Natascha Oppelt

Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data

Information on the quantity and spatial distribution of canopy physiological and biochemical components is of importance for the study of nutrient cycles, productivity, vegetation stress and, more recently, in driving ecosystem models. In this context, remote sensing can play a unique and essential role because of its ability to acquire synoptic information at different time and space scales. This paper presents parts of a two-year field and laboratory study with the new airborne hyperspectral sensor, the Airborne Visible near Infrared Imaging Spectrometer (AVIS), over a test site in the Bavarian Alpine foothills, Germany (48° 8′ N, 11° 17′ E). The 80-band AVIS was developed at the Department for Earth and Environmental Sciences of the Ludwig-Maximilians-University Munich and records the 550–1000 nm spectral range. Using this system, 18 hyperspectral datasets were collected between April and September of 1999 and 2000. Weekly measurements of several plant parameters (height, biomass, leaf chlorophyll content, leaf nitrogen content) were carried out during these time periods on three (1999) and six (2000) fields of winter wheat, whereby two different cultivars were investigated in 2000. After system correction and calibration, the hyperspectral data were atmospherically corrected and calibrated to reflectance. The resulting spectra were analysed for their chemical compounds. The statistical analysis was carried out using the Chlorophyll Absorption Integral (CAI) in comparison to established indices: Optimized Soil-Adjusted Vegetation Index (OSAVI) and hyperspectral Normalized Difference Vegetation Index (hNDVI). Both the chlorophyll and nitrogen content of the leaves showed good correlations with CAI on a field mean basis. These results as well as two-dimensional information on these parameters are presented to provide information about the spatial heterogeneity within a field.

Remote Sensing in Mapping Mangrove Ecosystems — An Object-Based Approach

Over the past few decades, clearing for shrimp farming has caused severe losses of mangroves in the Mekong Delta (MD) of Vietnam. Although the increasing importance of shrimp aquaculture in Vietnam has brought significant financial benefits to the local communities, the rapid and largely uncontrolled increase in aquacultural area has contributed to a considerable loss of mangrove forests and to environmental degradation. Although different approaches have been used for mangrove classification, no approach to date has addressed the challenges of the special conditions that can be found in the aquaculture-mangrove system in the Ca Mau province of the MD. This paper presents an object-based classification approach for estimating the percentage of mangroves in mixed mangrove-aquaculture farming systems to assist the government to monitor the extent of the shrimp farming area. The method comprises multi-resolution segmentation and classification of SPOT5 data using a decision tree approach as well as local knowledge from the region of interest. The results show accuracies higher than 75% for certain classes at the object level. Furthermore, we successfully detect areas with mixed aquaculture-mangrove land cover with high accuracies. Based on these results, mangrove development, especially within shrimp farming-mangrove systems, can be monitored. However, the mangrove forest cover fraction per object is affected by image segmentation and thus does not always correspond to the real farm boundaries. It remains a serious challenge, then, to accurately map mangrove forest cover within mixed systems.

Airborne Visible / Infrared Imaging Spectrometer AVIS: Design, Characterization and Calibration.

The Airborne Visible / Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the systems second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented.

Water constituents and water depth retrieval from Sentinel-2A – a first evaluation in an oligotrophic lake

Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr−1). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (aCDOM(440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and aCDOM(440) were modelled in optically shallow water. In deep water, SPM and aCDOM(440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m−1) showed an underestimation of S2-A derived aCDOM(440) (mean: 0.14 m−1); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m−1 vs. 0.019 m−1). Chlorophyll-a concentrations (~1 mg·m−3) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A.

Hyperspectral classification approaches for intertidal macroalgae habitat mapping - a case study in Heligoland

Abstract. Analysis of coastal marine algae communities enables us to adequately estimate the state of coastal marine environments and provides evidence for environmental changes. Hyperspectral remote sensing provides a tool for mapping macroalgal habitats if the algal communities are spectrally resolvable. We compared the performance of three classification approaches to determine the distribution of macroalgae communities in the rocky intertidal zone of Heligoland, Germany, using airborne hyperspectral (AISAeagle) data. The classification results of two supervised approaches (maximum likelihood classifier and spectral angle mapping) are compared with an approach combining k-Means classification of derivative measures. We identified regions of different slopes between main pigment absorption features of macroalgae and classified the resulting slope bands. The maximum likelihood classifier gained the best results (Cohan’s kappa=0.81), but the new approach turned out as a time-effective possibility to identify the dominating macroalgae species with sufficient accuracy (Cohan’s kappa=0.77), even in the heterogeneous and patchy coverage of the study area.

The determination of mesoscale soil moisture patterns with ERS data

Mesoscale soil moisture maps can be determined quantitatively from coarse resolution radar backscatter data, which will be available in the near future from ENVISAT. The method requires the correction of the backscatter signal for the impact of forests, built-up areas, and water surfaces as well as a normalization of the backscatter to a reference crop and the correction of the plant water content. The required landuse data can be derived from AVHRR. Plant water content can be derived from observations, plant growth models, or estimated from NDVI.

Detecting land use and land cover changes in Northern German agricultural landscapes to assess ecosystem service dynamics.

Land use and land cover (LULC) and their changes in share and number of classes can be documented by remote sensing techniques. Information on LULC is needed for the assessment of ecosystem services and is used as input data for mapping and modelling. This information is important for decision-making and management of ecosystems and landscapes. In this study, LULC were analysed in two agricultural areas in Northern Germany by means of a pixel-based maximum likelihood classification approach of 11 Landsat TM 5 scenes between 1987 and 2011 followed by a post-classification refinement using the tool IRSeL. In this time period, grassland declined by about 50 % in both case study areas. This loss in grassland area can be associated with changes in provisioning ecosystem services as the supply of fodder and crops and the number of livestock declined from 1987 to 2007. Furthermore, an on-going increase in maize cultivation area, which is nowadays more and more used as biomass for biogas production, documents the addition of another provisioning service, i.e., biomass for energy. Combining remote sensing and research on ecosystem services supports the assessment and monitoring of ecosystem services on different temporal, spatial, and semantic scales.

Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

Tropical forest cover dynamics for Latin America using Earth observation data: a review covering the continental, regional, and local scale

The tropical forest cover has varied greatly over the last few decades. The rapid advance of agricultural crops and illegal clearings in natural areas has resulted in the conversion of the majority of the world’s forest into desolated patches. Although rates of deforestation have decreased compared to previous years, forest loss still remains a crucial concern. Latest studies conducted on a global scale identified the Latin American continent as one of the regions exhibiting the highest rates of deforestation in the world. The dynamics of forests over the past 40 years has attracted numerous remote-sensing-based studies to monitor forest loss, analyse patterns, and understand the drivers of land conversion. This review article provides a comprehensive overview of the remote-sensing-based studies of tropical forest dynamics in Latin America. Following an introduction with respect to global forest mapping products, a general outline of tropical forest ecoregions and drivers of deforestation in Latin America is provided. Subsequently, a review and categorization of the existing studies is presented, where focus is laid on selected sensors and data analysis methodologies apply. Furthermore, a case study for the whole of Paraguay is presented; Paraguay is a region which contains highly diverse ecosystems that have been ravaged as a result of deforestation over the past 40 years. The main results, challenges, and future needs are discussed.

Sensitivity analysis for predicting continuous fields of tree-cover and fractional land-cover distributions in cloud-prone areas

The use of multi-temporal datasets, such as vegetation index time series or phenological metrics, for improved classification and regression performance is well established in the remote-sensing science community. However, the usefulness of such information is less apparent for areas with distinct wet season periods and heavily concentrated cloud cover. In view of this, this study examines the potential of multi-temporal datasets for the estimation of sub-pixel land-cover fractions and percentage tree cover in an area having distinct wet and dry seasons. Prediction is based on a regression tree algorithm in combination with linear least-squares regression planes, which relate multi-spectral and multi-temporal satellite data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor to sub-pixel land-cover proportions and percentage tree cover, derived from high-resolution land-cover maps. Furthermore, several versions of the latter were produced using different classification approaches to evaluate the sensitivity of the response variable on overall prediction accuracy. The results were evaluated according to absolute accuracy levels and according to their long-term inter-annual robustness by applying the regression models to MODIS data over a period of 11 years. The best regression model based on dry season information only estimated continuous fields of percentage tree cover with a prediction error of less than 7% and an inter-annual variability of less than 4% over a time period of 11 years. The inclusion of intra-annual information did not contribute to any improvements in model accuracy compared to information from the dry season alone, and furthermore, deteriorated inter-annual robustness of model predictions. In addition, it has been shown that the quality of the response variable in the training data had significant effects on overall accuracy.