Assessing and monitoring the risk of desertification in Dobrogea, Romania, using Landsat data and decision tree classifier

Abstract

The risk of the desertification of a part of Romania is increasingly evident, constituting a serious problem for the environment and the society. This article attempts to assess and monitor the risk of desertification in Dobrogea using Landsat Thematic Mapper (TM) satellite images acquired in 1987, 1994, 2000, 2007 and 2011. In order to assess the risk of desertification, we used as indicators the Modified Soil Adjustment Vegetation Index 1 (MSAVI1), the Moving Standard Deviation Index (MSDI) and the albedo, indices relating to the vegetation conditions, the landscape pattern and micrometeorology. The decision tree classifier (DTC) was also used on the basis of pre-established rules, and maps displaying six grades of desertification risk were obtained: non, very low, low, medium, high and severe. Land surface temperature (LST) was also used for the analysis. The results indicate that, according to pre-established rules for the period of 1987-2011, there are two grades of desertification risk that have an ascending trend in Dobrogea, namely very low and medium desertification. An investigation into the causes of the desertification risk revealed that high temperature is the main factor, accompanied by the destruction of forest shelterbelts and of the irrigation system and, to a smaller extent, by the fragmentation of agricultural land and the deforestation in the study area.

A data‐mining‐based approach for aeolian desertification susceptibility assessment: A case‐study from Northern China

“…Our finding agrees with previous works claiming that DT performance is acceptable for desertification assessment (Xu et al, ) and landslide susceptibility mapping (Yeon et al, ). More specifically, the C5 algorithm performs best in landslide susceptibility mapping (Braun et al, ), breast cancer survivability prediction (Delen et al, ), land cover classification (Pal & Mather, ; Vorovencii, ), and soil map production (Qi & Zhu, ) relative to other algorithms. This is partly because C5 adds a powerful boosting algorithm to improve CA by establishing the DT, which focuses on data that are wrongly classified.…”

“…In the present study, the C5 algorithm (Quinlan, 1993(Quinlan, , 2001Wu et al, 2008) was used to derive DTs from training data, because it proved to be both fast and highly accurate (e.g., Braun et al, 2015;Delen et al, 2005;Pal & Mather, 2003;Qi & Zhu, 2003;Vorovencii, 2015;Wu et al, 2008) and allowed for viewing established rules. This makes it widely applicable in various areas.…”

“…More recently, remote sensing has become an important method for monitoring and assessment of desertification susceptibility at global and regional scales, with a large number of up‐to‐date and high‐accuracy desertification detection results (De Jong, de Bruin, Schaepman, & Dent, ; Metternicht, Zinck, Blanco, & Del Valle, ; Prince, Becker‐Reshef, & Rishmawi, ; Vorovencii, ; Yue, Li, et al, , and references therein). However, remote sensing is unable to reveal the impact mechanism of desertification impact factors (e.g., soil, vegetation, and climate) on desertification.…”

Monitoramento Espaço-Temporal da Detecção de Mudanças em Vegetação de Caatinga por Sensoriamento Remoto no Semiárido Brasileiro

“…Os mapas do risco de degradação ambiental da região foram avaliados estatisticamente por matriz de confusão, pelas medidas da avaliação de precisão e coeficiente Kappa (qualidade dos mapas temáticos, Landis e Koch, 1977) através da amostragem de vários pontos (pixels aleatórios) em cada imagem para os diferentes graus do risco de degradação ambiental na região semiárida (Xu et al, 2009;Vorovencii, 2015;.…”

“…O estudo das mudanças de cobertura do solo torna-se viável quando avaliado em grandes áreas, assim, o sensoriamento remoto é uma ferramenta que através de um conjunto de técnicas junto a algoritmos permitem esse tipo de avaliação, além de um monitoramento espaço-temporal de áreas heterogêneas com alta precisão, avaliando principalmente a biomassa vegetal e o padrão de paisagem, assim como, as condições micrometeorológicas através de indicadores para detecção de mudanças na superfície. Existem diferentes aplicações disponíveis que permitem realizar previsões meteorológicas, mudanças climáticas e planejamento agroecológico, todos estes fatores essenciais para avaliar e monitorar o risco de degradação ambiental ao longo do tempo nas regiões semiáridas do Brasil (Xu et al, 2009;Bastiaanssen et al, 2010;Teixeira et al, 2014;Vorovencii, 2015;Zheng et al, 2016;.…”