Angela Lausch

Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability

Abstract In most parts of the world, land-use/land cover can be considered an interface between natural conditions and anthropogenic influence. Indicators are being sought which reflect landscape conditions, pressures and related societal responses. Landscape metrics, which are based on the number, size, shape and arrangement of patches of different land-use/land cover types, are used-together with areal statistics-to quantify landscape structure and composition. The applicability of landscape metrics for landscape monitoring has been investigated in a 700 km 2 test region in eastern Germany, where open cast coal mining has caused far reaching land-use changes in the course of this century. Time series of maps (1912–2020) have been elaborated from various data sources (topographic maps, aerial photography, satellite images, prospective planning material). Landscape metrics have been calculated for the entire test region and for ecologically defined subregions at the landscape, class and patch level. The results are presented and methodological issues are addressed, namely the impact of scale, spatial and temporal resolution on the interpretability of landscape metrics. Critical issues are: • the application of remote sensing methods, which is a pre-requisite for the area-wide monitoring of land-use change; • standardised data processing techniques, which are vital for the spatial and temporal comparability of results; • the selection of a manageable set of indicators which embraces the structural properties of landscapes; • the choice of appropriate spatial units which allow for an integration of landscape indicators (which tend to relate to cross-border phenomena) and socio-economic indicators (which are usually available for administrative entities or areas). These issues are discussed in relation to the application of landscape indices in environmental monitoring.

Supplementing Land-Use Statistics with Landscape Metrics: Some Methodological Considerations

Landscape monitoring usually relies on land-use statistics whichreflect the share of land-sue/land cover types. In order tounderstand the functioning of landscapes, landscape pattern mustbe considered as well. Indicators which address the spatialconfiguration of landscapes are therefore needed. Thesuitability of landscape metrics, which are computed from thetype, geometry and arrangement of patches, is examined. Two casestudies in a surface mining region show that landscape metricscapture landscape structure but are highly dependent on the datamodel and on the methods of data analysis. For landscape metricsto become part of policy-relevant sets of environmentalindicators, standardised procedures for their computation fromremote sensing images must be developed.

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data.

The Bode hydrological observatory: a platform for integrated, interdisciplinary hydro-ecological research within the TERENO Harz/Central German Lowland Observatory

This article provides an overview about the Bode River catchment that was selected as the hydrological observatory and main region for hydro-ecological research within the TERrestrial ENvironmental Observatories Harz/Central German Lowland Observatory. It first provides information about the general characteristics of the catchment including climate, geology, soils, land use, water quality and aquatic ecology, followed by the description of the interdisciplinary research framework and the monitoring concept with the main components of the multi-scale and multi-temporal monitoring infrastructure. It also shows examples of interdisciplinary research projects aiming to advance the understanding of complex hydrological processes under natural and anthropogenic forcings and their interactions in a catchment context. The overview is complemented with research work conducted at a number of intensive research sites, each focusing on a particular functional zone or specific components and processes of the hydro-ecological system.

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.

Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods

The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use of the available information. In contrast, machine learning methods, such as RF, are supposed to be better suited to extract information on vegetation status. First, vegetation parameters are extracted from hyperspectral signatures simulated with the radiative transfer model, PROSAIL. Second, the transferability of these results with respect to laboratory and field measurements is investigated. In situ observations of plant physiological parameters and corresponding spectra are gathered in the laboratory for summer barley (Hordeum vulgare). Field in situ measurements focus on winter crops over several growing seasons. Chlorophyll content, Leaf Area Index and phenological growth stages are derived from simulated and measured spectra. RF performs very robustly and with a very high accuracy on PROSAIL

Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions

1. Helmholtz Center Potsdam, German Research Center for Geosciences, Telegrafenberg, Potsdam 14473, Germany; E-Mails: christian.mielke@gfz-potsdam.de (C.M.); daniel.scheffler@gfz-potsdam.de (D.S); nina.boesche@gfz-potsdam.de (N.B.); christin.lubitz@gfz-potsdam.de (C.L.); maximilian.brell@gfz-potsdam.de (M.B.); daniel.spengler@gfz-potsdam.de (D.S.); andreas.eisele@gfz-potsdam.de (A.E.); karl.segl@gfz-potsdam.de (K.S.); luis.guanter@gfz-potsdam.de (L.G.) 2. Helmholtz Center for Environmental Research-UFZ, Permoserstr 15, Leipzig 04318, Germany; E-Mail: angela.lausch@ufz.de

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA) can contribute to overcoming this challenge. Reference

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time- and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

In Situ/Remote Sensing Integration to Assess Forest Health—A Review

For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.