Cartografía de bosques de manglar mediante imágenes de sensores remotos: estudio de caso Buenaventura, Colombia

Abstract

Resumen: Los manglares son comunidades vegetales de alta importancia ecológica y económica para las regiones costeras. La presente investigación proporciona un método para determinar la cartografía de bosques manglar mediante imágenes de sensores remotos a escala 1:25.000 en un sector del municipio de Buenaventura, Colombia; para la cartografía de bosques de manglar se empleó una imagen satelital Sentinel 2 y una ortofotografía de alta resolución del año 2017; usando el software libre QGIS, se realizó los análisis espectrales, se estableció una clasificación supervisada mediante el algoritmo de máxima verosimilitud. Los resultados obtenidos muestran que la cobertura de manglar es la de mayor representatividad en el área de estudio con una extensión total de 7.264,21 ha (59,21% del área total), la clasificación desarrollada presentó una exactitud temática global de 80% e índice de Kappa de 0,70. El método empleado sirve como un referente sobre la cartografía de bosques de manglar en el mundo.Abstract: Mangroves are plant communities of high ecological and economic importance for coastal regions. This investigation provides a methodology for mapping Mangrove forests through remote sensing images in a semidetail scale (1:25,000) in a sector of the municipality of Buenaventura, Colombia. A Sentinel 2 image and 2017 highresolution ortophotomosaic of the municipality were used for the mangrove cartography, using QGIS software, spectral analysis was performed and supervised classification was established using Maximum Likelihood algorithm. Results shown that mangrove is the most representative cover in the study area whit 7,264.21 ha in total extension (59.21% of total area), the development classification got a thematic accuracy of 80% and 0.70 in Kappa index. The used methodology can be used as an academic and research reference for mangrove semi-detail mapping in the world.

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

“…The first one is to determine the quantitative relationship between mangrove monitoring indicators and characteristic bands to achieve the extraction of nutrient content. Through processing the spectral data, including itself and its 23 kinds of transformation data, three band combination algorithms of band difference, band ratio, and band difference and ratio are tested to form a controllable machine learning model package [12,26,39]. They are used to conduct large-scale training and learning on mangrove component content and hyperspectral data, and extract information from statistical significance.…”

“…Recently, hyperspectral sensors based on satellites, aircraft, unmanned aerial vehicles, and the ground have emerged endlessly, and have been increasingly introduced into mangrove ecological assessment [12,21,42,44,47]. The traditional sampling and analysis technology has been unable to meet the needs of digital applications due to the special growth environment of mangroves, and the intervention of new technologies is urgently needed [14].…”

“…To explore the response mechanism of mangrove leaf area index and main nutrient content in the overall framework, firstly, multi-source data acquisition and pre-processing are carried out, in combination with monitoring indicators such as leaf area index, mangrove canopy leaf water content, chlorophyll a, chlorophyll b, total nitrogen, total phosphorus, total potassium, etc., to analyze the mangrove canopy spectral response characteristics of different species composition and abundance in the study area, and to construct the extraction method of mangrove canopy species end elements; secondly, the characteristic bands of mangrove evaluation indicators are screened, and the hyperspectral inversion of mangrove monitoring indicators is carried out by using various linear and nonlinear methods, and the best model is selected; finally, the mapping of hyperspectral retrieval results of mangrove canopy indicators was completed. The research results provide basic data and technical support for mangrove ecological remote sensing monitoring [12,28,47].…”

“…The traditional method is based on the idea of threshold; a mangrove index is established using Sentinel-2 image to achieve accurate extraction of submerged mangroves in view of the fact that mangroves will be flooded by tidal water, rainy climate, and other adverse factors [9,10]. Additionally, the maximum likelihood classification technology of remote sensing images is introduced to accurately distinguish mangroves [11][12][13]. With the improvement in data resolution, the satellite data with high spatial resolution can accurately extract the boundary information of mangrove from the texture under the action of a neural network algorithm [14,15].…”

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“…La reflectancia de la banda infrarroja de 1998 y 2014 varió con respecto a la de 2017 entre el 12,6 y 17,9 %, respectivamente; sin embargo, estos valores se encuentran dentro del rango reportado por autores como Mondal, Trzaska y De Sherbinin (2018) y Perea-Ardila, Oviedo-Barrero y Leal-Villamil (2019). Por otra parte, el MAB para 1998 presentó un promedio de reflectancia en la banda infrarroja del 9 %, valor que es cercano al reportado por Vaghela y col. (2018) para manglares abiertos de la India.…”