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

Regional Land Use Mapping: the Phoenix Pilot Project

The Phoenix Pilot Program has been designed to make effective use of past experience in making land use maps and collecting land use information. Conclusions reached from the project are: (1) Land use maps and accompanying statistical information of reasonable accuracy and quality can be compiled at a scale of 1:250,000 from orbital imagery. (2) Orbital imagery used in conjunction with other sources of information when available can significantly enhance the collection and analysis of land use information. (3) Orbital imagery combined with modern computer technology will help resolve the problem of obtaining land use data quickly and on a regular basis, which will greatly enhance the usefulness of such data in regional planning, land management, and other applied programs. (4) Agreement on a framework or scheme of land use classification for use with orbital imagery will be necessary for effective use of land use data

Implementing a GIS-Based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint; Part I: A Deductive Approach

The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open‐source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc

Implementing a GIS-based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint. Part I: A Deductive Approach.

The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open‐source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc

Investigating Sentinel 2 Multispectral Imagery Efficiency in Describing Spectral Response of Vineyards Covered with Plastic Sheets

The protection of vineyards with overhead plastic covers is a technique largely applied in table grape growing. As with other crops, remote sensing of vegetation spectral reflectance is a useful tool for improving management even for table grape viticulture. The remote sensing of the spectral signals emitted by vegetation of covered vineyards is currently an open field of investigation, given the intrinsic nature of plastic sheets that can have a strong impact on the reflection from the underlying vegetation. Baring these premises in mind, the aim of the present work was to run preliminary tests on table grape vineyards covered with polyethylene sheets, using Copernicus Sentinel 2 (Level 2A product) free optical data, and compare their spectral response with that of similar uncovered vineyards to assess if a reliable spectral signal is detectable through the plastic cover. Vine phenology, air temperature and shoot growth, were monitored during the 2016 growing cycle. Twenty-four Copernicus Sentinel 2 (S2, Level 2A product) images were used to investigate if, in spite of plastic sheets, vine phenology can be similarly described with and without plastic covers. For this purpose, time series of S2 at-the-ground reflectance calibrated bands and correspondent normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index, version two (MSAVI2) and normalized difference water index (NDWI) spectral indices were obtained and analyzed, comparing the responses of two covered vineyards with different plastic sheets in respect of two uncovered ones. Results demonstrated that no significant limitation (for both bands and spectral indices) was introduced by plastic sheets while monitoring spectral behavior of covered vineyards

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

tThis 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 relatedin time scenes, one for each sensor, were classified by using Object Based Image Analysis and RandomForest (RF). The RF input consisted of several object-based features computed from spectral bands andincluding mean values, spectral indices and textural features. S2 and L8 data comparisons were alsoextended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery totest differences only due to their specific spectral contribution. The best band combinations to performsegmentation were found through a modified version of the Euclidian Distance 2 index. Four differentRF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overallaccuracies respectively, evaluated over the whole study area

MULTIRESOLUTION SEGMENTATION FOR EXTRACTING PLASTIC GREENHOUSES FROM DEIMOS-2 IMAGERY

Abstract. Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. Multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almería). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results

A detailed procedure for the use of small-scale photography in land use classification

A procedure developed to produce accurate land use maps from available high-altitude, small-scale photography in a cost-effective manner is presented. An alternative procedure, for use when the capability for updating the resultant land use map is not required, is also presented. The technical approach is discussed in detail, and personnel and equipment needs are analyzed. Accuracy percentages are listed, and costs are cited. The experiment land use classification categories are explained, and a proposed national land use classification system is recommended

Use of ERTS-1 data to assess and monitor change in the Southern California environment

There are no author-identified significant results in this report

NEW ADVANCED TECHNOLOGIES FOR SURVEY AND ANALYSISbOF AGROFORESTRY LAND: FROM LAND COVER CHANGES TO RURAL LANDSCAPE QUALITY ASSESSMENT

The general objective of this Ph.D. thesis is to explore the concepts and methodologies for investigating agroforestry land and rural landscape through the integration of historical and remote sensing geodata within a FoSS (Free and Open Source Software) approach; to provide more and more accurate data sets regarding land cover and to improve some mapping and data processing techniques commonly used in this research topic. The first part of thesis describes the different types of geodata used in the course of the studies and, above all, the techniques and methodologies used for their processing are illustrated. Starting from historical cartographies, we will go through aerial surveys and geographical maps up to the new remote sensing using advanced satellite observation technologies. In the second part, more specific issues were dealt in accordance with the general objective of the work have been defined. The issues were approached through case studies within the Basilicata Region where the intensity of the abandonment of the territory and agricultural surface is leading to the loss of many historical rural landscapes and with consequent problems from an ecological point of view due to the disappearance of many agroforestry systems.L'obiettivo generale di questa tesi di dottorato è quello di esplorare i concetti e le metodologie per lo studio del territorio agroforestale e del paesaggio rurale attraverso l'integrazione di geodati storici e telerilevamento con un approccio FoSS (Free and Open Source Software); per fornire serie di dati sempre più accurate sulla copertura del suolo e migliorare alcune tecniche di mappatura ed elaborazione comunemente utilizzate in questo ambito di ricerca. La prima parte della tesi descrive i diversi tipi di geodati impiegati nel corso degli studi e, soprattutto, vengono illustrate le tecniche e le metodologie utilizzate per la loro elaborazione. Partendo dalle cartografie storiche, si passerà ai rilievi aerei ed alle cartogrofaie classifche fino al remote sensing basato su immagini satellitari. Nella seconda parte sono state trattate tematiche più specifiche in accordo con l'obiettivo generale del lavoro. Le tematiche sono state affrontate attraverso casi di studio all'interno della Regione Basilicata dove l'intensità dell'abbandono del territorio e della superficie agricola sta portando alla perdita di molti paesaggi rurali storici con conseguenti problemi dal punto di vista ecologico dovuti alla scomparsa di molti sistemi agroforestali