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are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments.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

Monitoring of forest cover change and modeling biophysical forest parameters in the Western Carpathians

Die Umweltveränderungen durch den Menschen sind auf unserer Erde allgegenwärtig. Entwaldung und Waldschädigung beeinflussen das System Erde entscheidend, denn Wälder bieten wichtige Ökosystemleistungen und sind Kernelement der Debatte um den Klimawandel, speziell hinsichtlich der globalen Kohlenstoffbilanz. Veränderungen der Waldbedeckung zu quantifizieren ist daher von herausragendem wissenschaftlichen Interesse. Ziel dieser Arbeit ist es, Waldbedeckungsveränderungen in den Westlichen Karpaten grenzübergreifend zu bestimmen, sowie Dynamiken der Biomasse von Nadelwäldern und deren Auswirkungen auf die oberirdische Kohlenstoffspeicherung abzuleiten. Die Karpatenwälder zeichnen sich durch ein hohes Maß an Biodiversität, einen großen Holzvorrat und als wichtiger Kohlenstoffspeicher für Europa aus. Jedoch sind diese Wälder auch geprägt von einer bewegten Geschichte der Landnutzung, hoher Luftverschmutzung und einer andauernden Waldabnahme. Mittels Methoden der Fernerkundung wurden Veränderungen in der Waldbedeckung für die Jahre 1985 bis 2010 abgeleitet. Die Ergebnisse zeigen, dass insbesondere das frühere Forstmanagement sowie die starke Luftverschmutzung zu Zeiten des Kommunismus gemeinsam die erhebliche Schädigung von Nadelwäldern bedingen. Fichtendominierte Bestände offenbaren dabei eine geringere Widerstandsfähigkeit gegenüber biotischen sowie abiotischen Belastungen, z.B. Schädlingen und Extremwettersituationen. Seit 2005 verwandelten sich die Nadelwälder 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 Schätzungen über die Veränderung des oberirdischen Kohlenstoffvorrates der Wälder 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 ΔAOT550 ≈ 0.04, corresponding approximately to uncertainty in surface albedo of Δρ ≈ 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 Atmosphärenkorrektur von Rapid‐Eye 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 Earth’s 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

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