Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

Developing Allometric Equations for Teak Plantations Located in the Coastal Region of Ecuador from Terrestrial Laser Scanning Data

Traditional studies aimed at developing allometric models to estimate dry above-ground biomass (AGB) and other tree-level variables, such as tree stem commercial volume (TSCV) or tree stem volume (TSV), usually involves cutting down the trees. Although this method has low uncertainty, it is quite costly and inefficient since it requires a very time-consuming field work. In order to assist in data collection and processing, remote sensing is allowing the application of non-destructive sampling methods such as that based on terrestrial laser scanning (TLS). In this work, TLS-derived point clouds were used to digitally reconstruct the tree stem of a set of teak trees (Tectona grandis Linn. F.) from 58 circular reference plots of 18 m radius belonging to three different plantations located in the Coastal Region of Ecuador. After manually selecting the appropriate trees from the entire sample, semi-automatic data processing was performed to provide measurements of TSCV and TSV, together with estimates of AGB values at tree level. These observed values were used to develop allometric models, based on diameter at breast height (DBH), total tree height (h), or the metric DBH2 × h, by applying a robust regression method to remove likely outliers. Results showed that the developed allometric models performed reasonably well, especially those based on the metric DBH2 × h, providing low bias estimates and relative RMSE values of 21.60% and 16.41% for TSCV and TSV, respectively. Allometric models only based on tree height were derived from replacing DBH by h in the expression DBH2 x h, according to adjusted expressions depending on DBH classes (ranges of DBH). This finding can facilitate the obtaining of variables such as AGB (carbon stock) and commercial volume of wood over teak plantations in the Coastal Region of Ecuador from only knowing the tree height, constituting a promising method to address large-scale teak plantations monitoring from the canopy height models derived from digital aerial stereophotogrammetry

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Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

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

A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region

The model of the first spanish criminal codification in the american codes

En este estudio se analizan las influencias recibidas por la primera codificación penal española que luego serviría de modelo a muchos Códigos penales de las Repúblicas americanas independientes a lo largo del siglo XIX. También se alude a algunas instituciones penalísticas procedentes del viejo derecho indiano que aún permanecen en la nueva codificación. En paralelo se estudia la influencia de los nuevos principios constitucionales y la resistencia, de otra parte, a su aceptación en el nuevo Código penal. En el debate codificador se plantean los principios políticos que estuvieron presentes.This study analyzes the influences received by the first Spanish penal coding that later served as a model for many penal codes of the independent American Republics throughout the 19th century. Also it is alluded to some criminal penalties proceeding from the old Indian law that still remain in the new codification. In parallel, the influence of the new constitutional principles and the resistance of another party to its acceptance in the new penal code are studied. In the codifying debate are analized the political principles that were presents

Evaluation of Object-Based Greenhouse Mapping Using WorldView-3 VNIR and SWIR Data: A Case Study from Almería (Spain)

Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and eight additional bands in the short-wave infrared (SWIR) region. A few studies have already established the importance of indices based on some of these SWIR bands to detect urban plastic materials and hydrocarbons which are also related to plastics. This paper aims to investigate the capability of WV3 (VNIR and SWIR) for direct PCG detection following an object-based image analysis (OBIA) approach. Three strategies were carried out: (i) using object features only derived from VNIR bands (VNIR); (ii) object features only derived from SWIR bands (SWIR), and (iii) object features derived from both VNIR and SWIR bands (All Features). The results showed that the majority of predictive power was attributed to SWIR indices, especially to the Normalized Difference Plastic Index (NDPI). Overall, accuracy values of 90.85%, 96.79% and 97.38% were attained for VNIR, SWIR and All Features strategies, respectively. The main PCG misclassification problem was related to the agricultural practice of greenhouse whitewash (greenhouse shading) that temporally masked the spectral signature of the plastic film

Córdoba : Ciudad de Encuentro y Diálogo. El porvenir de la convivencia en el Mediterráneo

172 páginasConferencias y mesas redondas del Encuentro 'Córdoba: ciudad de encuentro y diálogo', celebrado del 10 al 17 de abril de 2016, y organizado entorno de una idea clave: la convivencia en un Mediterráneo cada vez más fracturado