This research aims to assess the capabilities of Very High Spatial Resolution (VHSR) hyperspectral satellite data in order to discriminate urban tree diversity. Four dimension reduction methods and two classifiers are tested, using two learning methods and applied with four in situ sample datasets. An airborne HySpex image (408 bands/2 m) was acquired in July 2015 from which prototypal spaceborne hyperspectral images (named HYPXIM) at 4 m and 8 m and a multispectral Sentinel2 image at 10 m have been simulated for the purpose of this study. A comparison is made using these methods and datasets. The influence of dimension reduction methods is assessed on hyperspectral (HySpex and HYPXIM) and Sentinel2 datasets. The influence of conventional classifiers (Support Vector Machine –SVM– and Random Forest –RF–) and learning methods is evaluated on all image datasets (reduced and non-reduced hyperspectral and Sentinel2 datasets). Results show that HYPXIM 4 m and HySpex 2 m reduced by Minimum Noise Fraction (MNF) provide the greatest classification of 14 species using the SVM with an overall accuracy of 78.4% (±1.5) and a kappa index of agreement of 0.7. More generally, the learning methods have a stronger influence than classifiers, or even than dimensional reduction methods, on urban tree diversity classification. Prototypal HYPXIM images appear to present a great compromise (192 spectral bands/4 m resolution) for urban vegetation applications compared to HySpex or Sentinel2 images.
Thermal conditions, influenced by the local environment, impact the development of the vine and determine the composition of the grapes. Bioclimatic indices, based on cumulative air temperatures, are modelled and mapped using statistical methods integrating local factors. Air temperature data from sensors networks are limited in space and time. We evaluated the potential of land surface temperature (LST) to identify comparable spatial distribution, and not to replace air temperature, by using a support vector machine algorithm to compare bioclimatic indices calculated from air temperature or LST. This study focused on the 2012–2018 period in the Saint-Emilion winegrowing area of France. The use of several digital elevation models with high spatial resolution (i.e., GMTED10 (1000, 500 and 250 m) and SRTM (90 and 30 m)) enabled LST to be downscaled at each resolution. The same topographic variables (elevation, slope, orientation coordinates) were used as predictors, and identical algorithms and cross-validation parameters were implemented in both mapping methods. Bioclimatic indices were calculated from daily air temperature, daily LST or weekly LST. The results of the daily and weekly downscaling of the MODIS time series at several spatial resolutions are encouraging for application to viticulture and have allowed to identify an optimal resolution between 500 m and 250 m limiting bias.
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