Earth observations from DSCOVR EPIC instrument

The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764, and 779 nm. We discuss a number of preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O3) amount, scene reflectivity, erythemal irradiance, ultraviolet (UV) aerosol properties, sulfur dioxide (SO2) for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties.The NASA GSFC DSCOVR project is funded by NASA Earth Science Division. We gratefully acknowledge the work by S. Taylor and B. Fisher for help with the SO2 retrievals and Marshall Sutton, Carl Hostetter, and the EPIC NISTAR project for help with EPIC data. We also would like to thank the EPIC Cloud Algorithm team, especially Dr. Gala Wind, for the contribution to the EPIC cloud products. (NASA Earth Science Division)Accepted manuscrip

Time Series Analysis of MODIS NDVI data with Cloudy Pixels: Frequency-domain and SiZer analyses of vegetation change in Western Rwanda

Remote sensing is a valuable source of data for the study of human ecology in rural areas. In this thesis, I attempt to analyze the presence of a long-term trend indicative of post-resettlement adaptation in the vegetation signals of Western Rwanda. There is a dearth of research utilizing medium resolution imagery to study difficult environments, such as tropical-montane regions, where complex topography and cloud cover diminish image accuracy. I attempt to add to the extant literature on frequency-domain smoothing methods as well as the literature on human-environment interaction in tropical-montane regions by applying a harmonic filtering and smoothing algorithm to the ‘MOD13Q1’, 16-day composite, 250m, NDVI, MODIS imagery. To create a more robust time-series, I combine Gaussian generalized additive models and discrete Fourier analysis of the residuals to impute values to a filtered time series, based on MODIS’s own pixel reliability data. These methods significantly improve the quality of the time-series being analyzed, compared with the raw data, or imputation of the mean signal. To control for conflating variables, I take a difference-in-differences (DD) approach (Abadie, 2005) comparing resettled regions to older regions, identified in Google Earth. Harmonic filtering and smoothing shows a definite long-term trend of post-resettlement changes in the vegetation signal, demonstrated by the DD approach, analyzed in SiZer maps (Chaudhuri & Marron, 1999). Further research will be needed to determine whether this is indicative of cropping changes, or other impacts of post-resettlement adaptation

Automated Extraction of Fire Line Parameters from Multispectral Infrared Images

Remotely sensed infrared images are often used to assess wildland ¯re conditions. Separately, ¯re propagation models are in use to forecast future conditions. In the Dynamic Data Driven Application System (DDDAS) concept, the ¯re propagation model will react to the image data, which should produce more accurate predictions of ¯re propagation. In this study we describe a series of image processing tools that can be used to extract ¯re propagation parameters from multispectral infrared images so that the parameters can be used to drive a ¯re propagation model built upon the DDDAS concept. The method is capable of automatically determining the ¯re perimeter, active ¯re line, and ¯re propagation direction. A multi-band image gradient calculation, the Normalized Di®erence Vegetation Index, and the Normalized Di®erence Burn Ratio along with several standard image processing techniques are used to identify and constrain the ¯re propagation parameters. These ¯re propagation parameters can potentially be used within the DDDAS modeling framework for model update and adjustment

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others

Using middle-infrared reflectance for burned area detection

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Meteorologia), Universidade de Lisboa, Faculdade de Ciências, 2011A strategy is presented that allows deriving a new index for burned area discrimination over the Amazon and Cerrado regions of Brazil. The index is based on information from the near-infrared (NIR) and middle-infrared (MIR) channels of the Moderate Resolution Imaging Spectroradiometer (MODIS). A thorough review is undertaken of existing methods for retrieving MIR reflectance and an assessment is performed, using simulated and real data, about the added value obtained when using the radiative transfer equation (RTE) instead of the simplified algorithm (KR94) developed by Kaufman and Remer (1994), the most used in the context of burned area studies. It is shown that use of KR94 in tropical environments to retrieve vegetation reflectance may lead to errors that are at least of the same order of magnitude of the reflectance to be retrieved and considerably higher for large values of land surface temperature (LST) and solar zenith angle (SZA). Use of the RTE approach leads to better estimates in virtually all cases, with the exception of high values of LST and SZA, where results from KR94 are also not usable. A transformation is finally defined on the MIR/NIR reflectance space aiming to enhance the spectral information such that vegetated and burned surfaces may be effectively discriminated. The transformation is based on the difference between MIR and NIR in conjunction with the distance from a convergence point in the MIR/NIR space, representative of a totally burnt surface. The transformation allows defining a system of coordinates, one coordinate having a small scatter for pixels associated to vegetation, burned surfaces and soils containing organic matter and the other coordinate covering a wide range of values, from green and dry/stressed vegetation to burned surfaces. The new set of coordinates opens interesting perspectives to applications like drought monitoring and burned area discrimination using remote-sensed information.O coberto vegetal da superfície da Terra tem vindo a sofrer mudanças, por vezes drásticas, que conduzem a alterações tanto na rugosidade da superfície terrestre como no seu albedo, afectando directamente as trocas de calor sensível e latente e de dióxido de carbono entre a superfície terrestre e a atmosfera (Sellers et al., 1996). Neste contexto, as queimadas assumem um papel de extremo relevo (Nobre et al., 1991; O’Brien, 1996; Xue, 1996) na medida em que constituem uma das mais importantes fontes de alteração do coberto vegetal, resultando na destruição de florestas e de recursos naturais, libertando carbono da superfície continental para a atmosfera (Sellers et al., 1995) e perturbando as interacções biosfera-atmosfera (Levine et al., 1995; Scholes, 1995) através de mudanças na rugosidade do solo, na área foliar e noutros parâmetros biofísicos associados ao coberto vegetal. Ora, neste particular, a Amazónia Brasileira constitui um exemplo notável de mudanças no uso da terra e do coberto vegetal nas últimas décadas, como resultado da desflorestação induzida pelo homem bem como por causas naturais (Gedney e Valdes, 2000; Houghton, 2000; Houghton et al., 2000; Lucas et al., 2000), estimando-se que as regiões tropicais sejam responsáveis por cerca de 32% da emissão global de carbono para a atmosfera (Andreae, 1991). Neste contexto, a disponibilidade de informações pormenorizadas e actualizadas sobre as distribuições espacial e temporal de queimadas e de áreas ardidas em regiões tropicais afigura-se crucial, não só para uma melhor gestão dos recursos naturais, mas também para estudos da química da atmosfera e de mudanças climáticas (Zhan et al., 2002). A detecção remota constitui, neste âmbito, uma ferramenta indispensável na medida em que permite uma monitorização em tempo quase real, a qual se revela especialmente útil em áreas extensas e/ou de difícil acesso afectadas pelo fogo (Pereira et al., 1997). Diversos instrumentos, tais como o Land Remote Sensing Satellite/Thematic Mapper (LANDSAT/TM) e o National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) têm vindo a ser extensivamente utilizados na gestão dos fogos florestais, em particular aos níveis da detecção de focos de incêndio e da monitorização de áreas queimadas. Mais recentemente, o instrumento VEGETATION a bordo do Satellite Pour l'Observation de la Terre (SPOT) tem vindo a ser utilizado com sucesso na monitorização de fogos. Finalmente, são de referir os sensores da série Along Track Scanning Radiometer (ATSR) para os quais têm vindo a ser desenvolvidos algoritmos de identificação de focos de incêndio, e ainda o sensor Moderate Resolution Imaging Spectroradiometer (MODIS) que tem vindo a demonstrar capacidades óptimas no que respeita à observação global de fogos, plumas e áreas queimadas. Neste contexto, os métodos actuais de detecção de áreas ardidas através da detecção remota têm vindo a dar prioridade à utilização das regiões do vermelho (0.64 μm) e infravermelho-próximo (0.84 μm) do espectro eletromagnético. No entanto, tanto a região do vermelho quanto a do infravermelho-próximo apresentam a desvantagem de serem sensíveis à presença de aerossóis na atmosfera (Fraser e Kaufman, 1985; Holben et. al., 1986). Desta forma, em regiões tropicais como a Amazónia, onde existem grandes camadas de fumo devido à queima de biomassa, a utlização destas duas regiões do espectro eletromagnético torna-se insatisfatória para a detecção de áreas ardidas. Por outro lado, a região do infravermelho médio (3.7 – 3.9 μm) tem a vantagem de não ser sensível à presença da maior parte dos aerossóis, exceptuando a poeira (Kaufman e Remer, 1994) mostrando-se, ao mesmo tempo, sensível a mudanças na vegetação devido à absorção de água líquida. Com efeito, estudos acerca dos efeitos do vapor de água na atenuação do espectro eletromagnético demonstraram que a região do infravermelho médio é uma das únicas regiões com relativamente pouca atenuação (Kerber e Schut, 1986). Acresce que a região do infravermelho médio apresenta uma baixa variação da irradiância solar (Lean, 1991), tendo-se ainda que a influência das incertezas da emissividade na estimativa da temperatura da superfície é pequena quando comparada com outras regiões térmicas tais como as de 10.5 e 11.5 μm (Salysbury e D’Aria, 1994). A utilização da radiância medida através de satélites na região do infravermelho médio é, no entanto, dificultada pelo facto de esta ser afectada tanto pelo fluxo térmico quanto pelo fluxo solar, contendo, desta forma, duas componentes, uma emitida e outra reflectida, tendo-se que a componente reflectiva contém os fluxos térmico e solar reflectidos pela atmosfera e pela superfície enquanto que as emissões térmicas são oriundas da atmosfera e da superfície. Ora, a componente solar reflectida é de especial interesse para a detecção de áreas ardidas pelo que se torna necessário isolá-la do sinal total medido pelo sensor. Devido à ambiguidade deste sinal, a distinção dos efeitos da reflectância e da temperatura torna-se uma tarefa muito complexa, verificando-se que os métodos em que se não assume nenhuma simplificação, levando-se, portanto, em consideração todos os constituintes do sinal do infravermelho médio se tornam complexos e difíceis de serem aplicados na prática, na medida em que requerem dados auxiliares (e.g. perfis atmosféricos) e ferramentas computacionais (e.g. modelos de tranferência radiativa). Kaufman e Remer (1994) desenvolveram um método simples para estimar a reflectância do infravermelho médio o qual assenta em diversas hipóteses simplificadoras. Apesar do objectivo primário que levou ao desenvolvimento do método ser a identificação de áreas cobertas por vegetação densa e escura em regiões temperadas, este método tem sido lagarmente utilizado nos estudos acerca da discriminação de áreas queimadas, algumas das vezes em regiões tropicais (Roy et al., 1999; Barbosa et al., 1999; Pereira, 1999). Na literatura não existe, no entanto, nenhum estudo acerca da exactidão e precisão deste método quando aplicado com o objectivo de detectar áreas ardidas, em especial em regiões tropicais. Neste sentido, no presente trabalho procedeu-se a um estudo de viabilidade do método proposto por Kaufman e Remer (1994) em simultâneo com a análise da equação de tranferência radiativa na região do infravermelho médio, tendo sido realizados testes de sensibilidade dos algoritmos em relação aos erros nos perfis atmosféricos, ruído do sensor e erros nas estimativas da temperatura da superfície. Para tal recorreu-se ao modelo de transferência radiativa Moderate Spectral Resolution Atmospheric Transmittance and Radiance Code (MODTRAN), dando-se especial atenção ao caso do sensor MODIS. Os resultados demonstraram que a utilização do método proposto por Kaufman e Remer (1994) em regiões tropicais para a estimativa da reflectância no infravermelho médio, leva a erros que são pelo menos da mesma ordem de magnitude do parâmetro estimado e, em alguns casos, muito maior, quando ocorre a combinação de altas temperaturas da superfície terrestre com baixos ângulos zenitais solares. A utilização da equação de transferência radiativa mostrouse uma boa alternativa, desde que estejam disponíveis dados acerca da temperatura da superfíce terrestre assim como dos perfis atmosféricos. Entretanto, nas regiões onde ocorrem altos valores de temperatura da superfície terrestre e baixos ângulos zenitais solares, quaisquer dos dois métodos se mostra pouco utilizável, já que nesta região a estimativa da reflectância constitui um problema mal-posto. Em paralelo, utilizaram-se informações sobre aerossóis de queimada para efectuar simulações do MODTRAN que permitiram avaliar a reposta do canal do infravermelho-médio à este tipo de perturbação do sinal, muito comum na Amazónia Brasileira. A fim de tornar o estudo o mais realístico possível, procedeu-se à coleta de material resultante de queimadas na região Amazónica, mais especificamente em Alta Floresta, Mato Grosso, Brasil. Estes resultado foram então integrados nos estudos em questão, possibilitando a caracterização espectral das áreas ardidas. Com base nos resultados obtido definiu-se uma tranformação no espaço do infravermelho próximo e médio com o objetivo de maximizar a informação espectral de forma a que as superfícies vegetadas pudessem ser efectivamente discriminadas e as áreas ardidas identificadas. A tranformação baseia-se na diferença entre a reflectância nos infravermelhos próximo e médio, em conjunto com a distância a um ponto de convergência no espaço espectral dos infravermelhos próximo e médio, ponto esse representativo de uma área completamente ardida. A tranformação permitiu a definição de um novo sistema de coordenadas, o qual provou ser bastante útil no que diz respeito á identificação de áreas ardidas. Este novo espaço de coordenadas constitui uma inovação na área dos estudos de queimadas, já que permite ao mesmo tempo definir dois tipos de índices, o primeiro dos quais identifica superfícies que contém ou não biomassa e o segundo identifica, de entre as superfícies que contêm biomassa, a quantidade de água presente, podendo variar de vegetação verde (abundância de água) até áreas ardidas (ausência de água). Além de distiguir áreas ardidas, os índices desenvolvidos podem ainda ser aplicados em outros casos como, por exemplo, estudos de estresse hídrico e secas.DSA/INPE; Portuguese Foundation of Science and Technology (Fundação para a Ciência e Tecnologia / FCT)(SFRH/BD/21650/2005

Dust aerosols over India and adjacent continents retrieved using METEOSAT infrared radiance Part I: sources and regional distribution

Mineral dust constitutes the single largest contributor to continental aerosols. To accurately assess the impact of dust aerosols on climate, the spatial and temporal distribution of dust radiative properties is essential. Regional characteristics of dust radiative properties, however, are poorly understood. The magnitude and even sign of dust radiative forcing is uncertain, as it depends on a number of parameters, such as vertical distribution of dust, cloud cover and albedo of the underlying surface. In this paper, infrared radiance (10.5-12.5 µm), acquired from the METEOSAT-5 satellite ( resolution), was used to retrieve regional characteristics of dust aerosols for all of 1999. The infrared radiance depression, due to the presence of dust in the atmosphere, has been used as an index of dust load, known as the Infrared Difference Dust Index (IDDI). There have been several studies in the past carried out over the Sahara using IDDI as a measure of dust load. Over the Indian region, however, studies on dust aerosols are sparse. Spatial and temporal variability in dust loading and its regional distribution over various arid and semiarid regions of India and adjacent continents (0-35° N; 30° E-100° E) (excluding Sahara) have been studied and the results are examined along with surface soil conditions (such as vegetation cover and soil moisture). The advantage of the IDDI method is that information on aerosol properties, such as chemical composition or microphysical properties, is not needed. A large day-to-day variation in IDDI was observed over the entire study region, with values ranging from 4 to 22 K. It was observed that dust activity starts by March over the Indian deserts, as well as over deserts of the Africa and Arabian regions. The IDDI reaches maximum during the period of May to August. Regional maps of IDDI, in conjunction with biomass burning episodes (using TERRA satellite fire pixel counts), suggest that large IDDI values observed during the winter months over Northern India could be due to a possible deposition of black carbon on larger dust aerosols. The IDDI values have been compared with another year (i.e. 2003), with a large number of dust storms reported by meteorological departments based on visibility data. During the dry season, the magnitude of the monthly average IDDI during 2003 was slightly higher than that of 1999. The monthly mean IDDI was in the range from 4 to 9 K over the Indian deserts, as well as over the deserts of Africa and Arabia. The maximum IDDI during a month was in the range from 6 to 18 K. Large IDDI values were observed even over vegetated regions (such as the vegetated part of Africa and central India), attributed to the presence of transported dust from nearby deserts

Locating Amazonian Dark Earths (ADE) in the Brazilian Amazon using Satellite Imagery

Amazonian Dark Earths (ADE) are patches of archaeological soils scattered throughout the Amazon Basin. These soils are anthropogenic and most evidence suggests that they are the result of unintentional cultural deposits as well as intentional efforts of Amerindian populations to improve the quality of their farmlands. ADE are a mixture of charcoal, organic matter and the underlying Oxisol soil. ADE are extremely fertile soils in comparison to the surrounding Oxisols and they are sought after by local residents for agricultural purposes. In the first chapter I discuss the value and physical properties of ADE in detail. Research is being conducted to learn how ADE were created and to explore the possibility of replicating them to sequester carbon and to reclaim depleted soils in the Amazon Basin. This dissertation seeks to assist in that effort by attempting to map currently unknown ADE sites hidden beneath the dense tropical forest canopy

A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses

Abstract Cloud detection in optical remote sensing images is a crucial problem because undetected clouds can produce misleading results in the analyses of surface and atmospheric parameters. Sentinel-2 provides high spatial resolution satellite data distributed with associated cloud masks. In this paper, we evaluate the ability of Sentinel-2 Level-1C cloud mask products to discriminate clouds over a variety of biogeographic scenarios and in different cloudiness conditions. Reference cloud masks for the identification of misdetection were generated by applying a local thresholding method that analyses Sentinel-2 Band 2 (0.490 μm) and Band 10 (1.375 μm) separately; histogram-based thresholds were locally tuned by checking the single bands and the natural color composite (B4B3B2); in doubtful cases, NDVI and DEM were also analyzed to refine the masks; the B2B11B12 composite was used to separate snow. The analysis of the cloud classification errors obtained for our test sites allowed us to get important inferences of general value. The L1C cloud mask generally underestimated the presence of clouds (average Omission Error, OE, 37.4%); this error increased (OE > 50%) for imagery containing opaque clouds with a large transitional zone (between the cloud core and clear areas) and cirrus clouds, fragmentation emerged as a major source of omission errors (R2 0.73). Overestimation was prevalently found in the presence of holes inside the main cloud bodies. Two extreme environments were particularly critical for the L1C cloud mask product. Detection over Amazonian rainforests was highly inefficient (OE > 70%) due to the presence of complex cloudiness and high water vapor content. On the other hand, Alpine orography under dry atmosphere created false cirrus clouds. Altogether, cirrus detection was the most inefficient. According to our results, Sentinel-2 L1C users should take some simple precautions while waiting for ESA improved cloud detection products

Methodical basis for landscape structure analysis and monitoring: inclusion of ecotones and small landscape elements

Habitat variation is considered as an expression of biodiversity at landscape level in addition to genetic variation and species variation. Thus, effective methods for measuring habitat pattern at landscape level can be used to evaluate the status of biological conservation. However, the commonly used model (i.e. patch-corridor-matrix) for spatial pattern analysis has deficiencies. This model assumes discrete structures within the landscape without explicit consideration of “transitional zones” or “gradients” between patches. The transitional zones, often called “ecotones”, are dynamic and have a profound influence on adjacent ecosystems. Besides, this model takes landscape as a flat surface without consideration of the third spatial dimension (elevation). This will underestimate the patches’ size and perimeter as well as distances between patches especially in mountainous regions. Thus, the mosaic model needs to be adapted for more realistic and more precise representation of habitat pattern regarding to biodiversity assessment. Another part of information that has often been ignored is “small biotopes” inside patches (e.g. hedgerows, tree rows, copse, and scattered trees), which leads to within-patch heterogeneity being underestimated. The present work originates from the integration of the third spatial dimension in land-cover classification and landscape structure analysis. From the aspect of data processing, an integrated approach of Object-Based Image Analysis (OBIA) and Pixel-Based Image Analysis (PBIA) is developed and applied on multi-source data set (RapidEye images and Lidar data). At first, a general OBIA procedure is developed according to spectral object features based on RapidEye images for producing land-cover maps. Then, based on the classified maps, pixel-based algorithms are designed for detection of the small biotopes and ecotones using a Normalized Digital Surface Model (NDSM) which is derived from Lidar data. For describing habitat pattern under three-dimensional condition, several 3D-metrics (measuring e.g. landscape diversity, fragmentation/connectivity, and contrast) are proposed with spatial consideration of the ecological functions of small biotopes and ecotones. The proposed methodology is applied in two real-world examples in Germany and China. The results are twofold. First, it shows that the integrated approach of object-based and pixel-based image processing is effective for land-cover classification on different spatial scales. The overall classification accuracies of the main land-cover maps are 92 % in the German test site and 87 % in the Chinese test site. The developed Red Edge Vegetation Index (REVI) which is calculated from RapidEye images has been proved more efficient than the traditionally used Normalized Differenced Vegetation Index (NDVI) for vegetation classification, especially for the extraction of the forest mask. Using NDSM data, the third dimension is helpful for the identification of small biotopes and height gradient on forest boundary. The pixel-based algorithm so-called “buffering and shrinking” is developed for the detection of tree rows and ecotones on forest/field boundary. As a result the accuracy of detecting small biotopes is 80 % and four different types of ecotones are detected in the test site. Second, applications of 3D-metrics in two varied test sites show the frequently-used landscape diversity indices (i.e. Shannon’s diversity (SHDI) and Simpson’s diversity (SIDI)) are not sufficient for describing the habitats diversity, as they quantify only the habitats composition without consideration on habitats spatial distribution. The modified 3D-version of Effective Mesh Size (MESH) that takes ecotones into account leads to a realistic quantification of habitat fragmentation. In addition, two elevation-based contrast indices (i.e. Area-Weighted Edge Contrast (AWEC) and Total Edge Contrast Index (TECI)) are used as supplement to fragmentation metrics. Both ecotones and small biotopes are incorporated into the contrast metrics to take into account their edge effect in habitat pattern. This can be considered as a further step after fragmentation analysis with additional consideration of the edge permeability in the landscape structure analysis. Furthermore, a vector-based algorithm called “multi-buffer” approach is suggested for analyzing ecological networks based on land-cover maps. It considers small biotopes as stepping stones to establish connections between patches. Then, corresponding metrics (e.g. Effective Connected Mesh Size (ECMS)) are proposed based on the ecological networks. The network analysis shows the response of habitat connectivity to different dispersal distances in a simple way. Those connections through stepping stones act as ecological indicators of the “health” of the system, indicating the interpatch communications among habitats. In summary, it can be stated that habitat diversity is an essential level of biodiversity and methods for quantifying habitat pattern need to be improved and adapted to meet the demands for landscape monitoring and biodiversity conservation. The approaches presented in this work serve as possible methodical solution for fine-scale landscape structure analysis and function as “stepping stones” for further methodical developments to gain more insights into the habitat pattern.Die Lebensraumvielfalt ist neben der genetischen Vielfalt und der Artenvielfalt eine wesentliche Ebene der Biodiversität. Da diese Ebenen miteinander verknüpft sind, können Methoden zur Messung der Muster von Lebensräumen auf Landschaftsebene erfolgreich angewandt werden, um den Zustand der Biodiversität zu bewerten. Das zur räumlichen Musteranalyse auf Landschaftsebene häufig verwendete Patch-Korridor-Matrix-Modell weist allerdings einige Defizite auf. Dieses Modell geht von diskreten Strukturen in der Landschaft aus, ohne explizite Berücksichtigung von „Übergangszonen“ oder „Gradienten“ zwischen den einzelnen Landschaftselementen („Patches“). Diese Übergangszonen, welche auch als „Ökotone“ bezeichnet werden, sind dynamisch und haben einen starken Einfluss auf benachbarte Ökosysteme. Außerdem wird die Landschaft in diesem Modell als ebene Fläche ohne Berücksichtigung der dritten räumlichen Dimension (Höhe) betrachtet. Das führt dazu, dass die Flächengrößen und Umfänge der Patches sowie Distanzen zwischen den Patches besonders in reliefreichen Regionen unterschätzt werden. Daher muss das Patch-Korridor-Matrix-Modell für eine realistische und präzise Darstellung der Lebensraummuster für die Bewertung der biologischen Vielfalt angepasst werden. Ein weiterer Teil der Informationen, die häufig in Untersuchungen ignoriert werden, sind „Kleinbiotope“ innerhalb größerer Patches (z. B. Feldhecken, Baumreihen, Feldgehölze oder Einzelbäume). Dadurch wird die Heterogenität innerhalb von Patches unterschätzt. Die vorliegende Arbeit basiert auf der Integration der dritten räumlichen Dimension in die Landbedeckungsklassifikation und die Landschaftsstrukturanalyse. Mit Methoden der räumlichen Datenverarbeitung wurde ein integrierter Ansatz von objektbasierter Bildanalyse (OBIA) und pixelbasierter Bildanalyse (PBIA) entwickelt und auf einen Datensatz aus verschiedenen Quellen (RapidEye-Satellitenbilder und Lidar-Daten) angewendet. Dazu wird zunächst ein OBIA-Verfahren für die Ableitung von Hauptlandbedeckungsklassen entsprechend spektraler Objekteigenschaften basierend auf RapidEye-Bilddaten angewandt. Anschließend wurde basierend auf den klassifizierten Karten, ein pixelbasierter Algorithmus für die Erkennung von kleinen Biotopen und Ökotonen mit Hilfe eines normalisierten digitalen Oberflächenmodells (NDSM), welches das aus LIDAR-Daten abgeleitet wurde, entwickelt. Zur Beschreibung der dreidimensionalen Charakteristika der Lebensraummuster unter der räumlichen Betrachtung der ökologischen Funktionen von kleinen Biotopen und Ökotonen, werden mehrere 3D-Maße (z. B. Maße zur landschaftlichen Vielfalt, zur Fragmentierung bzw. Konnektivität und zum Kontrast) vorgeschlagen. Die vorgeschlagene Methodik wird an zwei realen Beispielen in Deutschland und China angewandt. Die Ergebnisse zeigen zweierlei. Erstens zeigt es sich, dass der integrierte Ansatz der objektbasierten und pixelbasierten Bildverarbeitung effektiv für die Landbedeckungsklassifikation auf unterschiedlichen räumlichen Skalen ist. Die Klassifikationsgüte insgesamt für die Hauptlandbedeckungstypen beträgt 92 % im deutschen und 87 % im chinesischen Testgebiet. Der eigens entwickelte Red Edge-Vegetationsindex (REVI), der sich aus RapidEye-Bilddaten berechnen lässt, erwies sich für die Vegetationsklassifizierung als effizienter verglichen mit dem traditionell verwendeten Normalized Differenced Vegetation Index (NDVI), insbesondere für die Gewinnung der Waldmaske. Im Rahmen der Verwendung von NDSM-Daten erwies sich die dritte Dimension als hilfreich für die Identifizierung von kleinen Biotopen und dem Höhengradienten, beispielsweise an der Wald/Feld-Grenze. Für den Nachweis von Baumreihen und Ökotonen an der Wald/Feld-Grenze wurde der sogenannte pixelbasierte Algorithmus „Pufferung und Schrumpfung“ entwickelt. Im Ergebnis konnten kleine Biotope mit einer Genauigkeit von 80 % und vier verschiedene Ökotontypen im Testgebiet detektiert werden. Zweitens zeigen die Ergebnisse der Anwendung der 3D-Maße in den zwei unterschiedlichen Testgebieten, dass die häufig genutzten Landschaftsstrukturmaße Shannon-Diversität (SHDI) und Simpson-Diversität (SIDI) nicht ausreichend für die Beschreibung der Lebensraumvielfalt sind. Sie quantifizieren lediglich die Zusammensetzung der Lebensräume, ohne Berücksichtigung der räumlichen Verteilung und Anordnung. Eine modifizierte 3D-Version der Effektiven Maschenweite (MESH), welche die Ökotone integriert, führt zu einer realistischen Quantifizierung der Fragmentierung von Lebensräumen. Darüber hinaus wurden zwei höhenbasierte Kontrastindizes, der flächengewichtete Kantenkontrast (AWEC) und der Gesamt-Kantenkontrast Index (TECI), als Ergänzung der Fragmentierungsmaße entwickelt. Sowohl Ökotone als auch Kleinbiotope wurden in den Berechnungen der Kontrastmaße integriert, um deren Randeffekte im Lebensraummuster zu berücksichtigen. Damit kann als ein weiterer Schritt nach der Fragmentierungsanalyse die Randdurchlässigkeit zusätzlich in die Landschaftsstrukturanalyse einbezogen werden. Außerdem wird ein vektorbasierter Algorithmus namens „Multi-Puffer“-Ansatz für die Analyse von ökologischen Netzwerken auf Basis von Landbedeckungskarten vorgeschlagen. Er berücksichtigt Kleinbiotope als Trittsteine, um Verbindungen zwischen Patches herzustellen. Weiterhin werden entsprechende Maße, z. B. die Effective Connected Mesh Size (ECMS), für die Analyse der ökologischen Netzwerke vorgeschlagen. Diese zeigen die Auswirkungen unterschiedlicher angenommener Ausbreitungsdistanzen von Organismen bei der Ableitung von Biotopverbundnetzen in einfacher Weise. Diese Verbindungen zwischen Lebensräumen über Trittsteine hinweg dienen als ökologische Indikatoren für den „gesunden Zustand“ des Systems und zeigen die gegenseitigen Verbindungen zwischen den Lebensräumen. Zusammenfassend kann gesagt werden, dass die Vielfalt der Lebensräume eine wesentliche Ebene der Biodiversität ist. Die Methoden zur Quantifizierung der Lebensraummuster müssen verbessert und angepasst werden, um den Anforderungen an ein Landschaftsmonitoring und die Erhaltung der biologischen Vielfalt gerecht zu werden. Die in dieser Arbeit vorgestellten Ansätze dienen als mögliche methodische Lösung für eine feinteilige Landschaftsstrukturanalyse und fungieren als ein „Trittsteine” auf dem Weg zu weiteren methodischen Entwicklungen für einen tieferen Einblick in die Muster von Lebensräumen