This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 comparisons were also extended through using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific features contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered: L8 features extracted from both L8-based segments WV2-based segments; S2 features extracted from both S2-based segments and WV2-based segments. The best overall accuracies, evaluated on the whole study area, were 89.1%, 91.3%, 90.9% and 93.4% respectively.

Abstract:The main goal of this paper is to study the effect of the spatio-temporal changes of Land Use/Land Cover (LULC) within the hydrologic regime of the Cervaro basin in Southern Italy. LANDSAT Thematic Mapper (TM) imagery acquisition dates from 1984, 2003, 2009, and 2011 were selected to produce LULC maps covering a time trend of 28 years. Nine synthetic bands were processed as input data identified as the most effective for the Artificial Neural Network (ANN) classification procedure implemented in this case study. To assess the possible hydrological effects of the detected changes during rainfall events, a physically-based lumped approach for infiltration contribution was adopted within each sub-basin. The results showed an increase in flood peak and a decrease of the rangelands, forests, and bare lands between 1984 and 2011, indicating a good correlation between flooding areas and land use changes, even if it can be considered negligible in basins of large dimensions. These results showed that the impact of land use on the hydrological response is closely related to watershed scale.

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively.

The paper investigates the European space heating (SH) and domestic hot water (DHW) market in order to close knowledge gaps concerning its size. The stimulus for this research arises from incongruences found in SH and DHW market’s data in spite of over two decades of scientific research. The given investigation has been carried out in the framework of the Hotmaps project (Horizon 2020—H2020), which aims at designing an open source toolbox to support urban planners, energy agencies, and public authorities in heating and cooling (H&C) planning on country, regional, and local levels. Our research collects and analyzes SH and DHW market data in the European Union (EU), specifically the amount of operative units, installed capacities, energy efficiency coefficients as well as equivalent full-load hours per equipment type and country, with a bottom-up approach. The analysis indicates that SH and DHW account for a significant portion of the total EU energy utilization (more than 20%), amounting to almost 3900 TWh/y. At the same time, the energy consumption provided by district heating (DH) systems exceeds the one of condensing boilers. While DH systems applications are growing throughout the EU, the replacement of elderly, conventional boilers progresses at a slower pace.

M.A.A.); faguilar@ual.es (F.J.A.)Abstract: A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (F β around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.For instance, a combination of data from Sentinel-2 (2A and 2B) and Landsat 8 provides a global median average revisit interval of 2.9 days [9].Regarding PCG mapping from remote sensing, an increasing amount of scientific literature has been published during the last decade that has mainly focused on Landsat imagery [4,[10][11][12][13][14][15][16]. Novelli et al. [17] compared single-date Sentinel-2 and Landsat 8 data to automatically classify PCG. A few indices especially adapted to plastic sheet detection, such as the Index Greenhouse Vegetable Land Extraction (Vi) [18], Plastic-Mulched Landcover Index (PMLI) [10], Moment Distance Index (MDI) [12,19], Plastic Greenhouse Index (PGI) [13], and Greenhouse Detection Index (GDI) [16] have been recently proposed.In relation to the classification of crops via remote sensing, Badhwar [20] published one of the first works where Landsat imagery multi-temporal data (only three dates) were used for corn and soybean crops mapping. In fact, crop types classification from medium resolution satellite imagery was mainly conducted by using pixel-based approaches until approximately 2011. Just before Petitjean et al. [21] argued that the increasing spatial resolution of available spaceborne sensors was enabling the application of the object-based image analysis (OBIA) paradigm to extract crop types from satellite image time series, Peña-Barragán et al. [22] developed a methodology for outdoor crop identification and mapping using OBIA and decision tree algorithms. This methodology was also applied to a Landsat time series to map sugarcane over large areas [23]. This OBIA approach consisted of two main consecutive phases: (i) the delimitation of crop fields by image segmentation and, (ii) the application of decision rules based...