Agricultural plastic waste spatial estimation by Landsat 8 satellite images

Abstract The use of plastic materials in agriculture involves several benefits but it results in huge quantities of agricultural plastic waste to be disposed of. Input and output data on the use of plastics in agriculture are often difficult to obtain and poor waste management schemes have been developed. The present research aims to estimate and map agricultural plastic waste by using satellite images. Waste was evaluated by means of the indexes relating waste production to crop type and plastic application as defined by the land use map realized by classifying the Landsat 8 image. The image classification was carried out using Support Vector Machines (SVMs), and the accuracy assessment showed that the overall accuracy was 94.54% and the kappa coefficient equal to 0.934. Data on the plastic waste obtained by the satellite land use map were compared with the data obtained by using the institutional land use map; a difference of 1.74% was identified on the overall quantity of waste.

[1]  Demetres Briassoulis,et al.  Mechanical behaviour and properties of agricultural nets. Part II: Analysis of the performance of the main categories of agricultural nets , 2007 .

[2]  Claudia Arcidiacono,et al.  Classification of crop-shelter coverage by RGB aerial images: a compendium of experiences and findings. , 2010 .

[3]  Carmela Sica,et al.  PLASTIC MATERIALS IN EUROPEAN AGRICULTURE: ACTUAL USE AND PERSPECTIVES , 2012 .

[4]  Pietro Picuno,et al.  Innovative Material and Improved Technical Design for a Sustainable Exploitation of Agricultural Plastic Film , 2014 .

[5]  Eyal Ben-Dor,et al.  Remote sensing as a tool for monitoring plasticulture in agricultural landscapes , 2007 .

[6]  Demetres Briassoulis,et al.  Analysis and Design of Low-density Polyethylene Greenhouse Films , 2003 .

[7]  Carmela Sica,et al.  Experimental tests and technical characteristics of regenerated films from agricultural plastics , 2012 .

[8]  Eufemia Tarantino,et al.  Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data , 2012, Remote. Sens..

[9]  Pietro Picuno,et al.  Modified plastic net-houses as alternative agricultural structures for saving energy and water in hot and sunny regions , 2016 .

[10]  He Li,et al.  Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features , 2016, Remote. Sens..

[11]  Qi Chen,et al.  A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones , 2011 .

[12]  A. Mistriotis,et al.  Airflow through net covered tunnel structures at high wind speeds , 2012 .

[13]  Carmela Sica,et al.  Swot analysis and land management of plastic wastes in agriculture , 2015 .

[14]  Athos Agapiou,et al.  Monitoring olive mills waste disposal areas in Crete using very high resolution satellite data , 2016 .

[15]  Abderrahim Nemmaoui,et al.  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 , 2016 .

[16]  Demetres Briassoulis,et al.  Plastic nets in agriculture ; a general review of types and applications , 2008 .

[17]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[18]  Jennifer Markarian Plasticulture comes of age , 2005 .

[19]  Abderrahim Nemmaoui,et al.  Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series , 2016, Remote. Sens..

[20]  G. Vox,et al.  Radiometric Properties of Plastic Films for Vineyard Covering and Their Influence on Vine Physiology and Production , 2012 .

[21]  Liping Di,et al.  Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: a case study in southern Xinjiang, China , 2015 .

[22]  Guobin Zhu,et al.  Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel , 2002 .

[23]  Barnali M. Dixon,et al.  Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? , 2008 .

[24]  Soe W. Myint,et al.  Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data , 2008, Sensors.

[25]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[27]  Giacomo Scarascia Mugnozza,et al.  Mapping of Agriculture Plastic Waste , 2016 .

[28]  Giuliano Vox,et al.  Effects of agrochemicals, ultra violet stabilisers and solar radiation on the radiometric properties of greenhouse films , 2013 .

[29]  A. Novelli,et al.  Combining ad hoc spectral indices based on LANDSAT-8 OLI/TIRS sensor data for the detection of plastic cover vineyard , 2015 .

[30]  Giuliano Vox,et al.  Effects of Solar Radiation on the Radiometric Properties of Biodegradable Films for Agricultural Applications , 2004 .

[31]  Le Yu,et al.  Towards automatic lithological classification from remote sensing data using support vector machines , 2010, Comput. Geosci..

[32]  Demetres Briassoulis,et al.  Review, mapping and analysis of the agricultural plastic waste generation and consolidation in Europe , 2013, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[33]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[34]  Kadim Tasdemir,et al.  Unsupervised extraction of greenhouses using WorldView-2 images , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[35]  Robert E. Dvorak,et al.  Plastics recycling: challenges and opportunities , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[36]  Abderrahim Nemmaoui,et al.  Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain) , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[37]  D. Waaijenberg,et al.  Mechanical Properties of Covering Materials for Greenhouses: Part 1, General Overview , 1997 .

[38]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[39]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[40]  Paul M. Mather,et al.  Support vector machines for classification in remote sensing , 2005 .

[41]  Dilek Koc-San,et al.  Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery , 2013 .

[42]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[43]  Hamid Reza Moradi,et al.  COMPARISON OF DIFFERENT CLASSIFICATION ALGORITHMS IN SATELLITE IMAGERY TO PRODUCE LAND USE MAPS (CASE STUDY: NOOR CITY) , 2014 .

[44]  Carlos González-Sánchez,et al.  Use of residual agricultural plastics and cellulose fibers for obtaining sustainable eco-composites prevents waste generation , 2014 .

[45]  D Briassoulis,et al.  Technical specifications for mechanical recycling of agricultural plastic waste. , 2013, Waste management.

[46]  Beniamino Murgante,et al.  Evaluation of urban sprawl from space using open source technologies , 2015, Ecol. Informatics.

[47]  Sergio Castellano,et al.  Test results and empirical correlations to account for air permeability of agricultural nets , 2016 .

[48]  Baharin Bin Ahmad,et al.  Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia , 2014 .

[49]  Pietro Picuno,et al.  Analysis of plasticulture landscapes in Southern Italy through remote sensing and solid modelling techniques , 2011 .

[50]  Raffaella Taddeo,et al.  The potential of Industrial Ecology in agri-food clusters (AFCs): A case study based on valorisation of auxiliary materials , 2015 .

[51]  Manuel A. Aguilar,et al.  Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery , 2014, Remote. Sens..

[52]  Demetres Briassoulis,et al.  Review Paper (SE—Structures and Environment): Radiometric and Thermal Properties of, and Testing Methods for, Greenhouse Covering Materials , 2000 .

[53]  Francisco Ayuga,et al.  A method for detecting and describing land use transformations: An examination of Madrid’s southern urban–rural gradient between 1990 and 2006 , 2014 .

[54]  Demetres Briassoulis,et al.  Mechanical Properties of Covering Materials for Greenhouses Part 2: Quality Assessment , 1997 .

[55]  Giacomo Scarascia Mugnozza,et al.  Georeferencing of agricultural plastic waste , 2016 .

[56]  Giuliano Vox,et al.  Effects of different plastic sheet coverings on microclimate and berry ripening of table grape cv "Matilde" , 2000 .

[57]  Sukumar Bandopadhyay,et al.  An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing , 2008 .

[58]  Claudia Arcidiacono,et al.  A model to manage crop-shelter spatial development by multi-temporal coverage analysis and spatial indicators , 2010 .

[59]  Giuliano Vox,et al.  Evaluation of the radiometric properties of roofing materials for livestock buildings and their effect on the surface temperature , 2016 .

[60]  Manuel A. Aguilar,et al.  Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain , 2015, Remote. Sens..