Comparative assessment of remote sensing-based water dynamic in a dam lake using a combination of Sentinel-2 data and digital elevation model.

Monitoring and determining the amount of water in reservoirs is of great importance in terms of water planning and management. This study proposes a geographic information system (GIS)-based methodology to estimate the water volume changes in water reservoirs. Two specific methods are proposed using Australian National University's Digital Elevation Model (ANUDEM) raster surface and Triangulated Irregular Network (TIN) surface models, both utilizing normalized difference water index (NDWI) of Sentinel 2A satellite images for water-covered area and coastline and digital elevation model (DEM) for 3D modelling of the reservoir. The most crucial part of this study is the comprehensive evaluation of the model findings considering hydrological, meteorological and anthropogenic factors, simultaneously. Application of the proposed methods is provided for the analysis of the multi-temporal water volume changes of Bayramiç Dam Lake (Çanakkale, Turkey) in two hydrological periods covering the 2015-2016 and 2016-2017 water years. The results indicate that the TINS model produced water volume values much closer to the in situ Turkish General Directorate of State Hydraulic Works (DSI) values than the ANUDEM model. The performance of these methods was also assessed by the temporal dynamics of surface hydrological processes. Regarding the water storage dynamics, hydro-meteorological factors influence the water input, while anthropogenic factors strongly influence the water output. Water consumption for irrigation and electricity generation was found to be the most important water budget components of the total water consumption.

Uncertainties in mapping forest carbon in urban ecosystems.

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m2, aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.

Determination of seasonal changes in wetlands using CHRIS/Proba Hyperspectral satellite images: A case study from Acigöl (Denizli), Turkey.

The changes in wetlands that occur through natural processes, as well as through industrialization and agricultural activities, are decreasing and even annihilating the living spaces of endemic species. Acigöl (Denizli, Turkey), which is a suitable habitat for flamingos (Phoenicopterus ruber), is a lake that is affected by seasonal anomalies as a result of being shallow. Acigöl, which is fed by precipitation, groundwater and the springs that occur along tectonic faults, has no water output other than evaporation and industrial activities. In addition to natural factors, it is important to determine the changes in the wetlands of Acig6l, where industrial salt is produced, in order to reveal the micro-ecological equilibrium, the relationship between climate and natural life, and regulation of industrial activities. Remote sensing tools are frequently used in determination of changes in wetlands. Changes in coastlines, water level and area covered by water are parameters that can be examined by remote sensing while investigating wetlands. In this study, the water-covered area was examined using remote sensing. Within the scope of this study, CHRIS/Proba Mode 2 (water bandset) hyperspectral satellite images, acquired on 9/17/2011 for the season and on 6/18/2012 - 6/19/2012 forwet season, were used in orderto present the seasonal changes in Acigöl, during one hydrogeological period. The processes of noise reduction, cloud screening, atmospheric correction, geometric correction, and identification of wetlands have been implemented on the CHRIS/Proba images. In determining the water-covered areas, the Normalized Difference Water Index (NDWI) was used. It was determined that W6 (560 nm) and W18 (1015 nm) and W2 (447 nm) and W18 (1015 nm) band combinations were most appropriate to be used in NDWI to demonstrate the water-land separation. Using Proba-NDWI image, it was established that an area of 27.4 km2 was covered with water during dry season, and 61.2 km2 was covered during wet season. The results indicated that; since the lake water area is directly affected by seasonal and annual climatic anomalies, water used by industrial facilities has to be drawn out of the lake in reasonable amount.

Investigation of grapevine photosynthesis using hyperspectral techniques and development of hyperspectral band ratio indices sensitive to photosynthesis.

The photosynthetic rate of 9 different grapevines were analyzed with simultaneous photosynthesis and spectroradiometric measurements on 08.08.2012 (veraison) and 06.09.2012 (harvest). The wavelengths and spectral regions, which most properly express photosynthetic rate, were determined using correlation and regression analysis. In addition, hyperspectral band ratio (BR) indices sensitive to photosynthesis were developed using optimum band ratio (OBRA) method. The relation of BR results with photosynthesis values are presented with the correlation matrix maps created in this study. The examinations were performed for both specific dates (i.e., veraison and harvest) and also in aggregate (i.e., correlation between total spectra and photosynthesis data). For specific dates wavelength based analysis, the photosynthesis were best determined with -0.929 correlation coefficient (r) 609 nm of yellow region at veraison stage, and -0.870 at 641 nm of red region at harvest stage. For wavelength based aggregate analysis, 640 nm of red region was found to be correlated with 0.921 and -0.867 r values respectively and red edge (RE) (695 nm) was found to be correlated with -0.922 and -0.860 r values, respectively. When BR indices results were analyzed with photosynthetic values for specific dates, -0.987 r with R8../R, at veraison stage and -0.911 r with R696/R944 at harvest stage were found most correlated. For aggregate analysis of BR, common BR presenting great correlation with photosynthesis for both measurements was found to be R632/R971 with -0.974, -0.881 r values, respectively and other R610/R760 with -0.976, -0.879 r values. The final results of this study indicate that the proportion of RE region to a region with direct or indirect correlation with photosynthetic provides information about rate of photosynthesis. With the indices created in this study, the photosynthesis rate of vineyards can be determined using in-situ hyperspectral remote sensing. The findings of this study would enable cost-efficient, rapid and effective control of viticulture activities.