Evaluation of Desertification Processes in Ouricuri-PE Through Trend Estimates of Times Series

Sousa, Willamys Rangel Nunes de; Couto, Mailson Sousa; Castro, Angélica Félix de; Silva, Marcelino Pereira Santos

doi:10.1109/tla.2013.6502869

Cited by 7 publications

(3 citation statements)

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“…NDVI time-series are further used for deforestation or forest disturbance mapping [12][13][14][15], fire risk estimation or forecasting [16,17] as well as forest recovery monitoring [18]. Long time-series of NDVI are also important for monitoring desertification [19][20][21] and land degradation [22][23][24], as well as for monitoring drought impacts [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

et al. 2023

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Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) time-series derived thereof can be used for monitoring vegetation conditions. This study presents the novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly NDVI composites at a 1 km spatial resolution based on AVHRR data for Europe and North Africa, currently spanning the period from 1981 to 2018. After investigating temporal and spatial data availability within the TIMELINE monthly NDVI composite product, seasonal NDVI trends have been derived thereof for the period 1989–2018 to assess long-term vegetation change in Europe and northern Africa. The trend analysis reveals distinct patterns with varying NDVI trends for spring, summer and autumn for different regions in Europe. Integrating the entire growing season, the result shows positive NDVI trends for large areas within Europe that confirm and reinforce previous research. The analyses show that the TIMELINE NDVI product allows long-term vegetation dynamics to be monitored at 1 km resolution on a pan-European scale and the detection of specific regional and seasonal patterns.

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Section: Introductionmentioning

confidence: 99%

Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

et al. 2023

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“…Dharumarajan et al (2018), utilizando dados de clima, uso do solo, solos e dados socioeconômicos, criaram um índice de vulnerabilidade à desertificação para uma região da Índia utilizando um modelo de análise multivariada com resultados bem satisfatórios. Um estudo no Brasil, em Ouricuri, Pernambuco, desenvolvido para a identificação dos processos de desertificação a partir da modelagem de séries temporais de imagens de satélite Landsat (TM), usando índice de vegetação e um método de estimação de tendência de séries temporais, apresentou-se viável para a região estudada (SOUSA et al, 2013). O estado do Espírito Santo sofreu nas últimas décadas um processo acelerado de desmatamento, restando with the percentiles of 0-33.33%; 33.33-66.66%; and, 66.66-100%, respectively.…”

Section: Introductionunclassified

Avaliação Da Vulnerabilidade Socioambiental À Desertificação No Estado Do Espírito Santo, Brasil

Barreto-Neto

Marchesi

2019

Rev. Bras. Ciênc. Ambient. (Online)

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O processo de desertificação é caracterizado pela influência do clima, que emregiões secas (áridas e semiáridas) é o fator preponderante de fragilidadedo ambiente, que provoca déficits sociais e econômicos, que por sua vezretroalimentam o processo por meio da pressão da população sobre o meioambiente. O índice de vulnerabilidade socioambiental à desertificação (IVSD)foi desenvolvido em um sistema de informações geográficas utilizando dadosambientais e socioeconômicos, tais como taxa de analfabetismo, crescimentopopulacional, produto interno bruto, rendimento médio da população,precipitação, evapotranspiração, temperatura da superfície, índice de arideze monitoramento da qualidade da água. No desenvolvimento do IVSDfoi realizada uma análise estatística dos dados preparados e calculados.Essa análise consistiu na eliminação dos valores de pico e na divisão de trêsclasses por percentis (classes de vulnerabilidade alta, média e baixa), compercentis de 0 a 33,33%; 33,33 a 66,66%; e 66,66 a 100%, respectivamente. O objetivo desta pesquisa foi desenvolver e avaliar um índice paradeterminação da vulnerabilidade socioambiental à desertificação no estadodo Espírito Santo, visando identificar as regiões mais vulneráveis a esseprocesso. O IVSD evidenciou uma área localizada no norte-noroeste, que já éconsiderada oficialmente como uma área susceptível à desertificação, e umaárea localizada no extremo sul, que embora possua as mesmas características,não é considerada oficialmente uma área susceptível à desertificação.O IVSD permitiu evidenciar a necessidade de implantação e de melhorias daspolíticas públicas para promover o avanço no desenvolvimento das regiõesmais fragilizadas do Espírito Santo, visando torná-las menos vulneráveis aosprocessos de desertificação.

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“…The systematic and intelligent analyses of the remote sensing images provide a unique opportunity for understanding how, when, and where changes take place in our world. Precious information exploited from spatial repositories has been promoting benefits on many areas, such as agricultural [1,2] (forecast of harvests and soil erosion), hydric [3] (use of water resources and verification of the water quality), urban [4] (urban planning and demographic inferences), forest [5][6][7] (monitoring deforestation and biomass control), limnology [8] (characterization of aquatic vegetation and identification of water types), meteorology [9] (weather and climate studies), air traffic [10] (information for safety in the air), and national security [11] (military strategic planning of operations and missions).…”

Section: Introductionmentioning

confidence: 99%

Multiagent System for Image Mining

Freitas¹,

SantosSilva²

2017

Multi-Agent Systems

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The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases.

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Evaluation of Desertification Processes in Ouricuri-PE Through Trend Estimates of Times Series

Cited by 7 publications

References 5 publications

Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

Avaliação Da Vulnerabilidade Socioambiental À Desertificação No Estado Do Espírito Santo, Brasil

Multiagent System for Image Mining

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