Fundamentals of Earth Observation Policy: Examples for German and European Missions

Several European countries have developed their national high resolution earth observation systems. Some of them are operated in close cooperation with industrial partners, others are dual-use missions earmarked to fulfil the needs of national security. In addition, the European Space Agency and the European Commission have initiated the Global Monitoring for Environment and Security (GMES) project. Therein, a fleet of satellites (SENTINELs) will deliver data for European wide information services, augmented by data from national and non-European earth observation systems. This new scenario needs clear guidance and regulations. Besides the principles for operations of earth observation missions – as set out in UN principles on earth observation – the operators of very high resolution missions require clear governmental acts which international users can be served and which data might be restricted in distribution. For national science and the SENTINEL-missions, a policy for free and open access is being developed to guarantee a maximum use of the data. Exemplified on the German national missions and the European GMES scenario, data policies and regulations for existing and new earth observation missions will be explained

INVESTIGATION OF DEFORESTATION USING MULTI-SENSOR SATELLITE TIME SERIES DATA IN NORTH KOREA

Department of Urban and Environmental Engineering(Environmental Science and Engineering)North Korea is very vulnerable to natural disasters such as floods and landslides due to institutional, technological, and other various reasons. Recently, the damage has been more severe and vulnerability is also increased because of continued deforestation. However, due to political constraints, such disasters and forest degradation have not been properly monitored. Therefore, using remote sensing based satellite imagery for forest related research of North Korea is regarded as currently the only and most effective method. Especially, machine learning has been widely used in various classification studies as a useful technique for classification and analysis using satellite images. The aim of this study was to improve the accuracy of forest cover classification in the North Korea, which cannot be accessed by using random forest model. Indeed, another goal of this study was to analyze the change pattern of denuded forest land in various ways. The study area is Musan-gun, which is known to have abundant forests in North Korea, with mountainous areas accounting for more than 90%. However, the area has experienced serious environmental problems due to the recent rapid deforestation. For example, experts say that the damage caused by floods in September 2016 has become more serious because denuded forest land has increased sharply in there and such pattern appeared even in the high altitude areas. And this led the mountain could not function properly in the flood event. This study was carried out by selecting two study periods, the base year and the test year. To understand the pattern of change in the denuded forest land, the time difference between the two periods was set at about 10 years. For the base year, Landsat 5 imageries were applied, and Landsat 8 and RapidEye imageries were applied in the test year. Then the random forest machine learning was carried out using randomly extracted sample points from the study area and various input variables derived from the used satellite imageries. Finally, the land cover classification map for each period was generated through this random forest model. In addition, the distribution of forest changing area to cropland, grassland, and bare-soil were estimated to the denuded forest land. According to the study results, this method showed high accuracy in forest classification, also the method has been effective in analyzing the change detection of denuded forest land in North Korea for about 10 years.ope

Analysis of the Geometric Quality of the LPIS-based RapidEye level 3A

This report describes the geometric image quality of the LPIS-based RapidEye level 3A product in the context of the Common Agriculture Policy (CAP) Control with Remote Sensing (CwRS) Programme. This product is similar to the RE standard level 3A product, but the standard RapidEye planimetric auxiliary data is replaced with a set of ground control points derived from the LPIS vector data. Based on the current analysis there are two issues that most likely driving the differences in the geometric quality of the provided tiles: the heterogonous quality of the input height data and a low polynomial order for reprojection to the Gauss-Krueger. In order to comprehensively verify these hypotheses, the quality analysis must be repeated. Based on the limited RapidEye sample images, the accuracy of the LPIS-based RapidEye level 3A products is within the RE product specifications accuracy (1-D RMSE of 6.5m) provided the shift elimination based on the set of well-distributed ground control points.JRC.DG.G.3 - Monitoring agricultural resource

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi- Temporal LiDAR Datasets

Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r(2) = 0.77, n = 79) and 2011 (r(2) = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+

Classificação automática de imagem do satélite rapideye para o mapeamento de áreas cafeeiras em Carmo de Minas, MG.

O mapeamento de áreas cafeeiras é o primeiro passo para estimar a produção e propor modelos de previsão das safras, Monitoramento ambiental e planejamento sustentável do agronegócio café. Estudos atuais descrevem metodologias de mapeamentos utilizando imagens de satélite. Recentemente foi lançado o sistema RapidEye, uma constelação de cinco satélites que carregam sensores com resolução espacial de 5 m e possibilidade de revisita na mesma área em períodos de 24 horas a 5,5 dias. O objetivo do presente estudo foi testar metodologias de classificação automática em uma imagem RapidEye visando o mapeamento de áreas cafeeiras em região de relevo fortemente ondulado. A resposta espectral da cafeicultura na imagem RapidEye apresentou-se bastante complexa em função das variáveis culturais e do ambiente. A precisão do mapeamento pelo método MAXVER editado foi considerada boa, com índice Kappa de 73%. A classe temática café foi bem mapeada por esse método. Para melhorar a qualidade do mapeamento automático de áreas cafeeiras é imprescindível o auxílio da interpretação visual e campanhas de campo para conferência da resposta espectral presentes na imagem RapidEye

Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal)

Madeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result.info:eu-repo/semantics/publishedVersio

Copernicus high-resolution layers for land cover classification in Italy

The high-resolution layers (HRLs) are land cover maps produced for the entire Italian territory (approximately 30 million hectares) in 2012 by the European Environment Agency, aimed at monitoring soil imperviousness and natural cover, such as forest, grassland, wetland, and water surface, with a high spatial resolution of 20 m. This study presents the methodologies developed for the production, verification, and enhancement of the HRLs in Italy. The innovative approach is mainly based on (a) the use of available reference data for the enhancement process, (b) the reduction of the manual work of operators by using a semi-automatic approach, and (c) the overall increase in the cost-efficiency in relation to the production and updating of land cover maps. The results show the reliability of these methodologies in assessing and enhancing the quality of the HRLs. Finally, an integration of the individual layers, represented by the HRLs, was performed in order to produce a National High-Resolution Land Cover ma

RapidEye - Initial findings of Geometric Image Quality Analysis

This report summarizes the outcomes of the preliminary geometric image quality analysis of the RapidEye 2A and 3A standard image products. The objective of this study is to evaluate some geometric characteristics of this satellite image data, in particular in the context of its suitability for the Common Agriculture Policy (CAP) Control with Remote Sensing (CwRS) Programme.JRC.DG.G.3-Monitoring agricultural resource

Earth Observation – A Fundamental Input for Crisis Information Systems

Space-borne and airborne earth observation (EO) is a highly valuable source of spatio-temporal information promoting the ability for a rapid up-to-date assessment and (near-) real-time monitoring of natural or and man-made hazards and disasters. Such information has become indispensable in present-day disaster management activities. Thereby, EO based technologies have a role to play in each of the four phases of the disaster management cycle (i.e. mitigation, preparedness, response and recovery) with applications grouped into three main stages: - Pre-disaster (preparedness and mitigation): EO-based information extraction for assessing potential spatial distributions and severities of hazards as well as the vulnerability of a focus region for disaster risk evaluation and subsequent mitigation and preparedness activities. - Event crisis (response): Assessment and monitoring of regional extent and severities of the characteristics and impacts of a disaster to assist rapid crisis management. - Post-disaster (recovery): EO based information extraction to assist recovery activities. Within the PHAROS system a wide range of data products are used, which are varying in temporal, spatial and spectral resolution and coverage. The used sensor platforms comprise space-borne satellites and airborne systems, i.e. aircrafts as well as unmanned aerial systems (UAS)