Affordable Use of Satellite Imagery in Agriculture and Development Projects: Assessing the Spatial Distribution of Invasive Weeds in the UNESCO-Protected Areas of Cuba

Eduardo Moreno, Alberto Zabalo, Encarnacion Gonzalez, Reinaldo Alvarez, Victor Manuel Jimenez and Julio Menendez
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Eduardo Moreno: Faculty of Experimental Sciences, University of Huelva, Av. de las Fuerzas Armadas, S/N, 21071 Huelva, Spain
Alberto Zabalo: Departamento Ciencias Agroforestales, Escuela Tecnica Superior de Ingenieria, Universidad de Huelva, Av. de las Fuerzas Armadas, S/N, 21071 Huelva, Spain
Encarnacion Gonzalez: Faculty of Experimental Sciences, University of Huelva, Av. de las Fuerzas Armadas, S/N, 21071 Huelva, Spain
Reinaldo Alvarez: Departamento de Agronomia, Facultad de Ciencias Agropecuarias, Universidad de Sancti Spiritus “Jose Marti”, Av. de los Martires, 360, Sancti Spiritus, Cuba
Victor Manuel Jimenez: Institute of Geography, National Autonomous University of Mexico, Circuito de la Investigacion Cientifica, Ciudad Universitaria, Ciudad de Mexico 04510, Mexico
Julio Menendez: Departamento Ciencias Agroforestales, Escuela Tecnica Superior de Ingenieria, Universidad de Huelva, Av. de las Fuerzas Armadas, S/N, 21071 Huelva, Spain

Agriculture, 2021, vol. 11, issue 11, 1-17

Abstract: The effective and regular remote monitoring of agricultural activity is not always possible in developing countries because the access to cloud-based geospatial analysis platforms or expensive high-resolution satellite images are not always available. Herein, using paid high-resolution satellite images first and then free medium-resolution satellite images, we aimed to develop a cost-free, affordable method for regularly mapping the spatial distribution of sicklebush ( Dichrostachys cinerea ), an archetypal allochthonous invasive plant in Cuba that is becoming impossible to control owing to its rapid growth in areas planted with sugar cane in the Trinidad-Valle de los Ingenios area (Cuba), a UNESCO World Heritage Site. Two types of images were used (WorldView-2 and Landsat-8); these were subjected to supervised classification, with accuracy values of 88.7% and 93.7%, respectively. Vegetation cover was first derived from the purchased WorldView-2 image, and this information was then used as the training field to obtain spectral signatures from the Landsat-8 free image so that Landsat images may be regularly used to monitor D. cinerea infestations. The results obtained in the spatial distribution map for sicklebush in the Landsat-8 images had an overall reliability of 93.7% and a Kappa coefficient reliability of 91.9%. These values indicate high confidence in the results, which suggests that sicklebush has invaded 52.7% of the total 46,807.26-ha area of the Trinidad-Valle de los Ingenios. This process proved extremely effective and demonstrated the benefits of using high-resolution spatial images from which information can be transferred to free satellite images with a larger pixel size.

Keywords: sicklebush; marabou; WorldView-2; Landsat-8; supervised classification; spatial distribution (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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