Long-term monitoring of Water Hyacinth in Lake Nokoué, Benin, West Africa

IF 4.5 Q2 ENVIRONMENTAL SCIENCES
Priscilla Baltezar , Ufuoma Ovienmhada , David Lagomasino , Lola Fatoyinbo , Seamus Lombardo , Metogbe Belfrid Djihouessi , Djigbo Félicien Badou , Gildas Tomavo , Fohla Mouftaou , Danielle Wood
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引用次数: 0

Abstract

Water hyacinth is a globally recognized invasive aquatic plant known for its significant environmental impact and the substantial costs associated with its management. Its proliferation has caused widespread damage across Lake Nokoué in southern Benin, home to fishing communities that practice traditional fishing techniques called Acadja. Although these fishing structures increase fishing yields, they also exacerbate the water hyacinth infestation rate. This study, therefore, models the extent of water hyacinth, Acadja with attached water hyacinth, other land, and other vegetation in the Lake Nokoué area using Landsat Collection 2 Tier 1 and Sentinel-1 C-band Synthetic Aperture Radar imagery from 2015 to 2022 with Random Forest machine learning. Seventeen predictors were selected to model each land cover, including five spectral indices, seven spectral bands, two radar bands, and three terrain predictors. The model mapped a total of 17,413.4 ha for water, 2907.5 ha for water hyacinth, 1780.6 ha for Acadja, 2128.6 ha for other land, and 8289 ha for other vegetation areas by the end of 2022. The rate of change in the region since 2015 was −6.8 % (water), +149.7 % (water hyacinth), +726.1 % (Acadja), −20.6 % (other land), and −15 % (other vegetation) for each class. A separate method was also tested to compare the supervised modeling to an unsupervised method. Otsu segmentation was used for the same study period. Otsu detected 614 ha of vegetation for 2015 and increased to 1133 ha by 2022, but results indicate this method is unreliable. Vegetation found in Lake Nokoué was also assessed monthly from 2000 to 2022 using manual segmentation of a harmonic Landsat time series. By 2022, results indicated that infestations consistently maxed out during December and January and exponentially expanded. Although infestations traditionally peaked during November and December, the study found that lake vegetation increased by 941 % and 304 % for the high- (September to December) and low-water (January to May) seasons.
西非贝宁nokou湖水葫芦的长期监测
水葫芦是一种全球公认的入侵水生植物,因其对环境的重大影响和与管理相关的巨额成本而闻名。它的扩散已经在贝宁南部的nokou湖造成了广泛的破坏,那里是渔民社区的家园,他们使用传统的捕鱼技术,称为Acadja。虽然这些渔场结构增加了渔业产量,但也加剧了水葫芦的侵害率。因此,本研究利用2015 - 2022年Landsat Collection 2 Tier 1和Sentinel-1 c波段合成孔径雷达图像,采用随机森林机器学习方法,对nokou湖区水葫芦、附水葫芦的Acadja、其他土地和其他植被的覆盖范围进行了建模。选取了17个预测因子,包括5个光谱指数、7个光谱波段、2个雷达波段和3个地形预测因子。到2022年底,该模型共绘制了17413.4公顷的水域、2907.5公顷的水葫芦、1780.6公顷的阿卡贾、2128.6公顷的其他土地和8289公顷的其他植被区域。自2015年以来,该地区各类别的变化率分别为- 6.8%(水)、+ 149.7%(水葫芦)、+ 726.1%(阿卡贾)、- 20.6%(其他土地)和- 15%(其他植被)。还测试了一种单独的方法来比较监督建模和非监督建模。同一研究期间采用Otsu分割法。Otsu在2015年检测到614 ha的植被,到2022年增加到1133 ha,但结果表明该方法不可靠。从2000年到2022年,通过对调和Landsat时间序列的人工分割,每月对nokou湖的植被进行评估。到2022年,结果表明,虫害一直在12月和1月达到顶峰,并呈指数增长。虽然虫害通常在11月和12月达到高峰,但研究发现,在高水季(9月至12月)和低水季(1月至5月),湖泊植被分别增加了941%和304%。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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