Derivación indirecta de la distribución espacial y estado de desarrollo de los bosques secundarios en Costa Rica usando imágenes satelitales de mediana resolución espacial. Documento I

Proyecto de Investigación (Código: 1401-0077) Instituto Tecnológico De Costa Rica. Vicerrectoría De Investigación Y Extensión (VIE). Dirección de Proyectos. Escuela de Ingeniería Forestal. Centro de Investigación en Innovación Forestal (CIF), 2020El objetivo de la presente investigación fue desarrollar una técnica indirecta para conocer la distribución espacial y estado de desarrollo de bosques secundarios usando sensores remotos de mediana resolución. La metodología fue probada en un sector de la Zona Huetar Norte de Costa Rica. Se realizó la clasificación de imágenes satelitales Landsat y Sentinel-2 de diferentes años: 2000, 2005, 2010 y 2017/2018. Se comparó la ubicación de las masas boscosas por año y se efectuó una reclasificación de las áreas donde se presentó ganancia de cobertura arbórea (bosques nuevos) identificándose cuáles de estas representan bosques secundarios. Se validaron los resultados y las exactitudes obtenidas se utilizaron como variables respuesta de un ANOVA. El método OBIA supera a MLC en la identificación de los bosques nuevos (pvalue=0,035). El mes de la imagen influye en la exactitud del productor ya que produce una interacción con el método (pvalue= 0,027) y con el tipo de imagen (pvalue= 0,008). También se produce una interacción mes-imagen para la exactitud del usuario (pvalue= 0,042) y la exactitud general de la clase de bosques secundarios (pvalue= 0,012). La tendencia muestra que el mejor método para cuantificar y ubicar los bosques secundarios es la clasificación de imágenes Sentinel-2 de los meses de junio y julio mediante Análisis Basado en Objetos (OBIA).The objective of the present investigation was to develop an indirect technique to know the spatial distribution and development status of secondary forests using medium resolution remote sensors. The methodology was tested in a sector of the Northern Huetar Zone of Costa Rica. Landsat and Sentinel-2 images were classified for different years: 2000, 2005, 2010, 2017, and 2018. The location of the forest stands per year was compared and a reclassification of the areas where there was a gain in tree cover (new forests) was carried out, identifying which of these represent secondary forests. Results were validated and the accuracies obtained were used as ANOVA response variables. The OBIA method outperforms MLC in identifying new forests (pvalue = 0.035). The month of the image influences the accuracy of the producer since it produces an interaction with the method (pvalue = 0.027) and with the image type (pvalue = 0.008). A month-image interaction also occurs for user accuracy (pvalue = 0.042) and overall accuracy of secondary forest class (pvalue = 0.012). The trend shows that the best method to quantify and locate secondary forests is the classification of Sentinel-2 images for the months of June and July using Object Based Analysis (OBIA)

Evaluation of four classification algorithms of Landsat-8 and Sentinel-2 satellite images to identify forest cover in highly fragmented regions in Costa Rica

[EN] Mapping of land use and forest cover and assessing their changes is essential in the design of strategies to manage and preserve the natural resources of a country, and remote sensing have been extensively used with this purpose. By comparing four classification algorithms and two types of satellite images, the objective of the research was to identify the type of algorithm and satellite image that allows higher global accuracy in the identification of forest cover in highly fragmented landscapes. The study included a treatment arrangement with three factors and six randomly selected blocks within the Huetar Norte Zone in Costa Rica. More accurate results were obtained for classifications based on Sentinel-2 images compared to Landsat-8 images. The best classification algorithms were Maximum Likelihood, Support Vector Machine or Neural Networks, and they yield better results than Minimum Distance Classification. There was no interaction among image type and classification methods, therefore, Sentinel-2 images can be used with any of the three best algorithms, but the best result was the combination of Sentinel-2 and Support Vector Machine. An additional factor included in the study was the image acquisition date. There were significant differences among months during which the image was acquired and an interaction between the classification algorithm and this factor was detected. The best results correspond to images obtained in April, and the lower to September, month that corresponds with the period of higher rainfall in the region studied. The higher global accuracy identifying forest cover is obtained with Sentinel-2 images from the dry season in combination with Maximum Likelihood, Support Vector Machine, and Neural Network image classification methods.[ES] Conocer y cartografiar los cambios del uso y cobertura de la tierra es esencial para la formulación de estrategias de manejo y conservación de los recursos naturales. Las herramientas que conforman la disciplina de la teledetección han sido extensamente usadas con este objetivo. Al comparar cuatro algoritmos de clasificación y dos tipos de imágenes satelitales, el objetivo de la investigación fue determinar el tipo de algoritmo e imagen satelital que permite obtener una mayor fiabilidad global en la identificación de la cobertura boscosa en paisajes de uso de la tierra con alta fragmentación. El estudio se desarrolló en la Zona Huetar Norte de Costa Rica, utilizando un diseño experimental de seis bloques con un arreglo de tratamientos con tres factores. El uso de imágenes Sentinel-2 fue superior al obtenido con Landsat-8. No existen diferencias significativas en la fiabilidad lograda con los algoritmos de clasificación de Máxima Verosimilitud, Máquinas de Vectores Soporte y Redes Neuronales, pero sí de estos con respecto a la clasificación por Mínima Distancia. No se detectó interacción entre tipo de imagen y algoritmo de clasificación, por lo que las imágenes de Sentinel-2 podrían usarse con cualquiera de los tres mejores algoritmos estudiados. Se analizó además el efecto que tuvo el mes en cada imagen adquirida, y se encontraron diferencias significativas debido a este factor, además se produce una interacción de este con el método de clasificación. Los mejores resultados se obtuvieron con imágenes de abril, y los más bajos con imágenes de septiembre, mes que coincide con la época lluviosa en la zona estudiada. Se concluye que la mayor fiabilidad en la identificación de la cobertura boscosa se logra mediante el uso de los algoritmos de Máxima Verosimilitud, Máquinas de Vectores Soporte y Redes Neuronales empleando imágenes Sentinel-2 tomadas en la temporada seca.Los autores agradecen a la Vice-Rectoría de Investigación y Extensión del ITCR por el apoyo financiero y administrativo para la realización del proyecto: Derivación indirecta de la distribución espacial y estado de desarrollo de los bosques secundarios en Costa Rica usando imágenes satelitales de mediana resolución espacial. Igualmente se agradece al programa de becas CeNAT-CONARE y al laboratorio PRIAS del Centro Nacional de Alta Tecnología (CeNAT) de Costa Rica por la facilitación de los equipos de cómputo de avanzada y el uso de las licencias de los softwares requeridos para llevar a cabo esta investigación.Ávila-Pérez, I.; Ortiz-Malavassi, E.; Soto-Montoya, C.; Vargas-Solano, Y.; Aguilar-Arias, H.; Miller-Granados, C. (2020). Evaluación de cuatro algoritmos de clasificación de imágenes satelitales Landsat-8 y Sentinel-2 para la identificación de cobertura boscosa en paisajes altamente fragmentados en Costa Rica. Revista de Teledetección. 0(57):37-49. https://doi.org/10.4995/raet.2020.13340OJS374905

Emprego de séries temporais na Amazônia : análise de imagens MODIS e RADAR para mapeamento de uso e ocupação do solo no Estado do Acre

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2019.Objetivou-se avaliar metodologias para trabalhar séries temporais na Amazônia, em ambiente com alta frequência de nuvens. A região amazônica abriga grande biodiversidade ambiental e diversidade sociocultural, justificando a necessidade de estudos para acompanhar a dinâmica de uso e ocupação do solo, bem como realizar o monitoramento dos recursos ambientais. Para tanto, foram desenvolvidos 3 capítulos em formato de artigos. No primeiro artigo, avaliou-se a quantidade ideal de imagens para uma série temporal visando melhores resultados de classificação de uso e ocupação do solo. Observou-se que os cubos temporais precisam ter imagens claras para atingir os melhores resultados das classificações. A presença de imagens com nuvem resulta em classificações com baixo índice kappa. Dentre os classificadores utilizados, o Mínima Distância foi o que se apresentou menos sensível à presença de nuvens. No segundo artigo, foram avaliadas técnicas de composição afim de encontrar uma síntese que abrangesse os períodos secos e chuvosos na região. Observou-se que intervalos regulares de composição não foram suficientes para obtenção de imagens livres de nuvem ao longo do ano. Intervalos irregulares (personalizados) de composição podem trazer um maior número de dados ao pesquisador, sobretudo, em ambientes com alta frequência de cobertura de nuvem. Dentre os métodos síntese testados, o de Máximo NDVI e o de Mediana apresentaram os melhores resultados. A restrição do ângulo do sensor zênite levou a composições mais limpas, ou seja, menos influenciadas por fatores geométricos e atmosféricos. No terceiro artigo, utilizamos uma série temporal de imagens de radar, avaliando sua capacidade de identificar alvos como o cultivo da cana-de-açúcar. Dentre os testes realizados, aquele que combinou dados de radar e óticos na série temporal, apresentou os melhores resultados. Foi possível identificar a cana-de-açúcar, com mais de 50% de acertos na maioria dos testes realizados. Concluímos que as análises com dados orbitais para a classificação de séries temporais na Amazônia, são possíveis, embora as séries temporais necessitem ter baixa influência de cobertura de nuvem. As possibilidades que se apresentam com as imagens de radar Sentinel são grandes considerando o volume de dados disponíveis, com boa resolução temporal e espacial.This study aimed to evaluate methodologies for working time series in the Amazon, in a high cloud frequency environment. The Amazon region is home to great environmental biodiversity and sociocultural diversity, justifying the need for studies to follow the dynamics of land use and occupation, as well as monitoring environmental resources. For that, 3 chapters were developed in paper format. In the first paper, the ideal amount of images for a temporal series was evaluated aiming at better results of classification of use and occupation of the soil. It was observed that temporal cubes need to have clear images to achieve the best results of the classifications. The presence of cloud images results in ratings with a low kappa index. Among the classifiers used, the Minimum Distance was the one that was less sensitive to the presence of clouds in the time series. In the second paper, image composition techniques were evaluated in order to find a synthesis that covered the dry and rainy periods in the region. It was observed that regular composition intervals were not enough to obtain cloud-free images throughout the year. Irregular (custom) composition intervals can bring a greater amount of data to the researcher, especially in environments with high frequency cloud coverage. Among the synthesis methods tested, Maximum NDVI and Median presented the best results. Restriction of the zenith sensor angle has led to cleaner compositions that is lessinfluenced by geometric and atmospheric factors. In the third article, we used a time series of radar images, evaluating their ability to identify targets such as sugarcane cultivation. Among the tests performed, the one that combined radar and optical data in the time series presented the best results. It was possible to identify the sugarcane, with more than 50% of correct answers in most of the tests performed. We conclude that the analyzes with orbital data for the classification of time series in the Amazon, are possible, although the time series need to have low influence of cloud coverage. The possibilities presented with Sentinel radar images are large considering the volume of data available, with good temporal and spatial resolution

Exploring Locational Criteria to Optimise Biofuel Production Potential in Nigeria

Energy is one of the important building blocks of any economy and the sustainability of its supply is crucial. Renewable energy sources are being explored with the objective of harnessing their potential to address demand shortages and provide sustainable clean energy. Biofuels, as one of these renewables, continue to expand and their share in global energy consumption continues to increase. Apart from lower net carbon emissions compared to fossil fuels and their role as transitional fuel sources in global shift towards renewable energy, biofuels offer other benefits such as increasing the volume of liquid fuels, improving air quality, expanding trade, import substitution and energy diversification. Therefore, there are strong environmental and economic arguments for the Nigerian Government to embark on deployment of renewable energy, including biofuels. Despite abundant biomass resources, biofuel programmes have not been fully operationalised in the country, partly because biofuels vary in their favourability profiles which depend on local conditions and practices, as well as spatial conflicts between land designed for energy production and other land uses such as agriculture or nature reserves. Consequently, there is a need for robust and detailed approaches to this location-related problem. Although Spatial Multi-criteria Analysis (SMCA) as a support tool has been applied to biofuel production analysis, accounting for multiple stakeholder opinions has been one of the major challenges. In Nigeria, there have been few attempts to apply spatial analysis to locational problems related to biofuel production. In addition, these studies are limited in terms of scope, were based on feedstock other than energy crops, and provided superficial analysis of suitability of the identified sites. The goal of this thesis was to show how to improve the robustness and transparency of spatial analysis in Nigeria through answering some spatial questions about biofuel production, which extends our knowledge of GIS and is relevant to practice. Robustness implies detailed exploration of the required environmental criteria and incorporation of the expert decisions on the criteria preferences. This work transparently demonstrates detailed application of the combined geospatial and multi-criteria methods to make the academic contribution transferable. The technical goal of the work was to conduct spatial optimisation for biofuel production in the country through detailed assessment of environmental criteria, modelling land suitability for cultivating sweet sorghum, sugarcane, cassava, oil palm and jatropha as biofuel crops in Nigeria and modelling optimal sites for biofuel processing and/or blending. This will provide support for spatial decisions regarding establishing biofuel processing plants or expanding the existing ones. Analytical Hierarchy Process (pairwise comparison) was adopted as the multi-criteria analysis method due to its robustness regarding stakeholder inclusion. Weighted overlay was adopted as method of land suitability modelling and supply area modelling was adopted as the method of site optimisation. The analysis showed that northcentral geo-political zone of Nigeria has the largest areas of land that is very suitable for cultivating sugarcane, cassava, oil palm and jatropha, while northeast has the largest areas of land that is very suitable for cultivating sweet sorghum. Based on these, three sizes of service area were considered assuming worst, average and highest crop yields scenarios to optimise processing/blending sites. Existing petroleum depots were considered as the candidate sites. Ilorin petroleum depot was found to be the most optimal location for processing/blending biofuel in Nigeria based on all the crop yields scenarios, within 300 km service area. However, assuming worst case yields scenario within 100 km service area, Maiduguri depot was found to be the best location for sweet sorghum and sugarcane biofuel processing/blending, but Yola depot was suggested as replacement for sugarcane. Ibadan was found to be the best for oil palm and jatropha, but Ikot Abasi depot was suggested as replacement for oil palm. Aba was found to be the best for cassava, but Makurdi was suggested as replacement. This work had demonstrated how robust integration of GIS tools with MCDM techniques could improve the effectiveness of spatial decision-making process regarding positioning biofuel production in developing countries like Nigeria. It is therefore concluded that this work will serve as a point of reference for state-of-the-art application of spatial multi-criteria evaluation analysis, not only for the biofuel industry, but also for other sectors of environmental management such as river basin management, land use or settlement planning. The tendency of a biofuel programme in Nigeria to succeed would greatly be enhanced by adopting sustainability strategies along its value chain through climate smart agriculture, designing and/or adopting a suitable feedstock supply model, effective land use management, realigning policy objectives, enforcing policy directives and balancing between strong and weak sustainability strategies. This will create a conducive environment for stimulating biofuel programme, delivering energy source diversification, economic growth and sustainable development for Nigeria

Exploring Locational Criteria to Optimise Biofuel Production Potential in Nigeria

Energy is one of the important building blocks of any economy and the sustainability of its supply is crucial. Renewable energy sources are being explored with the objective of harnessing their potential to address demand shortages and provide sustainable clean energy. Biofuels, as one of these renewables, continue to expand and their share in global energy consumption continues to increase. Apart from lower net carbon emissions compared to fossil fuels and their role as transitional fuel sources in global shift towards renewable energy, biofuels offer other benefits such as increasing the volume of liquid fuels, improving air quality, expanding trade, import substitution and energy diversification. Therefore, there are strong environmental and economic arguments for the Nigerian Government to embark on deployment of renewable energy, including biofuels. Despite abundant biomass resources, biofuel programmes have not been fully operationalised in the country, partly because biofuels vary in their favourability profiles which depend on local conditions and practices, as well as spatial conflicts between land designed for energy production and other land uses such as agriculture or nature reserves. Consequently, there is a need for robust and detailed approaches to this location-related problem. Although Spatial Multi-criteria Analysis (SMCA) as a support tool has been applied to biofuel production analysis, accounting for multiple stakeholder opinions has been one of the major challenges. In Nigeria, there have been few attempts to apply spatial analysis to locational problems related to biofuel production. In addition, these studies are limited in terms of scope, were based on feedstock other than energy crops, and provided superficial analysis of suitability of the identified sites. The goal of this thesis was to show how to improve the robustness and transparency of spatial analysis in Nigeria through answering some spatial questions about biofuel production, which extends our knowledge of GIS and is relevant to practice. Robustness implies detailed exploration of the required environmental criteria and incorporation of the expert decisions on the criteria preferences. This work transparently demonstrates detailed application of the combined geospatial and multi-criteria methods to make the academic contribution transferable. The technical goal of the work was to conduct spatial optimisation for biofuel production in the country through detailed assessment of environmental criteria, modelling land suitability for cultivating sweet sorghum, sugarcane, cassava, oil palm and jatropha as biofuel crops in Nigeria and modelling optimal sites for biofuel processing and/or blending. This will provide support for spatial decisions regarding establishing biofuel processing plants or expanding the existing ones. Analytical Hierarchy Process (pairwise comparison) was adopted as the multi-criteria analysis method due to its robustness regarding stakeholder inclusion. Weighted overlay was adopted as method of land suitability modelling and supply area modelling was adopted as the method of site optimisation. The analysis showed that northcentral geo-political zone of Nigeria has the largest areas of land that is very suitable for cultivating sugarcane, cassava, oil palm and jatropha, while northeast has the largest areas of land that is very suitable for cultivating sweet sorghum. Based on these, three sizes of service area were considered assuming worst, average and highest crop yields scenarios to optimise processing/blending sites. Existing petroleum depots were considered as the candidate sites. Ilorin petroleum depot was found to be the most optimal location for processing/blending biofuel in Nigeria based on all the crop yields scenarios, within 300 km service area. However, assuming worst case yields scenario within 100 km service area, Maiduguri depot was found to be the best location for sweet sorghum and sugarcane biofuel processing/blending, but Yola depot was suggested as replacement for sugarcane. Ibadan was found to be the best for oil palm and jatropha, but Ikot Abasi depot was suggested as replacement for oil palm. Aba was found to be the best for cassava, but Makurdi was suggested as replacement. This work had demonstrated how robust integration of GIS tools with MCDM techniques could improve the effectiveness of spatial decision-making process regarding positioning biofuel production in developing countries like Nigeria. It is therefore concluded that this work will serve as a point of reference for state-of-the-art application of spatial multi-criteria evaluation analysis, not only for the biofuel industry, but also for other sectors of environmental management such as river basin management, land use or settlement planning. The tendency of a biofuel programme in Nigeria to succeed would greatly be enhanced by adopting sustainability strategies along its value chain through climate smart agriculture, designing and/or adopting a suitable feedstock supply model, effective land use management, realigning policy objectives, enforcing policy directives and balancing between strong and weak sustainability strategies. This will create a conducive environment for stimulating biofuel programme, delivering energy source diversification, economic growth and sustainable development for Nigeria