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Earth observation time series are well suited to monitoring global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product’s quality with respect to its use for modeling and the geoscientific community.
Stefan Mayr; Igor Klein; Martin Rutzinger; Claudia Kuenzer. Determining Temporal Uncertainty of a Global Inland Surface Water Time Series. Remote Sensing 2021, 13, 3454 .
AMA StyleStefan Mayr, Igor Klein, Martin Rutzinger, Claudia Kuenzer. Determining Temporal Uncertainty of a Global Inland Surface Water Time Series. Remote Sensing. 2021; 13 (17):3454.
Chicago/Turabian StyleStefan Mayr; Igor Klein; Martin Rutzinger; Claudia Kuenzer. 2021. "Determining Temporal Uncertainty of a Global Inland Surface Water Time Series." Remote Sensing 13, no. 17: 3454.
Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.
Stefan Mayr; Igor Klein; Martin Rutzinger; Claudia Kuenzer. Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series. Remote Sensing 2021, 13, 2675 .
AMA StyleStefan Mayr, Igor Klein, Martin Rutzinger, Claudia Kuenzer. Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series. Remote Sensing. 2021; 13 (14):2675.
Chicago/Turabian StyleStefan Mayr; Igor Klein; Martin Rutzinger; Claudia Kuenzer. 2021. "Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series." Remote Sensing 13, no. 14: 2675.
Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species—the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)—and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.
Igor Klein; Natascha Oppelt; Claudia Kuenzer. Application of Remote Sensing Data for Locust Research and Management—A Review. Insects 2021, 12, 233 .
AMA StyleIgor Klein, Natascha Oppelt, Claudia Kuenzer. Application of Remote Sensing Data for Locust Research and Management—A Review. Insects. 2021; 12 (3):233.
Chicago/Turabian StyleIgor Klein; Natascha Oppelt; Claudia Kuenzer. 2021. "Application of Remote Sensing Data for Locust Research and Management—A Review." Insects 12, no. 3: 233.
Remote sensing time series offer the possibility to monitor surface water at dense temporal intervals. Open data archives as well as developments in cloud computing are the main drivers towards and increased availability of raw data allowing for the extraction of detailed information on water bodies such as natural lakes and artificial reservoirs. At the same time, changes in precipitation patterns, increasing frequency and intensity of droughts, intensification of human water use, and regulatory upstream measurements affect water resources around the world today. With regard to water availability and supply-demand balance, artificial water reservoirs have become most important elements e.g. for hydropower, irrigated agriculture, flood control, as well as for domestic and industrial water use. Nevertheless, publicly accessible information on reservoirs is still not harmonized and available at global scale. Therefore, it is more essential than ever to acquire detailed knowledge about spatio-temporal variability of water resources - especially reservoirs - and the drivers of their dynamics. In this study, we analyze daily water extent time series of the 1267 largest reservoirs worldwide based on the existing DLR-DFD Global WaterPack product derived from MODIS data (Klein et al., 2017). The study aims to present an experimental way of spatio-temporal variability analysis by implementing the TIMESAT software which is usually used for vegetation analyses. In our experimental approach we derive information on the timing when the open surface water areas of reservoirs increase and decrease by identifying start date, end date and duration of such reservoir cycles as well as timing of maximum surface water extent (hydro-metrics). For four selected reservoirs, these hydro-metrics derived from surface water extent are compared with hydro-metrics derived from in-situ water level measurements or altimetry datasets and are discussed in more detail. Based on the presented examples we demonstrate the potential of high temporal resolution surface water extent data and spatio-temporal variability analyses with TIMESAT for future applications supporting the understanding of reservoir variability as a result of water management and hydroclimatic variability.
Igor Klein; Stefan Mayr; Ursula Gessner; Andreas Hirner; Claudia Kuenzer. Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability. Remote Sensing of Environment 2020, 253, 112207 .
AMA StyleIgor Klein, Stefan Mayr, Ursula Gessner, Andreas Hirner, Claudia Kuenzer. Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability. Remote Sensing of Environment. 2020; 253 ():112207.
Chicago/Turabian StyleIgor Klein; Stefan Mayr; Ursula Gessner; Andreas Hirner; Claudia Kuenzer. 2020. "Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability." Remote Sensing of Environment 253, no. : 112207.
The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed.
Ya-Lun Tsai; Igor Klein; Andreas Dietz; Natascha Oppelt. Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt. Remote Sensing 2020, 12, 3896 .
AMA StyleYa-Lun Tsai, Igor Klein, Andreas Dietz, Natascha Oppelt. Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt. Remote Sensing. 2020; 12 (23):3896.
Chicago/Turabian StyleYa-Lun Tsai; Igor Klein; Andreas Dietz; Natascha Oppelt. 2020. "Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt." Remote Sensing 12, no. 23: 3896.
In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China’s second largest freshwater lake in the middle reaches of the Yangtze River—the Dongting Lake—and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA’s Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake’s wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km2, indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km2. Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km2, which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.
Juliane Huth; Ursula Gessner; Igor Klein; Hervé Yesou; Xijun Lai; Natascha Oppelt; Claudia Kuenzer. Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China. Remote Sensing 2020, 12, 1761 .
AMA StyleJuliane Huth, Ursula Gessner, Igor Klein, Hervé Yesou, Xijun Lai, Natascha Oppelt, Claudia Kuenzer. Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China. Remote Sensing. 2020; 12 (11):1761.
Chicago/Turabian StyleJuliane Huth; Ursula Gessner; Igor Klein; Hervé Yesou; Xijun Lai; Natascha Oppelt; Claudia Kuenzer. 2020. "Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China." Remote Sensing 12, no. 11: 1761.
Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.
Stefan Mayr; Claudia Kuenzer; Ursula Gessner; Igor Klein; Martin Rutzinger. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products. Remote Sensing 2019, 11, 2616 .
AMA StyleStefan Mayr, Claudia Kuenzer, Ursula Gessner, Igor Klein, Martin Rutzinger. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products. Remote Sensing. 2019; 11 (22):2616.
Chicago/Turabian StyleStefan Mayr; Claudia Kuenzer; Ursula Gessner; Igor Klein; Martin Rutzinger. 2019. "Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products." Remote Sensing 11, no. 22: 2616.
Globally, the number of dams increased dramatically during the 20th century. As a result, monitoring water levels and storage volume of dam-reservoirs has become essential in order to understand water resource availability amid changing climate and drought patterns. Recent advancements in remote sensing data show great potential for studies pertaining to long-term monitoring of reservoir water volume variations. In this study, we used freely available remote sensing products to assess volume variations for Lake Mead, Lake Powell and reservoirs in California between 1984 and 2015. Additionally, we provided insights on reservoir water volume fluctuations and hydrological drought patterns in the region. We based our volumetric estimations on the area–elevation hypsometry relationship, by combining water areas from the Global Surface Water (GSW) monthly water history (MWH) product with corresponding water surface median elevation values from three different digital elevation models (DEM) into a regression analysis. Using Lake Mead and Lake Powell as our validation reservoirs, we calculated a volumetric time series for the GSWMWH–DEMmedian elevation combinations that showed a strong linear ‘area (WA) – elevation (WH)’ (R2 > 0.75) hypsometry. Based on ‘WA-WH’ linearity and correlation analysis between the estimated and in situ volumetric time series, the methodology was expanded to reservoirs in California. Our volumetric results detected four distinct periods of water volume declines: 1987–1992, 2000–2004, 2007–2009 and 2012–2015 for Lake Mead, Lake Powell and in 40 reservoirs in California. We also used multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) for San Joaquin drainage in California to assess regional links between the drought indicators and reservoir volume fluctuations. We found highest correlations between reservoir volume variations and the SPEI at medium time scales (12–18–24–36 months). Our work demonstrates the potential of processed, open source remote sensing products for reservoir water volume variations and provides insights on usability of these variations in hydrological drought monitoring. Furthermore, the spatial coverage and long-term temporal availability of our data presents an opportunity to transfer these methods for volumetric analyses on a global scale.
Tejas Bhagwat; Igor Klein; Juliane Huth; Patrick Leinenkugel. Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products. Remote Sensing 2019, 11, 1974 .
AMA StyleTejas Bhagwat, Igor Klein, Juliane Huth, Patrick Leinenkugel. Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products. Remote Sensing. 2019; 11 (17):1974.
Chicago/Turabian StyleTejas Bhagwat; Igor Klein; Juliane Huth; Patrick Leinenkugel. 2019. "Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products." Remote Sensing 11, no. 17: 1974.
Knowledge about inland surface water distribution and its short- to long-term variability is of high importance. Recently many studies have presented interesting results at regional, continental and global scale with 14.25 m to 25 km spatial and 10 day to one year temporal resolution. However, surface inland water bodies can feature temporally rapid spatial changes caused by extreme events, seasonal variability as well as human activity. Such changes can occur on temporal scales which are below the currently existing dynamic water body products. While the daily temporal resolution of available sensors has not been exploited yet. In this study we present an approach which uses the full temporal resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) to generate a 250 m daily global dataset of inland water bodies based on spectral information and dynamic thresholding. Based on a combination of MODIS Terra and Aqua daily classifications, auxiliary mask layers and temporal interpolation, a global cloud and gap free time series of inland water bodies is produced. The presented results are validated with 321 Landsat images across the globe. The executed validation shows an overall accuracy of 96.3% with 7.8% omission and 0.5% commission error, and a kappa coefficient of 93.3% for pure water pixels. The presented results demonstrate the high potential for different applications requiring information of inland water body dynamics at high temporal resolution. Examples demonstrate that e.g. the filling and emptying of water reservoirs, changes and inundation cycles of natural water bodies as well as freezing and thawing of lakes can be analyzed at a highly detailed temporal scal
Igor Klein; Ursula Gessner; Andreas J. Dietz; Claudia Kuenzer. Global WaterPack – A 250 m resolution dataset revealing the daily dynamics of global inland water bodies. Remote Sensing of Environment 2017, 198, 345 -362.
AMA StyleIgor Klein, Ursula Gessner, Andreas J. Dietz, Claudia Kuenzer. Global WaterPack – A 250 m resolution dataset revealing the daily dynamics of global inland water bodies. Remote Sensing of Environment. 2017; 198 ():345-362.
Chicago/Turabian StyleIgor Klein; Ursula Gessner; Andreas J. Dietz; Claudia Kuenzer. 2017. "Global WaterPack – A 250 m resolution dataset revealing the daily dynamics of global inland water bodies." Remote Sensing of Environment 198, no. : 345-362.
The assessment of water body dynamics is not only in itself a topic of strong demand, but the presence of water bodies is important information when it comes to the derivation of products such as land surface temperature, leaf area index, or snow/ice cover mapping from satellite data. For the TIMELINE project, which aims to derive such products for a long time series of Advanced Very High Resolution Radiometer (AVHRR) data for Europe, precise water masks are therefore not only an important stand-alone product themselves, they are also an essential interstage information layer, which has to be produced automatically after preprocessing of the raw satellite data. The respective orbit segments from AVHRR are usually more than 2000 km wide and several thousand km long, thus leading to fundamentally different observation geometries, including varying sea surface temperatures, wave patterns, and sediment and algae loads. The water detection algorithm has to be able to manage these conditions based on a limited amount of spectral channels and bandwidths. After reviewing and testing already available methods for water body detection, we concluded that they cannot fully overcome the existing challenges and limitations. Therefore an extended approach was implemented, which takes into account the variations of the reflectance properties of water surfaces on a local to regional scale; the dynamic local threshold determination will train itself automatically by extracting a coarse-scale classification threshold, which is refined successively while analyzing subsets of the orbit segment. The threshold is then interpolated by fitting a minimum curvature surface before additional steps also relying on the brightness temperature are included to reduce possible misclassifications. The classification results have been validated using Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and proven an overall accuracy of 93.4%, with the majority of errors being connected to flawed geolocation accuracy of the AVHRR data. The presented approach enables the derivation of long-term water body time series from AVHRR data and is the basis for applied geoscientific studies on large-scale water body dynamics.
Andreas J. Dietz; Igor Klein; Ursula Gessner; Corinne M. Frey; Claudia Kuenzer; Stefan Dech. Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor. Remote Sensing 2017, 9, 57 .
AMA StyleAndreas J. Dietz, Igor Klein, Ursula Gessner, Corinne M. Frey, Claudia Kuenzer, Stefan Dech. Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor. Remote Sensing. 2017; 9 (1):57.
Chicago/Turabian StyleAndreas J. Dietz; Igor Klein; Ursula Gessner; Corinne M. Frey; Claudia Kuenzer; Stefan Dech. 2017. "Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor." Remote Sensing 9, no. 1: 57.
Biomass is a sensitive indicator of environmental change and ecological functioning. Quantification of biomass is essential to identify and monitor those areas threatened by degradation and desertification. This is especially important in arid and semi-arid environments. However, robust techniques to monitor carbon stocks over large areas and through time are still missing. The major objective of the presented study is to develop a novel approach for biomass estimation in semi-arid environments using remote-sensing based Net Primary Productivity (NPP) data. The developed methodical concept aims at derivation of above-ground grass and shrub biomass for natural environments. It is based on NPP time-series and plants’ relative growth rates. Fractional cover data provide information about grass and shrub coverage. The developed approach has been applied to three study areas in Kazakhstan, in which field data were collected for validation. Biomass maps were derived that show the spatial distribution of grass and shrub biomass. Validation revealed a moderate correlation (R = 0.68) with field data for grass biomass. For shrub biomass, a high correlation (R = 0.83) is retrieved when fractional cover information from field observations is used. The presented novel approach for biomass estimation is based on remote sensing derived NPP time-series and is thus potentially transferable in space and time. This is a great advantage compared to commonly applied empirical relationships. The presented concept can be adapted to be applied to other vegetation communities. Providing the necessary data about fractional vegetation cover is available, the method will allow for repeated and large-area biomass estimation for natural semi-arid environments as needed for observing changes in biomass and support sustainable land management.
Christina Eisfelder; Igor Klein; Aruzhan Bekkuliyeva; Claudia Kuenzer; Manfred F. Buchroithner; Stefan Dech. Above-ground biomass estimation based on NPP time-series − A novel approach for biomass estimation in semi-arid Kazakhstan. Ecological Indicators 2017, 72, 13 -22.
AMA StyleChristina Eisfelder, Igor Klein, Aruzhan Bekkuliyeva, Claudia Kuenzer, Manfred F. Buchroithner, Stefan Dech. Above-ground biomass estimation based on NPP time-series − A novel approach for biomass estimation in semi-arid Kazakhstan. Ecological Indicators. 2017; 72 ():13-22.
Chicago/Turabian StyleChristina Eisfelder; Igor Klein; Aruzhan Bekkuliyeva; Claudia Kuenzer; Manfred F. Buchroithner; Stefan Dech. 2017. "Above-ground biomass estimation based on NPP time-series − A novel approach for biomass estimation in semi-arid Kazakhstan." Ecological Indicators 72, no. : 13-22.
Information on the spatio-temporal dynamics of inland water bodies is of high value for many applications, for example in the context of water and land management or for ecosystem service assessments. In this study, different approaches to delineate inland water bodies from MODIS 250 m time series were compared. Here, the performance of different input bands and indices, of trainings pixel selection methods, and of dynamic threshold definition approaches were assessed with the goal to find an optimized approach applicable for global inland water body detection based on moderate spatial and high temporal resolution MODIS data. The results of the tested approaches were cross validated with high resolution Landsat-8 classifications. The results show amongst others that a combination of near infrared band (NIR) and difference index (NIR - red band) performed best for most of the globally distributed test regions and that single band approaches revealed higher commission errors.
Igor Klein; Ursula Gessner; Andreas Dietz; Patrick Leinenkugel; Stefan Dech; Claudia Kuenzer. Detection of inland water bodies with high temporal resolution - assessing dynamic threshold approaches. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 7647 -7650.
AMA StyleIgor Klein, Ursula Gessner, Andreas Dietz, Patrick Leinenkugel, Stefan Dech, Claudia Kuenzer. Detection of inland water bodies with high temporal resolution - assessing dynamic threshold approaches. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():7647-7650.
Chicago/Turabian StyleIgor Klein; Ursula Gessner; Andreas Dietz; Patrick Leinenkugel; Stefan Dech; Claudia Kuenzer. 2016. "Detection of inland water bodies with high temporal resolution - assessing dynamic threshold approaches." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 7647-7650.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.
François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3 .
AMA StyleFrançois Waldner, Steffen Fritz, Antonio Di Gregorio, Dmitry Plotnikov, Sergey Bartalev, Nataliia Kussul, Peng Gong, Prasad Thenkabail, Gerard Hazeu, Igor Klein, Fabian Löw, Jukka Miettinen, Vinay Kumar Dadhwal, Céline Lamarche, Sophie Bontemps, Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data. 2016; 1 (1):3.
Chicago/Turabian StyleFrançois Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. 2016. "A Unified Cropland Layer at 250 m for Global Agriculture Monitoring." Data 1, no. 1: 3.
River deltas belong to the most densely settled places on earth. Although they only account for 5% of the global land surface, over 550 million people live in deltas. These preferred livelihood locations, which feature flat terrain, fertile alluvial soils, access to fluvial and marine resources, a rich wetland biodiversity and other advantages are, however, threatened by numerous internal and external processes. Socio-economic development, urbanization, climate change induced sea level rise, as well as flood pulse changes due to upstream water diversion all lead to changes in these highly dynamic systems. A thorough understanding of a river delta’s general setting and intra-annual as well as long-term dynamic is therefore crucial for an informed management of natural resources. Here, remote sensing can play a key role in analyzing and monitoring these vast areas at a global scale. The goal of this study is to demonstrate the potential of intra-annual time series analyses at dense temporal, but coarse spatial resolution for inundation characterization in five river deltas located in four different countries. Based on 250 m MODIS reflectance data we analyze inundation dynamics in four densely populated Asian river deltas—namely the Yellow River Delta (China), the Mekong Delta (Vietnam), the Irrawaddy Delta (Myanmar), and the Ganges-Brahmaputra (Bangladesh, India)—as well as one very contrasting delta: the nearly uninhabited polar Mackenzie Delta Region in northwestern Canada for the complete time span of one year (2013). A complex processing chain of water surface derivation on a daily basis allows the generation of intra-annual time series, which indicate inundation duration in each of the deltas. Our analyses depict distinct inundation patterns within each of the deltas, which can be attributed to processes such as overland flooding, irrigation agriculture, aquaculture, or snowmelt and thermokarst processes. Clear differences between mid-latitude, subtropical, and polar deltas are illustrated, and the advantages and limitations of the approach for inundation derivation are discussed.
Claudia Kuenzer; Igor Klein; Tobias Ullmann; Efi Foufoula Georgiou; Roland Baumhauer; Stefan Dech. Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series. Remote Sensing 2015, 7, 8516 -8542.
AMA StyleClaudia Kuenzer, Igor Klein, Tobias Ullmann, Efi Foufoula Georgiou, Roland Baumhauer, Stefan Dech. Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series. Remote Sensing. 2015; 7 (7):8516-8542.
Chicago/Turabian StyleClaudia Kuenzer; Igor Klein; Tobias Ullmann; Efi Foufoula Georgiou; Roland Baumhauer; Stefan Dech. 2015. "Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series." Remote Sensing 7, no. 7: 8516-8542.
The understanding and assessment of surface water variability of inland water bodies, for example, due to climate variability and human impact, requires steady and continuous information about its inter- and intra-annual dynamics. In this letter, we present an approach using dynamic threshold techniques and utilizing time series to generate a data set containing detected surface water bodies on a global scale with daily temporal resolution. Exemplary results for the year 2013 that were based on moderate resolution imaging spectroradiometer products are presented in this letter. The main input data sets for the presented product were MOD09GQ/MYD09GQ and MOD10A1/MYD10A1 with a spatial resolution of 250 m and 500 m, respectively. Using the static water mask MOD44W, we extracted training pixels to generate dynamic thresholds for individual data sets on daily basis. In a second processing step, the generated sequences of water masks were utilized to interpolate the results for any missing observations, either due to cloud coverage or missing data, as well as to reduce misclassification due to cloud shadow. The product provides an opportunity for further research and for assessing the drivers of changes of inland water bodies at a global scale.
Igor Klein; Andreas Dietz; Ursula Gessner; Stefan Dech; Claudia Kuenzer. Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis. Remote Sensing Letters 2015, 6, 78 -87.
AMA StyleIgor Klein, Andreas Dietz, Ursula Gessner, Stefan Dech, Claudia Kuenzer. Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis. Remote Sensing Letters. 2015; 6 (1):78-87.
Chicago/Turabian StyleIgor Klein; Andreas Dietz; Ursula Gessner; Stefan Dech; Claudia Kuenzer. 2015. "Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis." Remote Sensing Letters 6, no. 1: 78-87.
The knowledge and understanding of intra- and inter annual characteristics of inland water bodies, such as natural lakes and artificial reservoirs are crucial for many reasons. Inland water bodies are sensitive to environmental variations and human impact which is reflected in spatial and temporal dynamics of surface extent. A time-series of areal surface extent of lakes and reservoirs might be a helpful dataset to understand the complex system and the spatio-temporal patterns of natural lakes and artificial reservoirs. In this study, we describe an approach to detect water bodies based on dynamical thresholding on daily basis and utilizing high frequency observations. Daily MODIS (Moderate Resolution Imaging Spectrometer) products were used to generate water masks for the year 2013 on global scale. The results indicate that time series of water bodies’ extent are important especially for those inland water bodies which are dominated by temporal changes and fluctuation through the year. In combination with ancillary data, our understanding of environmental and human interaction and the reaction of water bodies will be improved. Such information is critical to support sustainable water management, as well as for climate change discussion since many inland water bodies are sensitive to short- and long term environmental alterations.
Igor Klein; Andreas J. Dietz; Ursula Gessner; Claudia Kuenzer. Global WaterPack: Intra-annual Assessment of Spatio-Temporal Variability of Inland Water Bodies. Remote Sensing and Digital Image Processing 2015, 99 -117.
AMA StyleIgor Klein, Andreas J. Dietz, Ursula Gessner, Claudia Kuenzer. Global WaterPack: Intra-annual Assessment of Spatio-Temporal Variability of Inland Water Bodies. Remote Sensing and Digital Image Processing. 2015; ():99-117.
Chicago/Turabian StyleIgor Klein; Andreas J. Dietz; Ursula Gessner; Claudia Kuenzer. 2015. "Global WaterPack: Intra-annual Assessment of Spatio-Temporal Variability of Inland Water Bodies." Remote Sensing and Digital Image Processing , no. : 99-117.
Arid and semiarid environments are susceptible to environmental degradation and desertification. Modelling net primary productivity (NPP) and analysis of spatio-temporal patterns help to understand ecological functioning especially in these areas. In this study, we apply the Biosphere Energy Transfer Hydrology Model (BETHY/DLR) to derive NPP for Kazakhstan for 2003–2011. Results are analyzed regarding spatial, monthly, and inter-annual variations. Mean annual NPP for Kazakhstan is 143 g C m−2 and maximum productivity is reached in June. Most monthly NPP anomalies occur in semiarid North of Kazakhstan. These regions seem to be most strongly affected by changes in meteorology and are likely to be vulnerable to changing climate. Arid ecosystems show lower inter-annual NPP variability than semiarid lands. Correlations between NPP and meteorological parameters reveal variable influence of temperature, PAR, and precipitation on vegetation productivity during the year. Reaction of vegetation growth to precipitation is delayed 1–2 months. Temperature is most critical in spring and precipitation in summer affects NPP in August–October. The results presented in this study help to identify regions that are vulnerable to global change. They allow predictions on possible effects of expected future climate change on vegetation productivity in arid and semiarid Kazakhstan and support sustainable land management
Christina Eisfelder; Igor Klein; Markus Niklaus; Claudia Kuenzer. Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables. Journal of Arid Environments 2014, 103, 17 -30.
AMA StyleChristina Eisfelder, Igor Klein, Markus Niklaus, Claudia Kuenzer. Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables. Journal of Arid Environments. 2014; 103 ():17-30.
Chicago/Turabian StyleChristina Eisfelder; Igor Klein; Markus Niklaus; Claudia Kuenzer. 2014. "Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables." Journal of Arid Environments 103, no. : 17-30.
Based on NOAA/AVHRR NDVI biweekly time-series data, the start and end of the growing season of Central Asia from 1982 to 2006 were estimated. Trend analysis results indicate an earlier green-up and a later dormancy over the entire area during the study period. For seven main vegetation types, the largest advance of greenup onset (0.597 days/year) occurs in cropland and the longest delay of dormancy (1.109 days/year) in open shrubland. The smallest advance (0.164 days/year) occurs in evergreen needleleaf forest and delay (0.443 days/year) in crop/natural vegetation mosaic. These results imply enhanced vegetation activity in the Central Asia region over last decades.
L Lu; H Guo; C Kuenzer; Igor Klein; L Zhang; X Li. Analyzing phenological changes with remote sensing data in Central Asia. IOP Conference Series: Earth and Environmental Science 2014, 17, 012005 .
AMA StyleL Lu, H Guo, C Kuenzer, Igor Klein, L Zhang, X Li. Analyzing phenological changes with remote sensing data in Central Asia. IOP Conference Series: Earth and Environmental Science. 2014; 17 (1):012005.
Chicago/Turabian StyleL Lu; H Guo; C Kuenzer; Igor Klein; L Zhang; X Li. 2014. "Analyzing phenological changes with remote sensing data in Central Asia." IOP Conference Series: Earth and Environmental Science 17, no. 1: 012005.
Monitoring of net primary productivity (NPP) is especially important for the fragile ecosystems in arid and semi-arid regions. Great interest exists in observing large-scale vegetation dynamics and understanding spatial and temporal patterns of NPP in these areas. In this study we present results of NPP obtained with the model BETHY/DLR for Kazakhstan for 2003–2011 and its spatial and temporal dynamics. The spatial distribution of vegetation productivity shows a gradient from North to South and clear differences between individual vegetation classes. The monthly NPP values show the highest productivity in June. Differences between rain-fed and irrigated areas indicate the dependency on water availability. Annual NPP variability was high for agricultural areas, but showed low values for natural vegetation. The analysis of different patterns in vegetation productivity provides valuable information for the identification of regions that are vulnerable to a possible climate change. This information may thus substantially support a sustainable land management.
C Eisfelder; Igor Klein; J Huth; M Niklaus; C Kuenzer. Spatio-temporal patterns and dynamics of net primary productivity for Kazakhstan. IOP Conference Series: Earth and Environmental Science 2014, 17, 012028 .
AMA StyleC Eisfelder, Igor Klein, J Huth, M Niklaus, C Kuenzer. Spatio-temporal patterns and dynamics of net primary productivity for Kazakhstan. IOP Conference Series: Earth and Environmental Science. 2014; 17 (1):012028.
Chicago/Turabian StyleC Eisfelder; Igor Klein; J Huth; M Niklaus; C Kuenzer. 2014. "Spatio-temporal patterns and dynamics of net primary productivity for Kazakhstan." IOP Conference Series: Earth and Environmental Science 17, no. 1: 012028.
In this study medium resolution remote sensing data of the AVHRR and MODIS sensors were used for derivation of inland water bodies extents over a period from 1986 till 2012 for the region of Central Asia. Daily near-infrared (NIR) spectra from the AVHRR sensor with 1.1 km spatial resolution and 8-day NIR composites from the MODIS sensor with 250 m spatial resolution for the months April, July and September were used as input data. The methodological approach uses temporal dynamic thresholds for individual data sets, which allows detection of water pixel independent from differing conditions or sensor differences. The individual results are summed up and combined to monthly composites of areal extent of water bodies. The presented water masks for the months April, July, and September were chosen to detect seasonal patterns as well as inter-annual dynamics and show diverse behaviour of static, decreasing, or dynamic water bodies in the study region. The size of the Southern Aral Sea, as the most popular example for an ecologic catastrophe, is decreasing significantly throughout all seasons (R2 0.96 for April; 0.97 for July; 0.96 for September). Same is true for shallow natural lakes in the northern Kazakhstan, exemplary the Tengiz-Korgalzhyn lake system, which have been shrinking in the last two decades due to drier conditions (R2 0.91 for July; 0.90 for September). On the contrary, water reservoirs show high seasonality and are very dynamic within one year in their areal extent with maximum before growing season and minimum after growing season. Furthermore, there are water bodies such as Alakol-Sasykol lake system and natural mountainous lakes which have been stable in their areal extent throughout the entire time period. Validation was performed based on several Landsat images with 30 m resolution and reveals an overall accuracy of 83% for AVHRR and 91% for MODIS monthly water masks. The results should assist for climatological and ecological studies, land and water management, and as input data for different modelling applications
Igor Klein; Andreas J. Dietz; Ursula Gessner; Anastassiya Galayeva; Akhan Myrzakhmetov; Claudia Kuenzer. Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data. International Journal of Applied Earth Observation and Geoinformation 2014, 26, 335 -349.
AMA StyleIgor Klein, Andreas J. Dietz, Ursula Gessner, Anastassiya Galayeva, Akhan Myrzakhmetov, Claudia Kuenzer. Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data. International Journal of Applied Earth Observation and Geoinformation. 2014; 26 ():335-349.
Chicago/Turabian StyleIgor Klein; Andreas J. Dietz; Ursula Gessner; Anastassiya Galayeva; Akhan Myrzakhmetov; Claudia Kuenzer. 2014. "Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data." International Journal of Applied Earth Observation and Geoinformation 26, no. : 335-349.