Land Use/Cover Response to Rainfall Variability:  A Comparing Analysis between NDVI and EVI in the  Southwest of Burkina Faso

Zoungrana, Benewinde J.‐B.; Christopher, Conrad; Amekudzi, Leonard K.; Thiel, Michael; Da, Evariste Dapola

doi:10.3390/cli3010063

Cited by 40 publications

(29 citation statements)

References 32 publications

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“…This further buttress the observed increase in bare surface in 2011 compared to 2006 and 1999. In fact, in this region, a decrease in rainfall leads to an increase in barren land area [12], mainly due to the close relationship between rainfall variability and vegetation dynamics [56]. …”

Section: Lulcc In the Study Areamentioning

confidence: 99%

Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa

et al. 2015

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Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served OPEN ACCESSRemote Sens. 2015, 7 12077 as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92%. Natural vegetation loss was estimated to be 17.9% ± 2.5% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.

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Section: Lulcc In the Study Areamentioning

confidence: 99%

Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa

et al. 2015

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“…Identifying the seasonal variability of LULC could help in improving the assessment of water withdrawals. In most studies, the seasonal variability of land use has been studied by analyzing data on the normalized difference vegetation index (NDVI) and leaf area index (LAI) [12,13] and not by seasonal LULC mapping. Different plants or crops may have similar NDVI values and as a result, may fall in the same NDVI classes, making it difficult to differentiate between different crops that might have different water consumption rates and different water requirements [14,15].…”

Section: Introductionmentioning

confidence: 99%

Accounting for Seasonal Land Use Dynamics to Improve Estimation of Agricultural Irrigation Water Withdrawals

Msigwa

Komakech

Verbeiren

et al. 2019

Water

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The assessment of water withdrawals for irrigation is essential for managing water resources in cultivated tropical catchments. These water withdrawals vary seasonally, driven by wet and dry seasons. A land use map is one of the required inputs of hydrological models used to estimate water withdrawals in a catchment. However, land use maps provide typically static information and do not represent the hydrological seasons and related cropping seasons and practices throughout the year. Therefore, this study assesses the value of seasonal land use maps in the quantification of water withdrawals for a tropical cultivated catchment. We developed land use maps for the main seasons (long rains, dry, and short rains) for the semi-arid Kikuletwa catchment, Tanzania. Three Landsat 8 images from 2016 were used to develop seasonal land use land cover (LULC) maps: March (long rains), August (dry season), and October (short rains). Quantitative and qualitative observation data on cropping systems (reference points and questionnaires/surveys) were collected and used for the supervised classification algorithm. Land use classifications were done using 20 land use and land cover classes for the wet season image and 19 classes for the dry and short rain season images. Water withdrawals for irrigated agriculture were calculated using (1) the static land use map or (2) the three seasonal land use maps. Clear differences in land use can be seen between the dry and the other seasons and between rain-fed and irrigated areas. A difference in water withdrawals was observed when seasonal and static land use maps were used. The highest differences were obtained for irrigated mixed crops, with an estimation of 572 million m3/year when seasonal dynamic maps were used and only 90 million m3/year when a static map was used. This study concludes that detailed seasonal land use maps are essential for quantifying annual irrigation water use of catchment areas with distinct dry and wet seasonal dynamics.

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“…Analisando as precipitações pluviais no período de janeiro a abril, nota-se que os valores ultrapassam os 300 mm mensais, sendo os meses mais pluvioso fevereiro e março com valores da ordem de 320 mm e 340 mm, respectivamente. Segundo Zoungrana et al (2015) o NDVI é mais sensível a variação da precipitação que outras variáveis climáticas. Considerando-se que o período de semeadura das culturas ocorreu entre os meses de janeiro e fevereiro os valores em NDVI indicam que os estoques de água no solo, no período chuvoso, principalmente entre os meses de março e abril, o NDVI apresentou valores mais elevados, predominando na faixa entre 0,5 a 0,6, identificado pela classe 4.…”

Section: Resultados E Discussõesunclassified

Produto Modis Associado À Condição Hídrica No Solo Para Diferenciar Padrões Na Vegetação No Município De Dom Eliseu, Pará (Modis Product Associated With Soil Water Condition to Distinguish Vegetation Patterns in the Municipality of Dom Eliseu, Pará)

Pismel

Martorano

Monteiro

et al. 2016

ABClima

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Objetivou avaliar a dinâmica de padrões na vegetação usando NDVI (Normalized Difference Vegetation Index) associado à oferta hídrica no município de Dom Eliseu, no Pará com base na reflectância em áreas com cultivos anuais de grãos e plantios florestais, nos períodos de maior e menor deficiência de água no solo. Foram analisados dados meteorológicos para calcular balanços hídricos (CAD = 300 mm) e respostas em NDVI (Normalized Difference Vegetation Index) extraídos do sensor MODIS (Moderate Resolution Imaging Spectroradiometer). As imagens-índice (NDVI) referentes aos meses de janeiro a dezembro de 2012 foram processadas no aplicativo Envi 4.7 e reclassificadas no ArcGIS10.1. Os resultados apontaram variações temporais ao longo do ano, tanto relacionados aos sistemas de agrícolas de produção, quanto aos remanescentes florestais os quais indicavam associações à oferta hídrica na região e possíveis respostas fenológicas. Em Dom Eliseu, o mês de maior valor em NDVI foi em abril com mais 60% do município expressando manutenção das folhas e da capacidade fotossintética das plantas, pois os valores em NDVI foram superiores a 0,6. No período de agosto a setembro ocorrem as menores cotas pluviais, ocasionando déficits hídricos que atingem valores superiores a 70 mm. Observou-se que as respostas em NDVI foram mais expressivas no mês de outubro, totalizando 16% da área de estudo com valores entre 0,2 a 0,3, evidenciando reduzida expressão em resposta espectral na biomassa dos remanescentes de vegetação e plantios florestais. Conclui-se que existe sensibilidade do NDVI em resposta à condição hídrica no solo. Ao contabilizar-se as diferenças entre a reflectâncias no infravermelho próximo e no vermelho divididos pela soma dessas reflectância, os baixos valores de NDVI, reforçam que no período de maior deficiência hídrica há queda de folhas, pois a superfície imageada, responde com valores mais elevados no solo do que na vegetação.

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Land Use/Cover Response to Rainfall Variability: A Comparing Analysis between NDVI and EVI in the Southwest of Burkina Faso

Abstract: contrasting conclusions were also noted in literature. Hence wider spatial investigation will be necessary to confirm the results of this paper.

Cited by 40 publications

References 32 publications

Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa

Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa

Accounting for Seasonal Land Use Dynamics to Improve Estimation of Agricultural Irrigation Water Withdrawals

Produto Modis Associado À Condição Hídrica No Solo Para Diferenciar Padrões Na Vegetação No Município De Dom Eliseu, Pará (Modis Product Associated With Soil Water Condition to Distinguish Vegetation Patterns in the Municipality of Dom Eliseu, Pará)

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