Satellitengestützte Erfassung der Bodenversiegelung in Bayern. Broschüre des Bayerischen Landesamts für Umwelt

Sowohl im Freistaat Bayern als auch bundesweit ist seit Jahrzehnten ein stetiger Zuwachs der Siedlungs- und Verkehrsflächen zu verzeichnen. Mit dieser Entwicklung eng verknüpft – allerdings keineswegs gleichzusetzen – ist ein kontinuierlicher Anstieg der Bodenversiegelung. Die mit einer Umwidmung in Siedlungs- oder Verkehrsflächen einhergehende teilweise Abdichtung des Bodens führt dabei unweigerlich zu einem irreversiblen Verlust seiner bisherigen ökologischen, geschichtlichen und ertragsbezogenen Funktionen. Die Versiegelung verringert die natürliche Verdunstung und die Versickerung von Niederschlägen. Die Folgen sind unter anderem eine Verstärkung von Hochwasserereignissen und eine Verringerung der Grundwasserneubildungsrate. In den Städten führt die Versiegelung durch Aufheizung, Verringerung der Luftfeuchte und eine verstärkte Staubentwicklung zu einer negativen Veränderung des lokalen Klimas. Zur Verdeutlichung des unterschiedlichen Ausmaßes des Versiegelungsgrades in unseren Städten und Gemeinden illustriert Abbildung 1 jeweils das charakteristische Erscheinungsbild eines unversiegelten (A), eines teilversiegelten (B) und eines vollständig überbauten (C) Siedlungsareals. Vor dem Hintergrund der erwähnten Folgen sind die Quantifizierung der Bodenversiegelung sowie die Erfassung von deren zeitlicher Entwicklung unverzichtbar für eine differenzierte Diskussion über die Folgen einer kontinuierlichen Flächeninanspruchnahme. Aktuell steht jedoch keine Methodik beziehungsweise Datenbasis zur flächendeckenden und fortschreibbaren Erfassung der Bodenversiegelung zur Verfügung. Im Rahmen der Studie „Satellitengestützte Erfassung der Bodenversiegelung in Bayern“ wurde daher erstmals eine landesweit einheitliche und objektive Erhebung der Versiegelung mit hohem räumlichen Detaillierungsgrad durchgeführt. Grundlage war die Entwicklung einer Technik zur weitestgehend automatisierten, bayernweiten Kartierung der Flächenversiegelung auf Basis von Satellitenaufnahmen und Daten aus dem Amtlichen Topographisch-Kartographischen Informationssystem (ATKIS). Ausgehend von dieser Datengrundlage wurden für das Jahr 2000 der • Versiegelungsgrad der Siedlungs- und Verkehrsfläche sowie die • versiegelte Siedlungs- und Verkehrsfläche pro Einwohner für den Freistaat Bayern und seine administrativen und raumstrukturellen Teilräume (Gemeinden, Landkreise, Regierungsbezirke, Planungsregionen, Gebietskategorien des Landesentwicklungsprogramms Bayern) ermittelt

Analyzing the seasonal relations between in situ fpar / LAI of cotton and spectral information of RapidEye

Leaf Area Index (LAI) and the fraction of absorbed Photosynthetically Active Radiation (fPAR) are frequently used for biophysical modeling of crop growth and yield prediction. This study examines the calculation of LAI and fPAR of cotton using statistical regression with spectral information of RapidEye. Based on the knowledge that commonly used vegetation indices (NDVI, SAVI, EVI) may underperform in the situation of dense vegetation the growing season was divided into main growth and reproductive phases. To account for saturation effects indices including the curvature in the red edge part of the spectra were tested. Field measurements on LAI and fPAR were carried out during the vegetation period of 2011 on cotton fields in Uzbekistan. The LAI/fPAR results for RapidEye data will be used as input for an upscaling to TERRA-MODIS time series and transfer to larger areas of Central Asia

Assessing irrigated cropland dynamics in central Asia between 2001 and 2010 based on MODIS time series

Monitoring of vegetation dynamics in extensive irrigated croplands is essential for improving land and water management, especially to understand the reaction of the system to water scarcity and degradation processes. This study focuses on the assessment of irrigated cropland dynamics in the western part of the Aral Sea Basin in Central Asia during the past decade. Extend of cropland and spatio-temporal cropping patters are analyzed based on phenological profiles extracted from 16day MODIS vegetation index time series at a spatial resolution of 250m. Knowledge-based classifications which needed to be adjusted for every single year were applied to distinguish between cropland and other major land cover types, the desert or sparsely vegetated steppes, settled areas, and water bodies. Interannual variability of the time series in the maximum cropland extend recorded between 2001 and 2010 was assessed by using Pearson’s cross correlation (PCC) coefficient. Shifts of maximum one month (+/-) were tested and the highest PCC coefficient was selected. Accuracy assessment using a multi-annual MODIS classification conducted for a representative irrigation system between 2004 and 2007 returned acceptable results for the cropland mask (<90%). Comparing the inter-annual cropland dynamics revealed using PCC with both, the MODIS classifications 2004-2007 and pure pixels of aggregated ASTER based maps showed that the PCC only permits differentiation between different modalities in the time series, i.e. years of a varying number of intra-annual crop cycles. However, simply overlaying the cropland extends 2001-2010 already exhibits areas of unreliable water supply. In this light, integration of both, PCC analysis of MODIS time series and annual maps of the cropland extent can be concluded as valuable next steps for better understanding the dynamics of the irrigated cropland at regional scale not only in the Aral Sea Basin of Central Asia, but also in other arid environments, where irrigation agriculture is essential for rural income generation and food security.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

A Re‐examination of Perpendicular Drought Indices over Central and South‐West Asia

Drought monitoring models and products assist decision makers in drought planning, preparation, and mitigation, all of which can play a role in reducing drought impacts. In this study, the performance of two newly developed remote sensing-based drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional drought monitoring in agricultural regions located in central and south western Asia. The study area covers regions from moderate and wet climatological zones with dense vegetation coverage to semi-arid and arid climatological conditions with moderate to poor vegetation coverage. The spatio-temporal patterns of surface drought derived by PDI and MPDI from 250m MODerate Resolution Imaging Spectroradiometer (MODIS) data in 8-day time steps are compared against two other drought indices: the Standardized Precipitation Index (SPI) as a meteorological drought index and the potential evapotranspiration (ET0) as an agro-meteorological drought index, which both were calculated based on field-measured precipitation and regional meteorological parameters. In addition, 8-day MODIS Normalized Difference Vegetation Index (NDVI) was calculated and its performance to detect drought occurrence and measuring of drought severity compared with the two perpendicular drought indices. Significant correlations were found between the PDI, the MPDI and precipitation and other applied meteorological and agrometeorological drought indices. The results confirm previous studies which has been analyzing the PDI and the MPDI over some study points in Iran. In this research, however, implementation of higher resolution data (MOD09Q1) in both spatial (250 m) and temporal (8-days) dimensions revealed a greater agreement between the drought information extracted by the MPDI, PDI and field meteorological measurements. It could be concluded that the applied perpendicular indices could be used as a drought early warning system over case study region and other regions with similar arid and semi-arid climatological conditions

Calculating crop yields from space

Agriculture in the Khorezm region faces high production risks due to fluctuating irrigation water supply, uncertain effects of future climate change, land degradation, and management issues. New methods such as satellite-based remote sensing can support sustainable and improved land and water management in the region. In this ZUR, we present a satellite-based methodology to calculate Khorezm-wide crop yields at the field scale, which could be used to substantially improve the informational base for decision makers

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as user´s and producer´s accuracy