Dr. Dirk Pflugmacher
Profil
Forschungsthemen1
Verbundvorhaben: Kartierung der Waldbrandgefahr mit fernerkundlichen und meteorologischen Daten; Teilvorhaben 2: Satellitengestützte Erfassung und Charakterisierung historischer und aktueller Waldbrände für die Modellierung der Waldbrandgefahr
Quelle ↗Förderer: Bundesministerium für Landwirtschaft, Ernährung und Heimat Zeitraum: 07/2020 - 12/2022 Projektleitung: Dr. Dirk Pflugmacher
Mögliche Industrie-Partner10
Stand: 26.4.2026, 19:48:44 (Top-K=20, Min-Cosine=0.4)
- 42 Treffer70.9%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon StocksP70.9%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
- 44 Treffer70.9%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon StocksP70.9%
- I-REDD+ - Impacts of Reducing Emissions from Deforestation and Forest Degradation and Enhancing Carbon Stocks
- 4 Treffer61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
- 5 Treffer61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
- 4 Treffer61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
C-O-M-B-I-N-E Arkitekter Ab
P6 Treffer61.1%- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
- 6 Treffer61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
- 5 Treffer61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
- 5 Treffer61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
- 13 Treffer61.1%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurgeP61.1%
- Integrated Urban Food Policies – Developing Sustainability Co-Benefits, Spatial Linkages, Social Inclusion and Sectoral Connections To Transform Food Systems in City-Regions (FoodCLIC)P52.3%
- Green Infrastructure and Urban Biodiversity for Sustainable Urban Development and the Green EconomySurge
Publikationen25
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
Frontiers in Ecology and the Environment · 379 Zitationen · DOI
When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote‐sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser‐scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long‐term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.
Remote Sensing of Environment · 367 Zitationen · DOI
Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data. Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and non-drought years. The maps show high spatial consistency and good delineation of field parcels. Combining optical, SAR, and environmental data increased overall accuracies by 6% to 10% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78% and 80%, and the mapped areas aligned well with agricultural statistics at the regional and national level. Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.
Nature Communications · 356 Zitationen · DOI
Mortality is a key indicator of forest health, and increasing mortality can serve as bellwether for the impacts of global change on forest ecosystems. Here we analyze trends in forest canopy mortality between 1984 and 2016 over more than 30 Mill. ha of temperate forests in Europe, based on a unique dataset of 24,000 visually interpreted spectral trajectories from the Landsat archive. On average, 0.79% of the forest area was affected by natural or human-induced mortality annually. Canopy mortality increased by +2.40% year<sup>-1</sup>, doubling the forest area affected by mortality since 1984. Areas experiencing low-severity mortality increased more strongly than areas affected by stand-replacing mortality events. Changes in climate and land-use are likely causes of large-scale forest mortality increase. Our findings reveal profound changes in recent forest dynamics with important implications for carbon storage and biodiversity conservation, highlighting the importance of improved monitoring of forest mortality.
Remote Sensing · 281 Zitationen · DOI
Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.
Remote Sensing of Environment · 249 Zitationen · DOI
Remote Sensing of Environment · 233 Zitationen · DOI
Remote Sensing of Environment · 232 Zitationen · DOI
Remote Sensing of Environment · 221 Zitationen · DOI
Remote Sensing of Environment · 214 Zitationen · DOI
Free and open access to satellite imagery and value-added data products have revolutionized the role of remote sensing in Earth system science. Nonetheless, rapid changes in the global environment pose challenges to the science community that are increasingly difficult to address using data from single satellite sensors or platforms due to the underlying limitations of data availability and tradeoffs that govern the design and implementation of currently existing sensors. Virtual constellations of planned and existing satellite sensors may help to overcome this limitation by combining existing observations to mitigate limitations of any one particular sensor. While multi-sensor applications are not new, the integration and harmonization of multi-sensor data is still challenging, requiring tremendous efforts of science and operational user communities. Defined by the Committee on Earth Observation Satellites (CEOS) as a “set of space and ground segment capabilities that operate in a coordinated manner to meet a combined and common set of Earth Observation requirements”, virtual constellations can principally be used to combine sensors with similar spatial, spectral, temporal, and radiometric characteristics. We extend this definition to also include sensors that are principally incompatible, because they are fundamentally different (for instance active versus passive remote sensing systems), but their combination is necessary and beneficial to achieve a specific monitoring goal. In this case, constellations are more likely to build upon the complementarity of resultant information products from these incompatible sensors rather than the raw physical measurements. In this communication, we explore the potential and possible limitations to be overcome regarding virtual constellations for terrestrial science applications, discuss potentials and limitations of various candidate sensors, and provide context on integration of sensors. Thematically, we focus on land-cover and land-use change (LCLUC), with emphasis given to medium spatial resolution (i.e., pixels sided 10 to 100 m) sensors, specifically as a complement to those onboard the Landsat series of satellites. We conclude that virtual constellations have the potential to notably improve observation capacity and thereby Earth science and monitoring programs in general. Various national and international parties have made notable and valuable progress related to virtual constellations. There is, however, inertia inherent to Earth observation programs, largely related to their complexity, as well as national interests, observation aims, and high system costs. Herein we define and describe virtual constellations, offer the science and applications information needs to offer context, provide the scientific support for a range of virtual constellation levels based upon applications readiness, capped by a discussion of issues and opportunities toward facilitating implementation of virtual constellations (in their various forms).
Remote Sensing of Environment · 179 Zitationen · DOI
Remote Sensing of Environment · 174 Zitationen · DOI
Remote Sensing of Environment · 171 Zitationen · DOI
MODIS land cover and LAI collection 4 product quality across nine sites in the western hemisphere
2006IEEE Transactions on Geoscience and Remote Sensing · 150 Zitationen · DOI
Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sup> study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed
Remote Sensing of Environment · 140 Zitationen · DOI
Global Environmental Change · 134 Zitationen · DOI
Remote Sensing of Environment · 134 Zitationen · DOI
Landscape and Urban Planning · 126 Zitationen · DOI
Remote Sensing · 120 Zitationen · DOI
We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia’s biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4% and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS.
Remote Sensing of Environment · 114 Zitationen · DOI
International Journal of Applied Earth Observation and Geoinformation · 111 Zitationen · DOI
Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation. In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.
Nature Plants · 110 Zitationen · DOI
Remote Sensing · 105 Zitationen · DOI
Detailed information from global remote sensing has greatly advanced ourunderstanding of Earth as a system in general and of agricultural processes in particular.Vegetation monitoring with global remote sensing systems over long time periods iscritical to gain a better understanding of processes related to agricultural change over longtime periods. This specifically relates to sub-humid to semi-arid ecosystems, whereagricultural change in grazing lands can only be detected based on long time series. Byintegrating data from different sensors it is theoretically possible to construct NDVI timeseries back to the early 1980s. However, such integration is hampered by uncertainties inthe comparability between different sensor products. To be able to rely on vegetationtrends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent acrossproducts. In this paper we analyzed several indicators of vegetation change for a range ofagricultural systems in Inner Mongolia, China, and compared the results across differentsatellite archives. Specifically, we compared two of the prime NDVI archives—AVHRR Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT)NDVI. Because a true accuracy assessment of long time series is not possible, we furthercompared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We foundhigh similarities in interannual trends, and also in trends of the seasonal amplitude andintegral between SPOT VGT and MODIS Terra (r > 0.9). However, we observedconsiderable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOTVGT. We detected similar discrepancies for trends based on phenological parameters, suchas amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles.Inconsistencies were partially related to land cover and vegetation density. Differentpre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introducedfurther uncertainties. Our results corroborate findings from other studies that vegetationtrends derived from AVHRR GIMMS data not always reflect true vegetation changes. Amore thorough understanding of the factors introducing uncertainties in AVHRR GIMMStime series is needed, and we caution against using AVHRR GIMMS data in regionalstudies without applying regional sensitivity analyses.
Remote Sensing of Environment · 104 Zitationen · DOI
ISPRS Journal of Photogrammetry and Remote Sensing · 96 Zitationen · DOI
Geografisk Tidsskrift-Danish Journal of Geography · 96 Zitationen · DOI
International climate negotiations have stressed the importance of considering emissions from forest degradation under the planned REDD+ (Reducing Emissions from Deforestation and forest Degradation + enhancing forest carbon stocks) mechanism. However, most research, pilot-REDD+ projects and carbon certification agencies have focused on deforestation and there appears to be a gap in knowledge on complex mosaic landscapes containing degraded forests, smallholder agriculture, agroforestry and plantations. In this paper we therefore review current research on how avoided forest degradation '… may affect emissions of greenhouse gases …' (GHG) and expected co-benefits in terms of biodiversity and livelihoods. There are still high uncertainties in measuring and monitoring emissions of carbon and other GHG from mosaic landscapes with forest degradation since most research has focused on binary analyses of forest vs. deforested land. Studies on the impacts of forest degradation on biodiversity contain mixed results and there is little empirical evidence on the influence of REDD+ on local livelihoods and tenure security, partly due to the lack of actual payment schemes. Governance structures are also more complex in landscapes with degraded forests as there are often multiple owners and types of rights to land and trees. Recent technological advances in remote sensing have improved estimation of carbon stock changes but establishment of historic reference levels is still challenged by the availability of sensor systems and ground measurements during the reference period. The inclusion of forest degradation in REDD+ calls for a range of new research efforts to enhance our knowledge of how to assess the impacts of avoided forest degradation. A first step will be to ensure that complex mosaic landscapes can be recognised under REDD+ on their own merits.
Kooperationen1
Bestätigte Forscher↔Partner-Paare aus HU-FIS — Gold-Standard-Positive für das Matching.
Verbundvorhaben: Kartierung der Waldbrandgefahr mit fernerkundlichen und meteorologischen Daten; Teilvorhaben 2: Satellitengestützte Erfassung und Charakterisierung historischer und aktueller Waldbrände für die Modellierung der Waldbrandgefahr
university
Stammdaten
Identität, Organisation und Kontakt aus HU-FIS.
- Name
- Dr. Dirk Pflugmacher
- Titel
- Dr.
- Fakultät
- Mathematisch-Naturwissenschaftliche Fakultät
- Institut
- Geographisches Institut
- Arbeitsgruppe
- Geofernerkundung
- Telefon
- +49 30 2093-9433
- HU-FIS-Profil
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- Zuletzt gescrapt
- 26.4.2026, 01:10:26