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Evapotranspiration (ET) $(ET)$ is a key process in the hydrological cycle that can help mitigate urban heat. ET $ET$ depends on the surface cover, as the surface affects the partitioning of precipitation between runoff and evapotranspiration. In urban neighborhoods, this surface cover is highly heterogeneous. The resulting neighborhood‐scale ET $ET$ can be observed with eddy‐covariance systems. However, these observations represent the signal from wind‐ and stability‐dependent footprints resulting in a continuously changing contribution of surface cover types to the observation. This continuous change prevents quantifying the contribution of the surface cover types to neighborhood ET $ET$ and their hourly dynamics. Here, we disentangle this neighborhood‐scale ET $ET$ at two sites in Berlin attributing the patch‐scale ET $ET$ dynamics to the four major surface cover types in the footprint: impervious surfaces, low vegetation, high vegetation, and open water. From the bottom‐up, we reconstruct neighborhood ET $ET$ based on patch‐scale observations and conceptual models. Alternatively, we start top‐down and attribute neighborhood ET $ET$ to the surface cover types solving a system of equations for three eddy‐covariance systems. Although data requirements for the bottom‐up approach are met more frequently, both approaches indicate that vegetation is responsible for more ET $ET$ than proportional to its surface fraction in the footprint related to the large evaporating surface compared to the ground surface. Evaporation from impervious surfaces cannot be neglected, although it is less than from vegetation due to limited water availability. The limited water availability causes impervious surfaces to cease evaporation hours after rainfall, while vegetation and open water sustain ET $ET$ for extended periods. Plain Language Summary: Different types of surfaces, like grass, trees, pavement, and open water, affect how rainwater is divided between evaporation and runoff. In cities with lots of pavement and buildings, more water runs off than in natural areas leaving less water for evaporation. Measurement towers have been observing the evaporation from whole neighborhoods, but separating the effects of different surfaces is hard. In our study, we figure out how much each surface type contributes to evaporation with two methods: one starting from the separate surfaces and rebuilding the neighborhood evaporation, and the other starting with the neighborhood evaporation and breaking it down into evaporation from each surface. Both ways showed that plants evaporate more than proportionally to their surface area, but even built surfaces like pavement evaporate. Our findings confirm that more plants lead to more evaporation, but built surfaces cannot be ignored. This information can help urban planners create cities that manage water better, making cities nicer places to live. Key Points: Neighborhood ET is reconstructed from patch‐scale data (bottom‐up) and disentangled attributing ET to four surface types (top‐down)The neighborhood and patch scale are connected through half‐hourly‐varying eddy‐covariance footprintsET dynamics after rainfall reveal that water limitation drives differences between surface cover types [ABSTRACT FROM AUTHOR]

This study investigated the statistical relationship between defoliation in pine forests infested by nun moths (Lymantria monacha) and the spectral bands of the RapidEye sensor, including the derived normalized difference vegetation index (NDVI) and the normalized difference red-edge index (NDRE). The strength of the relationship between the spectral variables and the ground reference samples of percent remaining foliage (PRF) was assessed over three test years by the Spearman’s ρ correlation coefficient, revealing the following ranking order (from high to low ρ): NDRE, NDVI, red, NIR, green, blue, and red-edge. A special focus was directed at the vegetation indices. In both discriminant analyses and decision tree classification, the NDRE yielded higher classification accuracy in the defoliation classes containing none to moderate levels of defoliation, whereas the NDVI yielded higher classification accuracy in the defoliation classes representing severe or complete defoliation. We concluded that the NDRE and the NDVI respond very similarly to changes in the amount of foliage, but exhibit particular strengths at different defoliation levels. Combining the NDRE and the NDVI in one discriminant function, the average gain of overall accuracy amounted to 7.8 percentage points compared to the NDRE only, and 7.4 percentage points compared to the NDVI only. Using both vegetation indices in a machine-learning-based decision tree classifier, the overall accuracy further improved and reached 81% for the test year 2012, 71% for 2013, and 79% for the test year 2014.

Within the framework of this project methods are being tested and implemented a) to introduce remote sensing based approaches into the existing process of biotope mapping and b) to develop a framework serving the multiple requirements arising from different users' backgrounds and thus the need for comprehensive data interoperability. Therefore state-wide high resolution land cover vector-data have been generated in an automated object oriented workflow based on aerial imagery and a normalised digital surface models.These data have been enriched by an extensive characterisation of the individual objects by e.g. site specific, contextual or spectral parameters utilising multitemporal satellite images, DEM-derivatives and multiple relevant geo-data. Parameters are tested on relevance in regard to the classification process using different data mining approaches and have been used to formalise categories of the European nature information system (EUNIS) in a semantic framework. The Classification will be realised by ontology-based reasoning. Dissemination and storage of data is developed fully INSPIRE-compatible and facilitated via a web portal. Main objectives of the project are a) maximum exploitation of existing "standard" data provided by state authorities, b) combination of these data with satellite imagery (Copernicus), c) create land cover objects and achieve data interoperability through low number of classes but comprehensive characterisation and d) implement algorithms and methods suitable for automated processing on large scales. [ABSTRACT FROM AUTHOR]

The Tarim River basin, located in Xinjiang, NW China, is the largest endorheic river basin in China and one of the largest in all of Central Asia. Due to the extremely arid climate, with an annual precipitation of less than 100 mm, the water supply along the Aksu and Tarim rivers solely depends on river water. This is linked to anthropogenic activities (e.g., agriculture) and natural and semi-natural ecosystems as both compete for water. The ongoing increase in water consumption by agriculture and other human activities in this region has been enhancing the competition for water between human needs and nature. Against this background, 11 German and 6 Chinese universities and research institutes have formed the consortium SuMaRiO (Sustainable Management of River Oases along the Tarim River; http://www.sumario.de), which aims to create a holistic picture of the availability of water resources in the Tarim River basin and the impacts on anthropogenic activities and natural ecosystems caused by the water distribution within the Tarim River basin. On the basis of the results from field studies and modeling approaches as well as from suggestions by the relevant regional stakeholders, a decision support tool (DST) will be implemented that will then assist stakeholders in balancing the competition for water, acknowledging the major external effects of water allocation to agriculture and to natural ecosystems. This consortium was formed in 2011 and is funded by the German Federal Ministry of Education and Research. As the data collection phase was finished this year, the paper presented here brings together the results from the fields from the disciplines of climate modeling, cryology, hydrology, agricultural sciences, ecology, geoinformatics, and social sciences in order to present a comprehensive picture of the effects of different water availability schemes on anthropogenic activities and natural ecosystems along the Tarim River. The second objective is to present the project structure of the whole consortium, the current status of work (i.e., major new results and findings), explain the foundation of the decision support tool as a key product of this project, and conclude with application recommendations for the region. The discharge of the Aksu River, which is the major tributary of the Tarim, has been increasing over the past 6 decades. From 1989 to 2011, agricultural area more than doubled: cotton became the major crop and there was a shift from small-scale to large-scale intensive farming. The ongoing increase in irrigated agricultural land leads to the increased threat of salinization and soil degradation caused by increased evapotranspiration. Aside from agricultural land, the major natural and semi-natural ecosystems are riparian (Tugai) forests, shrub vegetation, reed beds, and other grassland, as well as urban and peri-urban vegetation. Within the SuMaRiO cluster, focus has been set on the Tugai forests, with Populus euphratica as the dominant tree species, because these forests belong to the most productive and species-rich natural ecosystems of the Tarim River basin. At sites close to the groundwater, the annual stem diameter increments of Populus euphratica correlated with the river runoffs of the previous year. However, the natural river dynamics cease along the downstream course and thus hamper the recruitment of Populus euphratica. A study on the willingness to pay for the conservation of the natural ecosystems was conducted to estimate the concern of the people in the region and in China's capital. These household surveys revealed that there is a considerable willingness to pay for conservation of the natural ecosystems, with mitigation of dust and sandstorms considered the most important ecosystem service. Stakeholder dialogues contributed to creating a scientific basis for a sustainable management in the future. [ABSTRACT FROM AUTHOR]