was used to determine the relationships between the woody species diversity at Alpha (α) level and the spectral & texture properties derived from the satellite images. PCC test showed a positive significant relationship between the brightness of the RapidEye satellite image (i.e., Atmospherically Resistant Vegetation Index; ARVI) and the Simpson’s Diversity Index (S) (r = 0.80, p < 0.01). In order to also estimate the beta (β) diversity in the study, the relationship between species rarefaction curves and spectral rarefaction curves was calculated through multiple regression analysis i.e., Landsat NDVI, ASTER NDVI and RapidEye NDVI (respectively, R2 = 0.99; 0.99; 0.98, p < 0.01). The findings of the study revealed that satellite image with a resolution of 5 m would be more appropriate to estimate the woody species diversity of urban forest remnants. However, medium resolution satellite images (Landsat) may be used to examine the species rarefaction curve and β diversity in forest remnants.

Estimation of Above Ground Forest Biomass and Carbon Stock by Integrating Lidar, Satellite Image and Field Measurement in Nepal

For the first time in South Asia, the model-based Lidar Assisted Multisource Program (LAMP) was tested in 23500 km2 TAL area of Nepal by integrating 5% LiDAR sampling, wall-to-wall Rapid Eye satellite image and a representative field inventory to estimate Above Ground Biomass (AGB) and carbon stock. The average 1.26/m2LiDAR point density recorded by the scanner was used to measure canopy height and build a model using LiDAR variables and model coefficients. The developed LAMP model successfully estimated the AGB of the study area. The research tells that the study area comprises almost 50% forest cover with an average 211.63 t/ha AGB.Standing carbon stock was converted from AGB by multiplying the 0.47 which is default carbon fraction. Average standing carbon stock is 99.47 t/ha in the study area. The LAMP method found that the standing total AGB was 214.85-208.41 t/ha at a 95% confidence level and the FRA field-plot AGB estimate is 210.09/ha. This correspondence at this level of confidence means that the LAMP estimates are as accurate as those of the field-based inventory.J. Nat. Hist. Mus. Vol. 28, 2014: 160-170

Remote sensing monitoring of rural residential land based on RapidEye satellite images: A case study of Taihe County, Jiangxi Province

Remote sensing monitoring of rural residential land based on RapidEye satellite images: A case study of Taihe County, Jiangxi Province

CASI 초분광 영상을 이용한 RapidEye 위성영상의 대리복사보정

지상의 모든 물체는 고유의 분광 반사율을 갖고 있으며, 이러한 특성을 이용하여 지상 물체의 분류와 목표물 탐지 등이 가능하다. 정확한 분석을 위해서는 취득된 원격탐사 자료를 분광 반사율로 변환해야 한다. 이를 위한 절대복사보정 기법으로는 자료 제공 기관에서 명시한 변환 수식을 이용하는 방법, 지상에서 측정한 분광 반사율만으로 단순 경험적 회귀 분석을 이용하는 방법, ATCOR/FLAASH 같은 수학적 모델을 이용하는 방법 등이 있다. 본 연구에서는 CASI 초분광 영상의 분광 반사율 자료를 이용하여 RapidEye 위성영상의 대리복사보정을 수행하고, 그 결과를 다른 복사보정 기법 결과 및 지상 자료와 비교하였다. 실험 결과 제안 기법이 ATCOR 및 New Kurucz 2005 기법보다 높은 유사성을 보였으며, 일반적으로 활용되는 ELM 기법과 유사한 결과를 도출하였다.

Reconstruction of Lake Level Changes of Groundwater-Fed Lakes in Northeastern Germany Using RapidEye Time Series

Groundwater-fed lakes in northeastern Germany are characterized by significant lake level changes, but for only a few lakes are in situ water level measurements available. In this study, we test the potential of RapidEye satellite images for indirectly reconstructing lake level changes. The lake levels are derived by intersecting water-land borders with a high-resolution digital elevation model (DEM). Based on Lake Fürstenseer (LF), we define requirements and limitations of the method. Water-land borders were extracted automatically from the 37 RapidEye images available for the period between 2009 and 2014. Otsu’s threshold was used for the NIR band and for the normalized difference water index (NDWI). The results were validated with in situ gauging, contour lines from the DEM, and in situ Differential Global Positioning System (DGPS) measurements of the shoreline. Using an ideal shoreline subset, the lake levels could be reconstructed with decimeter accuracy using the NIR water-land border, but the levels were systematically underestimated by 0–20 cm. The accuracy of the reconstructed lake level retrieval strongly depends on the precision of the water-land border retrieval, on the accuracy of the DEM, and on the lake level itself. A clear shift of the water-land border with increasing lake level is also essential for the unambiguous reconstruction of different levels. This shift needs to be several times larger than the pixel size. The biggest challenges for lake level reconstruction are the presence of vegetation at the shorelines, the quality of the topographic data in the underwater area, the slope of the shoreline, and shadows in combination with low solar angles.

Determining the spatial and temporal dynamics of the green vegetation fraction of croplands using high-resolution RapidEye satellite images

The green vegetation fraction (GVF) is a key input variable of the evapotranspiration scheme applied in the widely used NOAH-MP land surface model (LSM). In standard applications of the NOAH-MP, the GVF is taken from a global map with a 15km×15km resolution. The central objective of the present study was (a) to derive gridded GVF data of a region in Southwest Germany in a high spatial resolution (5m×5m) from RapidEye satellite images, and (b) to improve the representation of the GVF dynamics of croplands in the NOAH-MP for a more accurate simulation of water and energy exchange between land surface and atmosphere. The GVF dynamics were determined based on the normalized difference vegetation index (NDVI) calculated from the red and near-infrared bands of the satellite images. The satellite GVF data were calibrated and validated against ground truth measurements. Based on the obtained calibration scheme, GVF maps were derived in a monthly resolution for the region. Our results confirm a linear relationship between GVF and NDVI and demonstrate that it is possible to determine the GVF of croplands from RapidEye images based on a simple two end-member mixing model. Our data highlight the high variability of the GVF in time and space. At the field scale, variability was mainly caused by soil heterogeneities and management differences. At the regional scale, the GVF showed a bimodal distribution formed by the different phenology of crops. We suggest to divide croplands according to their distinctly different temporal dynamics of the GVF into “early-covering” (winter rape, winter wheat, spring barley) and “late-covering” crops (sugar beet, silage maize). Based on our results, we recommend that simulations with LSM should take into account this differentiation of croplands, since it is to be expected that these two crop groups produce pronounced differences in energy partitioning at the land surface.

Extraction of Spatial Characteristics of Cadastral Land Category from RapidEye Satellite Images

With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.

The effects of 23 October and 9 November 2011 earthquakes on spatial transformation of the Van City

Earthquakes of 23 October 2011 and 9 November 2013 caused significant destruction on existing built-up areas of Van City, Turkey and had led to great loss of life and property. With the aim of identifying the effects of these two high-destructive earthquakes on spatial transformation process of the city, as well as to evaluate applied urban transformation strategies concerning to remove and mitigate the unfavourable effects of these earthquakes, maximum likelihood classification (MLC) is applied on related RapidEye satellite images to find out the urban macro-form changes, the change detection outputs by means of MLC results of the RapidEye satellite images dated before and after earthquakes which indicate that the overall accuracies are computed over 80%. The general results also reveal that the accuracies obtained are very promising to analyse spatial effect of the earthquakes on spatial transformation of the city. The identified findings of this analysis provide useful inputs to forthcoming researches aiming to evaluate the success of those kinds of urban transformation processes that may occur after similar natural disaster cases.

토지 피복 세분류를 위한 경지 정리 논 자동 추출

To fulfill the recent increasement in the public and private demands for various spatial data, the central and local governments started to produce those data. The low-level land cover map has been produced since 2000, yet the production of high-level land covered map has started later in 2010, and recently, a few regions was completed recently. Although many studies have been carried to improve the quality of land that covered in the map, most of them have been focused on the low-level and mid-level classifications. For that reason, the study for high-level classification is still insufficient. Therefore, in this study, we suggested the automatic extraction of land readjustment for paddy land that updated in the mid-level land mapping. At the study, the RapidEye satellite images, which consider efficient to apply in the agricultural field, were used, and the high pass filtering emphasized the outline of paddy field. Also, the binary images of the paddy outlines were generated from the Otsu thresholding. The boundary information of paddy field was extracted from the image-to-map registrations and masking of paddy land cover. Lastly, the snapped edges were linked, as well as the linear features of paddy outlines were extracted by the regional Hough line extraction. The start and end points that were close to each other were linked to complete the paddy field outlines. In fact, the boundary of readjusted paddy fields was able to be extracted efficiently. We could conclude in that this study contributed to the automatic production of a high-level land cover map for paddy fields.

Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into early-maturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.

Combination active optical and passive thermal infrared sensor for low-level airborne crop sensing

An integrated active optical, and passive thermal infrared sensing system was deployed on a low-level aircraft (50 m AGL) to record and map the simple ratio (SR) index and canopy temperature of a 230 ha cotton field. The SR map was found to closely resemble that created by a RapidEye satellite image, and the canopy temperature map yielded values consistent with on-ground measurements. The fact that both the SR and temperature measurements were spatially coincident facilitated the rapid and convenient generation of a direct correlation plot between the two parameters. The scatterplot exhibited the typical reflectance index-temperature profile generated by previous workers using complex analytical techniques and satellite imagery. This sensor offers a convenient and viable alternative to other forms of optical and thermal remote sensing for those interested in plant and soil moisture investigations using the ‘reflectance index-temperature’ space concept.

An Assessment of the Spectra Characteristics of Vegetation in South Western Nigeria

This study assessed the status of oil spill induced stress in the vegetation in Lagos and Ogun States, in the South West of Nigeria. The locations of oil spill impacted areas were identified on the field using information from the locals and a Global Positioning System with an accuracy of ± 5m and subsequently identified on the 5 m resolution RapidEye satellite image of 2009 and 2011. Field spectra were collected using Analytical Spectral Device (ASD) Handheld2 Spectrometer. The results showed varying spectral measurement of the impacted and the control. Indices were then used to distinguish less stressed from stressed vegetation which were statistically higher at F-ratio 4.825 (p < 0.01) and 3.194 (p < 0.01) in Lagos State; and 4.564 (p < 0.01) and 2.731 (p < 0.1) in Ogun State. It was concluded that oil spill had changed the spectra characteristics of impacted vegetation.

Monitoring above-ground forest biomass: A comparison of cost and accuracy between LiDAR assisted multisource programme and field-based forest resource assessment in Nepal

Analyzing forest monitoring costs and accuracy of forest carbon stock estimates are important criteria in the framework of Reducing Emission from Deforestation and Forest Degradation (REDD), because Monitoring, Reporting and Verification (MRV) system has been seen as an investment that aims to generate financial benefits to forest owners. Thus, comparisons of cost efficiency and accuracy were carried out between the LiDAR (Light Detection and Ranging) Assisted Multisource Programme (LAMP) and the field-based multisource Forest Resource Assessment (FRA) applied in the 23500 km2 Terai Arc Landscape (TAL) of Nepal in 2011 to estimate Above Ground Biomass (AGB). The model-based LAMP was applied by integrating 5% LiDAR sampling, wall to wall RapidEye satellite image and field sample plot inventory. The design-based FRA was carried out to generate comprehensive forest resource information. Administrative and initial variable costs of both approaches were calculated separately, and converted to unit costs for comparison. To compare the subsequent forest monitoring costs, cumulative costs were derived on the basis of the calculated present variable items and expenditures. The accuracies were calculated by using mean error of mean biomass estimates (tons/ha) at different spatial scales ranging from 1 to 350,000 ha forests. Design-based FRA was found to be cost-efficient (USD 0.22/ha) as compared to the LAMP approach (USD 0.28/ha) for baseline data collection, whereas administrative cost of multisource FRA (USD 0.26/ha) was significantly higher. Although a huge amount of data were generated through multisource FRA in each cycle, the LAMP approach appears to be cost-efficient to estimate AGB in subsequent forest inventory. The mean errors in the LAMP-derived mean biomass estimate were significantly smaller at all spatial resolutions than the FRA-plot-derived mean biomass estimate. The study concludes that spatial accuracy of LAMP is good enough to estimate biomass stock of Community Forests (CFs) where average size of CF is 150 ha in the study area. Banko Janakari, Vol. 23, No. 1, Page 12-22 DOI: http://dx.doi.org/10.3126/banko.v23i1.9463

Quantitative and Qualitative Assessment of Soil Erosion Risk in Małopolska (Poland), Supported by an Object-Based Analysis of High-Resolution Satellite Images

In 2011 the Marshal Office of Malopolska Voivodeship decided to evaluate the vulnerability of soils to water erosion for the entire region. The quantitative and qualitative assessment of the erosion risk for the soils of the Malopolska region was done based on the USLE approach. The special work-flow of geoinformation technologies was used to fulfil this goal. A high-resolution soil map, together with rainfall data, a detailed digital elevation model and statistical information about areas sown with particular crops created the input information for erosion modelling in GIS environment. The satellite remote sensing technology and the object-based image analysis (OBIA) approach gave valuable support to this study. RapidEye satellite images were used to obtain the essential up-to-date data about land use and vegetation cover for the entire region (15,000 km2). The application of OBIA also led to defining the direction of field cultivation and the mapping of contour tillage areas. As a result, the spatially differentiated values of erosion control practice factor were used. Both, the potential and the actual soil erosion risk were assessed quantificatively and qualitatively. The results of the erosion assessment in the Malopolska Voivodeship reveal the fact that a majority of its agricultural lands is characterized by moderate or low erosion risk levels. However, high-resolution erosion risk maps show its substantial spatial diversity. According to our study, average or higher actual erosion intensity levels occur for 10.6 % of agricultural land, i.e. 3.6 % of the entire voivodeship area. In 20 % of the municipalities there is a very urgent demand for erosion control. In the next 23 % an urgent erosion control is needed. Our study showed that even a slight improvement of P-factor estimation may have an influence on modeling results. In our case, despite a marginal change of erosion assessment figures on a regional scale, the influence on the final prioritization of areas (municipalities) according to erosion control needs is visible. The study shows that, high-resolution satellite imagery and OBIA may be efficiently used for P-factor mapping and thus contribute to a refined soil erosion risk assessment.

RapidEye 영상을 이용한 북한의 논 면적 산정

Remotely sensed satellite images can be applied to monitor and obtain land surface information on inaccessible areas. We classified paddy field area in North Korea based on on-screen digitization with visual interpretation using 291 RapidEye satellite images covering the whole country. Criteria for paddy field classification based on RapidEye imagery acquired at different time of rice growth period was defined. Darker colored fields with regular shape in the images with false color composite from early May to late June were detected as rice fields. From early July to late September, it was hard to discriminate rice canopy from other type of vegetation including upland crops, grass, and forest in the image. Regular form of readjusted rice field in the plains and uniform texture when compared with surrounding vegetation. Paddy fields classified from RapidEye imagery were mapped and the areas were calculated by administrative district, province or city. Sixty six percent of paddy fields () were distributed in the west coastal regions including Pyeongannam-do, Pyeonganbuk-do, and Hwanghaenam-do. The paddy field areas classified from RapidEye images showed less than 1% of difference from the paddy field areas of North Korea reported by FAO/WFP (Food and Agriculture Organization/World Food Programme).

Mapping field-scale yield gaps for maize: An example from Bangladesh

Accurate estimation of the size and spatial distribution of the yield gap has many practical applications, including relevance to precision agriculture and technology targeting. The objectives of this study were to illustrate a methodology to create a yield gap map and to discuss its potential uses to provide optimal crop management recommendations to the farmers. We used the HybridMaize crop simulation model to estimate potential yield for maize grown in the winter season in northwestern Bangladesh. This is a high yielding environment, where farmers achieve yields as high as 12Mg/ha. The model predicted a mean potential yield of 12.87Mg/ha. We used a RapidEye satellite image acquired around tasseling to identify the maize fields, calculate ground cover and its regression to actual yield from farmers’ fields. Next, the regression was applied to all the maize pixels in the image to calculate actual yield. In the last step, we created a yield gap map based on the difference between potential and actual yield. Yield gap maps will enable agronomists to identify production constraints on farmers’ fields with large yield gaps. Alternatively, by learning from the farmers with the highest actual yields and analyzing their data, it will be possible to generate region or field specific, optimized crop management recommendations.

OBJECT BASED IMAGE ANALYSIS TO SUPPORT ENVIRONMENTAL MONITORING UNDER THE EUROPEAN HABITAT DIRECTIVE: A CASE STUDY FROM DECOVER

Abstract. DeCOVER serves as a national extension of the European Global Monitoring for Environment and Security (GMES) initiative. It was initiated to develop land cover information services adapted to German user needs. One of its three service developments pillars is the application of Remote Sensing to support environmental monitoring schemes under the European Habitats Directive.Within two DeCOVER test sites located in North-Rhine Westphalia/Germany an object-based indicator classification approach is currently being developed to monitor heath habitats of importance under the Habitats Directive. While many previous Remote Sensing projects have focused on the discrete classification of habitat types to replace fieldwork, our approach is embedded in a strong operational context to a) focus and direct fieldwork efforts by pre-field visit assessment of habitat changes (change detection) and b) support fieldwork by contributing quality parameters and GIS-ready geometries. Using Geoeye satellite data (VHR component) and RapidEye satellite images (Multi-temporal HR component) together with existing habitat and biotope maps (knowledge and post-classification component) an image analysis approach is realised using object-based classification routines based on data mining tools to derive training information. To extract meaningful objects of heath-, sand- and grassland from the VHR-data, training sample areas have to be assigned. Thresholds and appropriate features for describing these samples are analysed by statistical algorithms and are used in the following classification. A multi-temporal approach for the acquisition of tree habitat areas integrates two RapidEye scenes into the classification process. To validate classification accuracies and potential transects were sampled in the field and analyzed for their structural composition using top view field photos of 1m2. First results demonstrate the realistic option to directly support the fieldwork or reduce its post-processing costs.