Combining ad hoc spectral indices based on LANDSAT-8 OLI/TIRS sensor data for the detection of plastic cover vineyard

Novelli, Antonio; Tarantino, Eufemia

doi:10.1080/2150704x.2015.1093186

Cited by 44 publications

(13 citation statements)

References 24 publications

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“…Land use and land cover changes, as well as the phenomenon of land take have been analyzed through a monitoring method based on remote sensing techniques to perform multi-temporal evaluations to provide important support for planning choices [43][44][45]. The use of satellite imagery (Landsat TM 4-5) and remote sensing techniques in a GIS environment allows defining various parameters, through supervised classification, in order to discriminate urban areas from other classes.…”

Section: Discussionmentioning

confidence: 99%

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Palma

Amato

Nolè

et al. 2016

IJGI

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Abstract:The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985-2010, based on the multi-temporal Landsat data Thematic Mapper (TM), has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy). Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take.

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Section: Discussionmentioning

confidence: 99%

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Palma

Amato

Nolè

et al. 2016

IJGI

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“…The research on plasticulture landscape extraction can be divided into two main approaches: pixel-based image analysis and object-based image analysis (OBIA). The former are adopted widely on detecting plasticulture information from satellite data at different spatial resolutions [5][6][7][8][9][10][11][12]. For example, Lu et al mapped the plastic-mulched cotton landcover from Landsat-5 TM and MODIS time series data respectively based on the decision tree model [2,3].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data

Lü

Yuan

2018

Remote Sensing

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Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1–3%.

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“…Over the last decade, greenhouse detection has mainly been addressed by using different pixel-based approaches supported by single satellite data, such as Landsat Thematic Mapper (TM) [7], Landsat 8 OLI/TIRS [8], the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [9], IKONOS or QuickBird [1,3,[10][11][12] and WorldView-2 [13,14]. The first paper using object-based image analysis (OBIA) approach for greenhouse detection was published in 2012 by Tarantino and Figorito [5], in this case working with digital true color (RGB) aerial data.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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Combining ad hoc spectral indices based on LANDSAT-8 OLI/TIRS sensor data for the detection of plastic cover vineyard

Cited by 44 publications

References 24 publications

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data

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

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