Frank Thonfeld

Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany)

Leaf Area Index (LAI) is an important variable for numerous processes in various disciplines of bio- and geosciences. In situ measurements are the most accurate source of LAI among the LAI measuring methods, but the in situ measurements have the limitation of being labor intensive and site specific. For spatial-explicit applications (from regional to continental scales), satellite remote sensing is a promising source for obtaining LAI with different spatial resolutions. However, satellite-derived LAI measurements using empirical models require calibration and validation with the in situ measurements. In this study, we attempted to validate a direct LAI retrieval method from remotely sensed images (RapidEye) with in situ LAI (LAIdestr). Remote sensing LAI (LAIrapideye) were derived using different vegetation indices, namely SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index). Additionally, applicability of the newly available red-edge band (RE) was also analyzed through Normalized Difference Red-Edge index (NDRE) and Soil Adjusted Red-Edge index (SARE). The LAIrapideye obtained from vegetation indices with red-edge band showed better correlation with LAIdestr (r = 0.88 and Root Mean Square Devation, RMSD = 1.01 & 0.92). This study also investigated the need to apply radiometric/atmospheric correction methods to the time-series of RapidEye Level 3A data prior to LAI estimation. Analysis of the the RapidEye Level 3A data set showed that application of the radiometric/atmospheric correction did not improve correlation of the estimated LAI with in situ LAI.

Recent Advances in Remote Sensing Change Detection – A Review

Change detection is a key methodology in remote sensing. Despite numerous important papers dedicated to this topic no comprehensive review of the subject currently exists. Most existing reviews are limited to certain fields of application or are simply no longer current. Here, we provide an overview of the most important algorithms used for change detection and their development and refinement through time. Change detection cannot be considered as a mere algorithm. We thus describe all of the steps necessary to perform change detection as part of a process chain. We also show how such a change detection process chain may be adapted to specific individual requirements. The labeling of changes is recognized as a basic part of this process. Three change labeling categories are introduced: (1) pre-change extraction labeling, (2) concurrent labeling, and (3) post-change extraction labeling. Methods developed specifically for use with synthetic aperture radar (SAR) data are a focus of this review. SAR data provide information compatible with data produced by optical sensors, but also often require specialized processing techniques and are useful within a unique field of application. An examination of time series analysis methods is also included in this review. To date, these techniques have not been considered in reviews, but the increasing availability of remote sensing data as well as recent advances in remote sensing change detection make it essential that they are included here. Although not exhaustive, this review is intended to provide a comprehensive overview of well established change detection methods as well as recent advances in this field.

Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

Image time series of high temporal and spatial resolution capture land surface dynamics of heterogeneous landscapes. We applied the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm to multi-spectral images covering two semi-arid heterogeneous rangeland study sites located in South Africa. MODIS 250 m resolution and RapidEye 5 m resolution images were fused to produce synthetic RapidEye images, from June 2011 to July 2012. We evaluated the performance of the algorithm by comparing predicted surface reflectance values to real RapidEye images. Our results show that ESTARFM predictions are accurate, with a coefficient of determination for the red band 0.80 < R2 < 0.92, and for the near-infrared band 0.83 < R2 < 0.93, a mean relative bias between 6% and 12% for the red band and 4% to 9% in the near-infrared band. Heterogeneous vegetation at sub-MODIS resolution is captured adequately: A comparison of NDVI time series derived from RapidEye and ESTARFM data shows that the characteristic phenological dynamics of different vegetation types are reproduced well. We conclude that the ESTARFM algorithm allows us to produce synthetic remote sensing images at high spatial combined with high temporal resolution and so provides valuable information on vegetation dynamics in semi-arid, heterogeneous rangeland landscapes.

Evaluating Crop Area Mapping from MODIS Time-Series as an Assessment Tool for Zimbabwe’s “Fast Track Land Reform Programme”

Moderate Resolution Imaging Spectroradiometer (MODIS) data forms the basis for numerous land use and land cover (LULC) mapping and analysis frameworks at regional scale. Compared to other satellite sensors, the spatial, temporal and spectral specifications of MODIS are considered as highly suitable for LULC classifications which support many different aspects of social, environmental and developmental research. The LULC mapping of this study was carried out in the context of the development of an evaluation approach for Zimbabwe’s land reform program. Within the discourse about the success of this program, a lack of spatially explicit methods to produce objective data, such as on the extent of agricultural area, is apparent. We therefore assessed the suitability of moderate spatial and high temporal resolution imagery and phenological parameters to retrieve regional figures about the extent of cropland area in former freehold tenure in a series of 13 years from 2001–2013. Time-series data was processed with TIMESAT and was stratified according to agro-ecological potential zoning of Zimbabwe. Random Forest (RF) classifications were used to produce annual binary crop/non crop maps which were evaluated with high spatial resolution data from other satellite sensors. We assessed the cropland products in former freehold tenure in terms of classification accuracy, inter-annual comparability and heterogeneity. Although general LULC patterns were depicted in classification results and an overall accuracy of over 80% was achieved, user accuracies for rainfed agriculture were limited to below 65%. We conclude that phenological analysis has to be treated with caution when rainfed agriculture and grassland in semi-humid tropical regions have to be separated based on MODIS spectral data and phenological parameters. Because classification results significantly underestimate redistributed commercial farmland in Zimbabwe, we argue that the method cannot be used to produce spatial information on land-use which could be linked to tenure change. Hence capabilities of moderate resolution data are limited to assess Zimbabwe’s land reform. To make use of the unquestionable potential of MODIS time-series analysis, we propose an analysis of plant productivity which allows to link annual growth and production of vegetation to ownership after Zimbabwe’s land reform.

Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data

Abstract The analysis of rapid land cover/land use changes by means of remote sensing is often based on data acquired under varying and occasionally unfavorable conditions. In addition, such analyses frequently use data acquired by different sensor systems. These acquisitions often differ with respect to sun position and sensor viewing geometry which lead to characteristic effects in each image. These differences may have a negative impact on reliable change detection. Here, we propose an approach called Robust Change Vector Analysis (RCVA), aiming to mitigate these effects. RCVA is an improvement of the widely-used Change Vector Analysis (CVA), developed to account for pixel neighborhood effects. We used a RapidEye and Kompsat-2 cross-sensor change detection test to demonstrate the efficiency of RCVA. Our analysis showed that RCVA results in fewer false negatives as well as false positives when compared to CVA under similar test conditions. We conclude that RCVA is a powerful technique which can be utilized to reduce spurious changes in bi-temporal change detection analyses based on high- or very-high spatial resolution imagery.

Automatic change detection in RapidEye data using the combined MAD and kernel MAF methods

The IR-MAD components show changes for a large part of the entire subset. Especially phenological changes in the agricultural fields surrounding the open pit are predominant. As opposed to this, kMAF components focus more on changes in the open-cast mine (and changes due to the two clouds and their shadows, not visible in the zoom). Ground data were available from bucket-wheel excavators on the extraction side (to the northwest in the open pit) in terms of elevation data for both dates. No ground data were available for changes due to backfill (southeastern part of the open pit) or changes due to mining machines other than the bucket-wheels.

SPOT-Based Sub-Field Level Monitoring of Vegetation Cover Dynamics: A Case of Irrigated Croplands

Acquiring multi-temporal spatial information on vegetation condition at scales appropriate for site-specific agricultural management is often complicated by the need for meticulous field measurements. Understanding spatial/temporal crop cover heterogeneity within irrigated croplands may support sustainable land use, specifically in areas affected by land degradation due to secondary soil salinization. This study demonstrates the use of multi-temporal, high spatial resolution (10 m) SPOT-4/5 image data in an integrated change vector analysis and spectral mixture analysis (CVA-SMA) procedure. This procedure was implemented with the principal objective of mapping sub-field vegetation cover dynamics in irrigated lowland areas within the lowerlands of the Amu Darya River. CVA intensity and direction were calculated separately for the periods of 1998–2006 and 2006–2010. Cumulative change intensity and the overall directional trend were also derived for the entire observation period of 1998–2010. Results show that most of the vector changes were observed between 1998 and 2006; persistent conditions were seen within the study region during the 2006–2010 period. A decreasing vegetation cover trend was identified within 38% of arable land. Areas of decreasing vegetation cover were located principally in the irrigation system periphery where deficient water supply and low soil quality lead to substandard crop development. During the 2006–2010 timeframe, degraded crop cover conditions persisted in 37% of arable land. Vegetation cover increased in 25% of the arable land where irrigation water supply was adequate. This high sub-field crop performance spatial heterogeneity clearly indicates that current land management practices are inefficient. Such information can provide the basis for implementing and adapting irrigation applications and salt leaching techniques to site-specific conditions and thereby make a significant contribution to sustainable regional land management.

Beyond trend analysis: How a modified breakpoint analysis enhances knowledge of agricultural production after Zimbabwe's fast track land reform

Abstract In the discourse on land reform assessments, a significant lack of spatial and time-series data has been identified, especially with respect to Zimbabwes “Fast-Track Land Reform Programme” (FTLRP). At the same time, interest persists among land use change scientists to evaluate causes of land use change and therefore to increase the explanatory power of remote sensing products. This study recognizes these demands and aims to provide input on both levels: Evaluating the potential of satellite remote sensing time-series to answer questions which evolved after intensive land redistribution efforts in Zimbabwe; and investigating how time-series analysis of Normalized Difference Vegetation Index (NDVI) can be enhanced to provide information on land reform induced land use change. To achieve this, two time-series methods are applied to MODIS NDVI data: Seasonal Trend Analysis (STA) and Breakpoint Analysis for Additive Season and Trend (BFAST). In our first analysis, a link of agricultural productivity trends to different land tenure regimes shows that regional clustering of trends is more dominant than a relationship between tenure and trend with a slightly negative slope for all regimes. We demonstrate that clusters of strong negative and positive productivity trends are results of changing irrigation patterns. To locate emerging and fallow irrigation schemes in semi-arid Zimbabwe, a new multi-method approach is developed which allows to map changes from bimodal seasonal phenological patterns to unimodal and vice versa. With an enhanced breakpoint analysis through the combination of STA and BFAST, we are able to provide a technique that can be applied on large scale to map status and development of highly productive cropping systems, which are key for food production, national export and local employment. We therefore conclude that the combination of existing and accessible time-series analysis methods: is able to achieve both: overcoming demonstrated limitations of MODIS based trend analysis and enhancing knowledge of Zimbabwes FTLRP.

Observing vegetation dynamics at medium spatial scale: Lessons from Africa and Asia

In this paper, we drew on experience from current Earth Observation (EO) research in Africa and Asia based on the 250m MODIS vegetation index time series covering large spatial extents. In the first example in the southern Africa, we derived a key set of phenometrics (overall greenness, peak and timing of annual greenness) and analyzed trends in these parameters. In the second example in the eastern Africa, we mapped human-induced vegetation productivity decline. The climate-induced changes in vegetation were largely disentangled from the anthropogenic-driven changes by using rainfall data from radar observations. In the last example, we revealed vegetation dynamic patterns across Central Asia by analyzing trends in overall greenness. The paper concluded on the possibilities and constraints of the available data and methods used to analyze vegetation dynamics at medium spatial resolution in relation to evolving issues in Africa and Asia.

Eine Zukunft, drei Perspektiven – räumlich explizite Prognose des Flächenverbrauchs für die Metropolregion Rheinland 2030

Zur Vorhersage zukunftiger Veranderungen der Landnutzung wurde eine Simulation unter drei unterschiedlichen Grundannahmen durchgefuhrt: A) nachhaltige Entwicklung, B) Business as usual, C) Wunsch nach Wohnen im Grunen. In allen drei Szenarien nimmt die Siedlungs- und Verkehrsflache zu, wobei in Szenario A vor allem bestehende Siedlungsraume verdichtet werden, wahrend in Szenario C vorrangig neue, „grune“ Flachen erschlossen werden.