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

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

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

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

Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression

Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric variables measured from airborne sensors, such as tree height (H) and crown diameter (CD). This study explores the usefulness of ensemble-type supervised machine learning regression algorithms, such as random forest regression (RFR), categorical boosting (CatBoost), gradient boosting (GBoost), or AdaBoost regression (AdaBoost), as an alternative to linear regression (LR) for modelling the allometric relationships DBH = Φ(H) and DBH = Ψ(H, CD). The original dataset was made up of 2272 teak trees (Tectona grandis Linn. F.) belonging to three different plantations located in Ecuador. All teak trees were digitally reconstructed from terrestrial laser scanning point clouds. The results showed that allometric models involving both H and CD to estimate DBH performed better than those based solely on H. Furthermore, boosting machine learning regression algorithms (CatBoost and GBoost) outperformed RFR (bagging) and LR (traditional linear regression) models, both in terms of goodness-of-fit (R2) and stability (variations in training and testing samples)

DSM and DTM generation from VHR satellite stereo imagery over plastic covered greenhouse areas

Agriculture under Plastic Covered Greenhouses (PCG) has represented a step forward in the evolution from traditional to industrial farming. However, PCG-based agricultural model has been also criticized for its associated environmental impact such as plastic waste, visual impact, soil pollution, biodiversity degradation and local runoff alteration. In this sense, timely and effective PCG mapping is the only way to help policy-makers in the definition of plans dealing with the trade-off between farmers’ profit and environmental impact for the remaining inhabitants. This work proposes a methodological pipeline for producing high added value 3D geospatial products (Digital Surface Models (DSM) and Digital Terrain Models (DTM)) from VHR satellite imagery over PCG areas. The 3D information layer provided through the devised approach could be very valuable as a complement to the traditional 2D spectral information offered by VHR satellite imagery to improve PCG mapping over large areas. This methodological approach has been tested in Almeria (Southern Spain) from a WorldView-2 VHR satellite stereo-pair. Once grid spacing format DSM and DTM were built, their vertical accuracy was assessed by means of lidar data provided by the Spanish Government (PNOA Programme). Regarding DSM completeness results, the image matching method based on hierarchical semi-global matching yielded much better scores (98.87%) than the traditional image matching method based on area-based matching and cross-correlation threshold (86.65%) when they were tested on the study area with the highest concentration of PCG (around 85.65% of PCG land cover). However, both image matching methods yielded similar vertical accuracy results in relation to the finally interpolated DSM, with mean errors ranging from 0.01 to 0.35m and random errors (standard deviation) between 0.56 and 0.82 m. The DTM error figures also showed no significant differences between both image matching methods, although being highly dependent on DSM-to- DTM filtering error, in turn closely related to greenhouse density and terrain complexity

Methodological proposal to assess plastic greenhouses land cover change from the combination of archival aerial orthoimages and Landsat data

This work outlines a methodological proposal to assess plastic covered greenhouses (PCG) land cover change from the combination of archival aerial orthoimages and Landsat data. In this way, landscape spatial metrics were semi-automatically derived to be used in the analysis of the spatial arrangement of PCG areas. The experimental process consisted of two main phases: (i) mapping PCG through a semi-automatic object-based image analysis (OBIA) approach relying on segmentation plus non-parametric supervised classification; (ii) processing the PCG classified objects to yield different landscape spatial metrics. The case study has focused on two high density PCG sites located in south-eastern Spain. To analyse PCG land cover evolution, each study site was composed of three multi-temporal remote sensed datasets formed by the fusion of orthoimages (O) derived from archival aerial photography and temporally corresponding Landsat images (L). In terms of PCG mapping performance, the best results were obtained when using O þ L datasets as complementary data to be used in a data fusion process. In addition, a new feature called “Greenhouse Detection Index” has been successfully developed and tested, yielding excellent results at the mapping phase. Finally, the semi-automatically extracted PCG land cover metrics, though depicting some variability, have reproduced the behaviour and temporal trend of the manually obtained ones (manual digitalisation) reasonably well. These results can be translated to an exponential reduction of time and cost for analysing long-term PCG land cover change

METHODOLOGICAL PROPOSAL FOR THE SELECTION AND ANALYSIS OF THE PERFORMANCE OF RIGID SAILS CONSIDERING PRE-STALL AND POST-STALL AERODYNAMIC CONDITIONS

The shipping industry is increasingly considering the issues of "green shipping" and "zero-emission shipping", which are closely related to reducing the consumption of fossil fuels to mitigate its environmental impact. In this way, the use of renewable energies is increasingly perceived as part of the energy mix, with the use of rigid sails (wind energy) being one of the tested alternatives as auxiliary propulsion systems. This work studied the fluid-mechanical optimization of the airfoil that defines the cross section geometry of the rigid sail. A methodology was developed to obtain the aerodynamic coefficients of the rigid sail for any position of the sail with respect to the wind depending on the tested airfoil. Once the aerodynamic coefficients were modeled for several angles of attack, the thrust and overturning components generated by the sail force on the boat were computed from before and after airfoil stalling predictive models of the aerodynamic coefficients. From the obtained results, the following two main conclusions can be stated. i) The use of the sail in post-stall conditions for higher angles of incidence (from approximately 90°) substantially improved the thrust generated on the boat. For the case of small incidence angles, it is recommended to set the sail at an angle of attack less than the airfoil stall. ii) Within the symmetrical four-digit NACA series, the symmetrical NACA 0015 airfoil turned out to be the best choice for geometrically defining the rigid sail cross section, as it provided the highest possible thrust while minimizing the overturning force

Optimizing multiresolution segmentation for extracting plastic greenhouses from WorldView-3 imagery

Multiresolution segmentation (MRS) has been pointed out as one of the most successful image segmentation algorithms within the object-based image analysis (OBIA) framework. The performance of this algorithm depends on the selection of three tuning parameters (scale, shape and compactness) and the bands combination and weighting considered. In this work, we tested MRS on a WorldView-3 bundle imagery in order to extract plastic green-house polygons. A recently published command line tool created to assess the quality of segmented digital images (AssesSeg), which implements a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to select both the best aforementioned MRS pa-rameters and the optimum image data source derived from WorldView-3 (i.e., panchromatic, multispectral and atmospherically corrected multispectral orthoimages). The best segmentation results were always attained from the atmospherically corrected multispectral WorldView-3 orthoimage