83 research outputs found
Evaluating the remote sensing and inventory-based estimation of biomass in the western carpathians
Understanding the potential of forest ecosystems as global carbon sinks requires a thorough knowledge of forest carbon dynamics, including both sequestration and fluxes among multiple pools. The accurate quantification of biomass is important to better understand forest productivity and carbon cycling dynamics. Stand-based inventories (SBIs) are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments. Satellite remote sensing may provide a supplement or substitute. We tested the accuracy of aboveground biomass estimates modeled from a combination of Landsat Thematic Mapper (TM) imagery and topographic data, as well as SBI-derived variables in a Picea abies forest in the Western Carpathian Mountains. We employed Random Forests for non-parametric, regression tree-based modeling. Results indicated a difference in the importance of SBI-based and remote sensing-based predictors when estimating aboveground biomass. The most accurate models for biomass prediction ranged from a correlation coefficient of 0.52 for the TM- and topography-based model, to 0.98 for the inventory-based model. While Landsat-based biomass estimates were measurably less accurate than those derived from SBI, adding tree height or stand-volume as a field-based predictor to TM and topography-based models increased performance to 0.36 and 0.86, respectively. Our results illustrate the potential of spectral data to reveal spatial details in stand structure and ecological complexity. © 2011 by the authors
An Overview of Copernicus Sentinel-2 Surface Reflectance Products From an Analysis Ready Data Perspective
Characteristics and drivers of forest cover change in the post-socialist era in Croatia: evidence from a mixed-methods approach
© 2016, Springer-Verlag Berlin Heidelberg.Extensive forests in Croatia represent an important biological and economic resource in Europe. They are characterised by heterogeneity in forest management practices dating back to the socialist planned economy of the pre-1991 era. In this study we investigated the difference in rates of deforestation and reforestation in private- and state-owned forests during the post-socialist period and the causal drivers of change. The selected region of Northern Croatia is characterised by a high percentage of privately owned forests with minimal national monitoring and control. We used a mixed-methods approach which combines remote sensing, statistical modelling and a household-based questionnaire survey to assess the rates of forest cover change and factors influencing those changes. The results show that predominantly privately owned forests in Northern Croatia have recorded a net forest loss of 1.8 % during the 1991â2011 period, while Croatia overall is characterised by a 10 % forest cover increase in predominantly state-owned forests. Main factors influencing forest cover changes in private forests are slope, altitude, education structure, population age and population density. The results also show that the deforestation in private forests is weakening overall, mostly due to the continuation of the de-agrarisation and de-ruralisation processes which began during socialism
Drivers of grassland loss in Hungary during the post-socialist transformation (1987â1999)
The increase in the speed of land-cover change experienced worldwide is becoming a growing
concern. Major socio-economic transitions, such as the breakdown of socialism in Europe, may
lead to particularly high rates of landscape transformations. In this paper we examined the loss of semi-natural grasslands in Hungary between 1987 and 1999. We studied the relationship between 9
potential driving forces and the fate of grasslands using logistic GLMs.
Grassland loss was found to be very high (1.31 % per year), which is far higher than either before
or after this period. The most influential predictors of grassland loss were environmental and
landscape characteristics (soil type, area of remnant grassland patches), and the socio-economic context (distance to paved road, and nearest settlement, human population density). Several
processes and relationships can only be understood from a historical perspective (e.g. large extent
of afforestation, strong decrease of soil water table). Grassland loss during the study period
emerged as a consequence of survival strategies of individual farmers seeking adaptation to the
changing environmental and socio-economic conditions, and not urbanization and agricultural intensification which are the main underlying drivers for the ongoing landscape transformations in
most parts of the developed world.
Though globalization increasingly influences local land use decisions , reconstructing and
modelling recent landscape changes cannot be done without a proper understanding of local history
and culture. Our analysis shows the importance of large-area yet high resolution landscape change research, which may reveal unexpected patterns of land cover change, undetected at coarser scales
Monitoring of forest cover change and modeling biophysical forest parameters in the Western Carpathians
Die UmweltverĂ€nderungen durch den Menschen sind auf unserer Erde allgegenwĂ€rtig. Entwaldung und WaldschĂ€digung beeinflussen das System Erde entscheidend, denn WĂ€lder bieten wichtige Ăkosystemleistungen und sind Kernelement der Debatte um den Klimawandel, speziell hinsichtlich der globalen Kohlenstoffbilanz. VerĂ€nderungen der Waldbedeckung zu quantifizieren ist daher von herausragendem wissenschaftlichen Interesse. Ziel dieser Arbeit ist es, WaldbedeckungsverĂ€nderungen in den Westlichen Karpaten grenzĂŒbergreifend zu bestimmen, sowie Dynamiken der Biomasse von NadelwĂ€ldern und deren Auswirkungen auf die oberirdische Kohlenstoffspeicherung abzuleiten. Die KarpatenwĂ€lder zeichnen sich durch ein hohes MaĂ an BiodiversitĂ€t, einen groĂen Holzvorrat und als wichtiger Kohlenstoffspeicher fĂŒr Europa aus. Jedoch sind diese WĂ€lder auch geprĂ€gt von einer bewegten Geschichte der Landnutzung, hoher Luftverschmutzung und einer andauernden Waldabnahme. Mittels Methoden der Fernerkundung wurden VerĂ€nderungen in der Waldbedeckung fĂŒr die Jahre 1985 bis 2010 abgeleitet. Die Ergebnisse zeigen, dass insbesondere das frĂŒhere Forstmanagement sowie die starke Luftverschmutzung zu Zeiten des Kommunismus gemeinsam die erhebliche SchĂ€digung von NadelwĂ€ldern bedingen. Fichtendominierte BestĂ€nde offenbaren dabei eine geringere WiderstandsfĂ€higkeit gegenĂŒber biotischen sowie abiotischen Belastungen, z.B. SchĂ€dlingen und Extremwettersituationen. Seit 2005 verwandelten sich die NadelwĂ€lder infolge eines weit verbreiteten Biomasseverlustes von einer Netto-Kohlenstoffsenke in eine Netto-Kohlenstoffquelle. Die Analysen betonen den Einfluss bestimmter Standortfaktoren wie Waldtyp, vorherrschende Baumart, topographische Gegebenheiten, Brennpunkte der Umweltverschmutzung, Mikroklima und deren Interaktion auf die Waldabnahme. Die Arbeit legt eine komplexe sozio-ökologische Geschichte dar und erbringt SchĂ€tzungen ĂŒber die VerĂ€nderung des oberirdischen Kohlenstoffvorrates der WĂ€lder der Westlichen Karpaten.Human-induced environmental change is evident across the globe. Deforestation and forest degradation are among the most critical impacts of humanity on the Earth system, as forests provide crucial ecosystem services, and are a key element in the global climate change discussion, specifically considering the global carbon balance. Therefore, monitoring and quantifying forest changes are of prime scientific interest. The main goals of this thesis were to monitor forest change across country borders in the Western Carpathians, and to assess coniferous forest biomass dynamics and their impact on aboveground forest carbon storage. Generally, Carpathian forests provide outstanding biodiversity levels, high growing stocks, and an important European carbon sink. However, the Western Carpathian forests are exceptional, with a turbulent land-use history, high airborne pollution loads, and ongoing forest decline. Forest change between 1985 and 2010 was quantified using remote sensing techniques. Results show that the synergistic effect of unsustainable forest management in the past and high pollution levels during communist times significantly damaged coniferous forests. Spruce-dominated stands exhibit lower resistance against biotic and abiotic impacts, and are more susceptible to pests and extreme weather events. Widespread biomass loss since 2005 has converted coniferous forests from a net carbon sink into a net carbon source. Cross-border analysis emphasized the role of site characteristics such as forest type, predominant species, topographic conditions, pollution hotspots, microclimate, and their interactions for forest decline. Summarizing, this thesis tells a complex socio-ecological story and provides estimates of aboveground carbon stock changes in Western Carpathian forests
Validation of atmospheric correction algorithm ATCOR
Atmospheric correction of satellite images is necessary for many applications of remote sensing, i.e. computation of vegetation indices and biomass estimation. The largest uncertainty in atmospheric correction arises out of spatial and temporal variation of aerosol amount and type. Therefore validation of aerosol estimation is one important step in validation of atmospheric correction algorithms. Our ground-based measurements of aerosol-optical thickness spectra (AOT) were performed synchronously to overpasses of satellites Rapid-Eye and Landsat. Validation of aerosol retrieval by the widely used atmospheric correction tool ATCOR1,2 was then realized by comparison of AOT derived from satellite data with the ground-truths. Mean uncertainty is ÎAOT550 â 0.04, corresponding approximately to uncertainty in surface albedo of ÎÏ â 0.004. Generally, ATCOR-derived AOT values are mostly overestimated when compared to the ground-truth measurements. Very little differences are found between Rapid-Eye and Landsat sensors. Differences between using rural and maritime aerosols are negligible within the visible spectral range
Validierung der AtmosphĂ€renkorrektur von RapidâEye Daten mit ATCOR
Atmospheric correction of satellite data is necessary for
many applications of remote sensing;
ATCOR is widely used for atmospheric correction of
Rapid Eye data;
No uncertainty estimation of using ATCOR for
atmospheric correction of Rapid Eye dat
Comparative analysis of the atmospheric correction results for inter- and cross-sensor application in LUCC studies
Remote sensing enables continuous Earth observations and change detection. On the one hand satellite data archives provide great records for multi-temporal analysis, on the other an increasing number of new remote sensing datasets offer opportunities for multi-scale and cross-sensor applications. Atmospheric correction and radiometric normalization of satellite imagery is a prerequisite in order to achieve reliable and comparable results in land change studies.
Atmospheric correction reduces effects of scattering and absorption by gases and aerosols in the atmosphere between the Earthâs surface and the sensor, and minimizes the influence of solar illumination and topography on the registered signal. Well performing atmospheric correction algorithms should provide identical results for individual images acquired on the same date and over the same area. Moreover, pseudo-invariant features such as for example dark water, asphalt or sand should return the same spectral signature in corrected imagery from different acquisition dates.
This study presents a comparison of atmospheric correction results obtained from correcting Landsat TM and RapidEye imagery using the ATCOR software version 8.2.1. First, we tested intra-sensor differences between correction results for the along- and across-track overlap areas of two scenes. Subsequently, the cross-sensor variation of surface reflectance between Landsat and RapidEye imagery was investigated. Finally, we compared ground-truth measurements of aerosol optical thickness (AOT) obtained simultaneously to the satellite acquisitions with that ATCOR derives from the imagery.
Overall, our results indicate high consistency in reflectance within the overlap areas of the separately corrected scenes. The mean difference in reflectance for the overlap area of two successive scenes is less than 0.01 . The highest differences occurred in near-infrared spectral range.
Nevertheless, larger disparities above 0.04 were observed. Such differences in surface reflectance cause uncertainties in the retrieval of biophysical parameters (leaf area index, aboveground biomass, etc.) or spectral indices (e.g. the normalized differenced vegetation index) across image frames and thus hinder their application.
Regarding aerosol optical thickness, the ATCOR-based AOT values were mostly overestimated when compared to the ground-truth measurements. Differences in AOT in the overlap areas were up to 0.05. The inconsistency of AOT for the overlapping area as well as uncertainties in AOT retrieval confirm the limitations of atmospheric correction found for the reflectance retrieval.
To overcome retrieval limitations our results underline the need for relative radiometric normalization performed additionally to atmospheric correction of imagery and conducted prior to the atmospheric correction, particularly for intra- and cross-sensor data integration
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