Jan Degener

Estimating Daily Maximum and Minimum Land Air Surface Temperature Using MODIS Land Surface Temperature Data and Ground Truth Data in Northern Vietnam

This study aims to evaluate quantitatively the land surface temperature (LST) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) MOD11A1 and MYD11A1 Collection 5 products for daily land air surface temperature (Ta) estimation over a mountainous region in northern Vietnam. The main objective is to estimate maximum and minimum Ta (Ta-max and Ta-min) using both TERRA and AQUA MODIS LST products (daytime and nighttime) and auxiliary data, solving the discontinuity problem of ground measurements. There exist no studies about Vietnam that have integrated both TERRA and AQUA LST of daytime and nighttime for Ta estimation (using four MODIS LST datasets). In addition, to find out which variables are the most effective to describe the differences between LST and Ta, we have tested several popular methods, such as: the Pearson correlation coefficient, stepwise, Bayesian information criterion (BIC), adjusted R-squared and the principal component analysis (PCA) of 14 variables (including: LST products (four variables), NDVI, elevation, latitude, longitude, day length in hours, Julian day and four variables of the view zenith angle), and then, we applied nine models for Ta-max estimation and nine models for Ta-min estimation. The results showed that the differences between MODIS LST and ground truth temperature derived from 15 climate stations are time and regional topography dependent. The best results for Ta-max and Ta-min estimation were achieved when we combined both LST daytime and nighttime of TERRA and AQUA and data from the topography analysis.

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

Recently, several methods have been introduced and applied to estimate daily air surface temperature (Ta) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily Ta-max, Ta-min, and Ta-mean estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various Ta estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of Ta estimation (R2 > 0.93/0.80/0.89 and RMSE ~1.5/2.0/1.6 °C of Ta-max, Ta-min, and Ta-mean, respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm.

Adaptive capacity based water quality resilience transformation and policy implications in rapidly urbanizing landscapes

Resilience-based management focuses on specific attributes or drivers of complex social-ecological systems, in order to operationalize and promote guiding principles for water quality management in urban systems. We therefore propose a resilience lens drawing on the theory of adaptive capacity and adaptive cycle to evaluate the urban resilience between water quality and land use type. Our findings show that the resilience of water quality variables, which were calculated based on their adaptive capacities, showed adaptive and sustainable trends with dramatic fluctuation. NH3-N, Cadmium and Total Phosphorus experienced the most vulnerable shifts in the built-up area, agricultural areas, and on bare land. Our framework provided a consistent and repeatable approach to address uncertainty inherent in the resilience of water quality in different landscapes, as well as an approach to monitor variables over time with respect to national water quality standards. Ultimately, we pointed to the political underpinnings for risk mitigation and managing resilient urban system in a particular coastal urban setting.

Atmospheric CO2 fertilization effects on biomass yields of 10 crops in northern Germany

The quality and quantity of the influence that atmospheric CO2 has on crop growth is still a matter of debate. This studys aim is to estimate if CO2 will have an effect on biomass yields at all, to quantify and spatially locate the effects and to explore if an elevated photosynthesis rate or water-use-efficiency is predominantly responsible. This study uses a numerical carbon based crop model (BioSTAR) to estimate biomass yields within the administrative boundaries of Niedersachsen in Northern Germany. 10 crops are included (winter grains: wheat, barley, rye, triticale - early, medium, late maize variety - sunflower, sorghum, spring wheat), modeled annually for the entire 21st century on 91,014 separate sites. Modeling was conducted twice, once with an annually adapted CO2 concentration according to the SRES-A1B scenario and once with a fixed concentration of 390 ppm to separate the influence of CO2 from that of the other input variables. Rising CO2 concentrations will play a central role in keeping future yields of all crops above or around todays level. Differences in yields between modeling with fixed or adapted CO2 can be as high as 60 % towards the centurys end. Generally yields will increase when CO2 rises and decline when it is kept constant. As C4-crops are equivalently affected it is presumed that an elevated efficiency in water use is the main responsible factor for all plants.

Inter-Comparison and Evaluation of the Global LAI Product (LAI3g) and the Regional LAI Product (GGRS-LAI) over the Area of Kazakhstan

Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM). The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series. The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data. The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS & Remote Sensing, GGRS dataset). The GGRS-LAI product uses a satellite-based LAI. This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season. During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g. LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS. Moreover, LAI3g showed LAI > 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan. A direct cause for this could be a too high base level of the LAI3g during the leafless phase.

Differences in biomass yield development of early, medium, and late maize varieties during the 21st century in Northern Germany

BackgroundThough there exists a general notion on how maize yields might develop throughout Europe during the current century, modeling approaches on a regional level that account for small-scale variations are not yet universally available. Furthermore, many studies only refer to one variety of maize. However, the few studies that include at least two varieties indicate that the respective choice will play a major role in how the yields will develop under a changing climate throughout the 21st century. This study will evaluate how far this choice of variety will affect future yields, identify the main factors to explain potential differences, and determine the magnitude of spatial variability.ResultsThe results suggest clearly differentiated development paths of all varieties. All varieties show a significant positive trend until the end of the century, though the medium variety also shows a significant decline of 5% during the first 30 years and only a slight recovery towards +5% around the century’s end. The late variety has the clearest and strongest positive trend, with peaks of more than +30% increase of biomass yields and around 25% average increase in the last three decades. The early variety can be seen as in-between, with no negative but also not an as-strong positive development path. All varieties have their strongest increase after the mid of the 21st century. Statistical evaluation of these results suggests that the shift from a summer rain to a winter rain climate in Germany will be the main limiting factor for all varieties. In addition, summer temperatures will become less optimal for all maize crops. As the data suggests, the increasing atmospheric CO2 concentrations will play a critical role in reducing the crops water uptake, thus enabling yield increases in the first place.ConclusionsThis study clearly shows that maize yields will develop quite differently under the assumed climatic changes of the 21st century when different varieties are regarded. However, the predominant effect is positive for all discussed varieties and expected to be considerably stronger in the second half of the century.

Long-Term Changes in Forest Cover in Central Veracruz, Mexico (1993–2014):

Deforestation and fragmentation are threats to the conservation of species and have consequences for ecosystem functions. The focus of this study was to elucidate forest cover change in the period of 1993 to 2014. Our study area is in the central region of Veracruz, Mexico. Land cover and land use classes for the Years 1993, 2000, and 2014 were derived from Landsat images applying supervised classification. Then, we quantified the net change in forest area, the loss of original forest area, and evaluated forest fragmentation using landscape metrics. Our results showed that the area covered by remnant forests decreased 57%. The annual net forest cover change rate for 1993 to 2000 was −0.44%; since then forest cover increased at a rate of 0.11% from 2000 to 2014. The decreasing total edge density and the mean proximity index during the entire period of the study indicate decreasing irregularity in the shape of remnant forest patches and a slight decrease of vulnerability to edge effects. Forest patches augmented in 2000 and decreased in 2014 demonstrating an 18% decrease in relation to the number of fragments existing in 1993. According to our study, this area demands an urgent attention on preservation initiatives because only 2% of the surface extent is below federal protection and 0.8% is under State protection. It is important to protect the larger forest areas left in the pine-oak and humid montane forest belt because of their importance to plant diversity conservation and particularly, as these are threatened by urban and agricultural expansion.