Geotechnologies applied in digital soil mapping
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/11/11140/tde-11012021-161813/ |
Resumo: | The civilisation lives in a world of maps and soil maps are vital at regional and farm levels to achieve best management agricultural practices. Soil is the substrate for plant growth and vital to the fulfilment of the food demand. However, the cartographic scale of those soil maps, which for the best management agricultural practice (BMAP) have to be the most detailed as possible and they are scarce. The Digital Soil Mapping (DSM) became the easiest and feasible approach to achieve such demand. Despite previous studies have tried to better characterise soil depths, there is space for improvements on its dynamics and mapping. Looking at this goal, Remote Sensing (RS) technologies have proven to be a great power on this task. Nevertheless, some aspects of that approach still need to be tested using another hybrid, stochastic, and deterministic models for the predictions of magnetic susceptibility (MS) and soil attributes at surface and subsurface. Therefore, chapter 1 presents the evaluation of nine machine learning algorithms (MLAs) to predict the free iron content at the soil surface (e.g. 0 - 20 cm) using the DSM framework. Based on the best performance of those nine MLAs, we selected five MLAs. Chapter 2 shows the use of those five MLAs with usual and new environmental variables (e.g. DEM, drainage network, and soil spectroscopy) to predict the MS and soil attributes up to 100 cm depth. Attempts on quantifying soil mineral consist of having an observation measured using traditional laboratory soil analysis. However, developments in interpreting and analysing the visible and near-infrared (VNIR) diffuse reflectance have allowed quantifying some soil minerals. In chapter 3, it implements a novel framework using VNIR spectroscopy to quantify the main soil minerals and evaluates the application of digital soil mapping framework to spatialise those soil minerals. Last but not least, the chapter 4 presents the novelty of using all predict soil components as predictors of the soil mapping units in the region of Piracicaba-SP at farm scale (1:20,000), generating the first detailed digital soil map of the region. Additionally in this chapter, it was created the digital yield environmental map for sugarcane production. Thus, this thesis presents a new integrative framework to achieve detail soil maps for the BMAP and serves as a guide for future soil surveys across the world. |
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Geotechnologies applied in digital soil mappingGeotecnologias aplicadas no mapeamento digital de solosDigital soil mappingEspectroscopiaMapeamento digital de solosMineralogiaMineralogyPedometriaPedometricsRemote sensingSensoriamento remotoSoil spectroscopyThe civilisation lives in a world of maps and soil maps are vital at regional and farm levels to achieve best management agricultural practices. Soil is the substrate for plant growth and vital to the fulfilment of the food demand. However, the cartographic scale of those soil maps, which for the best management agricultural practice (BMAP) have to be the most detailed as possible and they are scarce. The Digital Soil Mapping (DSM) became the easiest and feasible approach to achieve such demand. Despite previous studies have tried to better characterise soil depths, there is space for improvements on its dynamics and mapping. Looking at this goal, Remote Sensing (RS) technologies have proven to be a great power on this task. Nevertheless, some aspects of that approach still need to be tested using another hybrid, stochastic, and deterministic models for the predictions of magnetic susceptibility (MS) and soil attributes at surface and subsurface. Therefore, chapter 1 presents the evaluation of nine machine learning algorithms (MLAs) to predict the free iron content at the soil surface (e.g. 0 - 20 cm) using the DSM framework. Based on the best performance of those nine MLAs, we selected five MLAs. Chapter 2 shows the use of those five MLAs with usual and new environmental variables (e.g. DEM, drainage network, and soil spectroscopy) to predict the MS and soil attributes up to 100 cm depth. Attempts on quantifying soil mineral consist of having an observation measured using traditional laboratory soil analysis. However, developments in interpreting and analysing the visible and near-infrared (VNIR) diffuse reflectance have allowed quantifying some soil minerals. In chapter 3, it implements a novel framework using VNIR spectroscopy to quantify the main soil minerals and evaluates the application of digital soil mapping framework to spatialise those soil minerals. Last but not least, the chapter 4 presents the novelty of using all predict soil components as predictors of the soil mapping units in the region of Piracicaba-SP at farm scale (1:20,000), generating the first detailed digital soil map of the region. Additionally in this chapter, it was created the digital yield environmental map for sugarcane production. Thus, this thesis presents a new integrative framework to achieve detail soil maps for the BMAP and serves as a guide for future soil surveys across the world.A civilização vive em um mundo de mapas os quais são vitais em nível regional e local para o delineamento das melhores práticas agrícolas. O solo é o substrato para o crescimento das plantas e, portanto, fundamental para atender a demanda alimentar. No entanto, a escala cartográfica desses mapas de solos, que para a melhor prática agrícola de manejo (MPAM), tem que ser a mais detalhada possível os quais atualmente são escassos. O Mapeamento Digital de Solos (MDS) tornou-se a abordagem mais fácil e viável para atingir essa demanda. Apesar de estudos anteriores terem tentado caracterizar melhor as profundidades do solo, há espaço para aperfeiçoamento em sua dinâmica e mapeamento. Tendo como foco este objetivo, as tecnologias de Sensoriamento Remoto (SR) provam ser uma grande ferramenta nesta tarefa. No entanto, alguns aspectos dessa abordagem ainda precisam ser testados usando outros modelos híbridos, estocásticos e determinísticos para as previsões de susceptibilidade magnética (SM) e atributos do solo na superfície e subsuperfície. Portanto, o capítulo 1 apresenta a avaliação de nove algoritmos de aprendizado de máquinas (AAMs) para predizer o teor de ferro livre na superfície do solo (0 - 20 cm) usando a estrutura do MDS. Com base no melhor desempenho desses nove AAMs, selecionamos cinco. O capítulo 2 mostra o uso desses cinco AAMs com variáveis ambientais usuais e novas (por exemplo, DEM, rede de drenagem e espectroscopia do solo) para predizer SM e atributos do solo até 100 cm de profundidade. Quanto a mineralogia dos solos, a quantificação dos minerais do solo atualmente consistem na análise de laboratório tradicional de solos. No entanto, desenvolvimentos na interpretação e análise da refletância difusa do visível e do infravermelho próximo (VNIR) permitem quantificar alguns dos minerais do solo. No capítulo 3, implementa-se uma nova metodologia usando espectroscopia VNIR para quantificar os principais minerais do solo e avalia a aplicação da estrutura de mapeamento digital do solo para espacializar esses minerais. Por fim, mas não menos importante, o capítulo 4 apresenta a inovação de usar todos os componentes do solo como preditores das unidades de mapeamento de solo na região de Piracicaba-SP em escala de fazenda (1: 20.000), gerando o primeiro mapa digital detalhado do solo da região. Adicionalmente neste capítulo, foi criado o mapa digital de ambientes de produção para cana-de-açúcar. Assim, esta tese apresenta uma nova estrutura metodológica e integrativa na obtenção de mapas digitais de solo em escala detalhada para a MPAM e serve como guia para futuros levantamentos de solos em todo o mundo.Biblioteca Digitais de Teses e Dissertações da USPDematte, Jose Alexandre MeloMendes, Wanderson de Sousa2020-11-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/11/11140/tde-11012021-161813/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2021-01-13T17:31:01Zoai:teses.usp.br:tde-11012021-161813Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212021-01-13T17:31:01Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Geotechnologies applied in digital soil mapping Geotecnologias aplicadas no mapeamento digital de solos |
title |
Geotechnologies applied in digital soil mapping |
spellingShingle |
Geotechnologies applied in digital soil mapping Mendes, Wanderson de Sousa Digital soil mapping Espectroscopia Mapeamento digital de solos Mineralogia Mineralogy Pedometria Pedometrics Remote sensing Sensoriamento remoto Soil spectroscopy |
title_short |
Geotechnologies applied in digital soil mapping |
title_full |
Geotechnologies applied in digital soil mapping |
title_fullStr |
Geotechnologies applied in digital soil mapping |
title_full_unstemmed |
Geotechnologies applied in digital soil mapping |
title_sort |
Geotechnologies applied in digital soil mapping |
author |
Mendes, Wanderson de Sousa |
author_facet |
Mendes, Wanderson de Sousa |
author_role |
author |
dc.contributor.none.fl_str_mv |
Dematte, Jose Alexandre Melo |
dc.contributor.author.fl_str_mv |
Mendes, Wanderson de Sousa |
dc.subject.por.fl_str_mv |
Digital soil mapping Espectroscopia Mapeamento digital de solos Mineralogia Mineralogy Pedometria Pedometrics Remote sensing Sensoriamento remoto Soil spectroscopy |
topic |
Digital soil mapping Espectroscopia Mapeamento digital de solos Mineralogia Mineralogy Pedometria Pedometrics Remote sensing Sensoriamento remoto Soil spectroscopy |
description |
The civilisation lives in a world of maps and soil maps are vital at regional and farm levels to achieve best management agricultural practices. Soil is the substrate for plant growth and vital to the fulfilment of the food demand. However, the cartographic scale of those soil maps, which for the best management agricultural practice (BMAP) have to be the most detailed as possible and they are scarce. The Digital Soil Mapping (DSM) became the easiest and feasible approach to achieve such demand. Despite previous studies have tried to better characterise soil depths, there is space for improvements on its dynamics and mapping. Looking at this goal, Remote Sensing (RS) technologies have proven to be a great power on this task. Nevertheless, some aspects of that approach still need to be tested using another hybrid, stochastic, and deterministic models for the predictions of magnetic susceptibility (MS) and soil attributes at surface and subsurface. Therefore, chapter 1 presents the evaluation of nine machine learning algorithms (MLAs) to predict the free iron content at the soil surface (e.g. 0 - 20 cm) using the DSM framework. Based on the best performance of those nine MLAs, we selected five MLAs. Chapter 2 shows the use of those five MLAs with usual and new environmental variables (e.g. DEM, drainage network, and soil spectroscopy) to predict the MS and soil attributes up to 100 cm depth. Attempts on quantifying soil mineral consist of having an observation measured using traditional laboratory soil analysis. However, developments in interpreting and analysing the visible and near-infrared (VNIR) diffuse reflectance have allowed quantifying some soil minerals. In chapter 3, it implements a novel framework using VNIR spectroscopy to quantify the main soil minerals and evaluates the application of digital soil mapping framework to spatialise those soil minerals. Last but not least, the chapter 4 presents the novelty of using all predict soil components as predictors of the soil mapping units in the region of Piracicaba-SP at farm scale (1:20,000), generating the first detailed digital soil map of the region. Additionally in this chapter, it was created the digital yield environmental map for sugarcane production. Thus, this thesis presents a new integrative framework to achieve detail soil maps for the BMAP and serves as a guide for future soil surveys across the world. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-16 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/11/11140/tde-11012021-161813/ |
url |
https://www.teses.usp.br/teses/disponiveis/11/11140/tde-11012021-161813/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
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1815256851854393344 |