Factors Affecting Wetland Loss: A Review

Ballut-Dajud, Gastón; Sandoval-Herazo, Luis Carlos; Fernández-Lambert, Gregorio; Marín-Muñiz, José Luis; Méndez, María Cristina López; Betanzo-Torres, Erick Arturo

doi:10.3390/land11030434

Cited by 70 publications

(20 citation statements)

References 164 publications

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“…Wetlands face numerous threats, including habitat loss and degradation, pollution, and climate change impacts, which are exacerbated in urban environments (Hu et al 2017;Ballut-Dajud et al 2022). Urbanization contributes to habitat loss and fragmentation as wetlands are drained, lled, or converted for development (Ehrenfeld 2000(Ehrenfeld , 2004.…”

Section: Introductionmentioning

confidence: 99%

Aquatic Condition Index (ACI): A rapid wetland assessment tool for evaluating urban wetland health

Erratt,

Nwaishi,

Lee

et al. 2024

Preprint

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Urbanization poses significant threats to wetland ecosystems, leading to habitat loss, hydrological alterations, and the introduction of invasive species that adversely affect essential ecosystem services. This widespread threat underscores the need to develop a robust management tool for gauging urban wetland health. The Aquatic Condition Index (ACI) has been proposed as a diagnostic tool for monitoring urban wetland health in Calgary, Alberta, Canada. The ACI evaluates wetland health by incorporating functional indicators (i.e., hydrologic, ecological, and water quality functions) chosen by scientific experts. Gathering the data necessary to generate wetland indicators for the ACI requires substantial financial resources and a high degree of analytical expertise for data collection (e.g., field surveys), thus presenting opportunities for optimizing indicators. Here, we shifted from physical to visual-based assessments (i.e., in the field and using geographic information systems), which refined indicator selection and removed those with low practicality. The refined ACI tool provides results comparable to the original prototype and a rapid and cost-effective solution for long-term urban wetland monitoring and assessments to ensure the sustainability of these core urban features.

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Section: Introductionmentioning

confidence: 99%

Aquatic Condition Index (ACI): A rapid wetland assessment tool for evaluating urban wetland health

Erratt,

Nwaishi,

Lee

et al. 2024

Preprint

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“…Urban sprawl and agriculture are the main factors behind this decline. 13 Many practices have a destructive effect on wetland ecosystems, such as embankments, lack of maintenance, drainage, and the straightening of watercourses, all of which reduce the relationship between wetlands and watercourses. 14 Several criteria can be used to identify wetlands.…”

Section: Introductionmentioning

confidence: 99%

Machine and deep learning methods for detection and mapping of coastal wetlands of Crozon Peninsula (Brittany, France) used metric and sub-metric spatial resolution

Le Guillou,

Niculescu,

Schmullius

2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Wetlands are one of the most important ecosystems in the world. Today, however, their fate is under serious threat, and their alarming decline highlights the urgent need to preserve these areas rich in biodiversity. The aim of this work is to spatially map and mapping the wetlands of the Crozon peninsula in Brittany France. The methodology is divided into two parts; the first part is to map the wetlands as a whole, while the second part is to map the wetlands using a adapted typology. Several databases were used to spatialize the wetlands: 12 Sentinel-2 images in L3A format, 23 Sentinel-1 VV and VH images and the RGE Alti (DTM at 1 metre resolution). The images were processed and stacked alone or in synergy. A Random Forest (RF) machine learning algorithm was then trained to predict wetlands in our study area using binary training data. The training data were obtained from a wetland inventory conducted in Brittany, distributed at the scale of a Sentinel-2 tile (30UUU). Post-processing was then carried out on the best result: binary morphological erosion and thresholding based on the DTM to remove outliers. We carried out two classifications, which we later merged. The classifications were carried out using a Pleiades time series (five dates) to achieve a very fine scale classification. A classification of 13 land cover classes with six different wetland types (mudflats, salt marshes, coastal lagoons, wet meadows, wet forest, swamps/bogs) was performed using three methods: pixel-by-pixel random forest, object-based random forest and Convolutional Neural Network (CNN). The best results obtained was for the pixel-based classification: kappa = 0.89, overall accuracy = 0.90, F1-score = 0.90.

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“…For food security reasons, wetlands have been reclaimed for agricultural lands in India, Bangladesh, Myanmar, Thailand, Vietnam, China, Malaysia, Egypt, Iraq, and Indonesia. Moreover, global analysis results indicate that agriculture, urbanization, aquaculture, and industry are primary causal factors in promoting the effective loss of wetlands [1]. Natural wetland ecosystems serve multiple ecological functions [1]: soil formation and stabilization support; food, water, and plant biomass supply; cultural/recreational services, landscape, and ecological tourism; climate regulation; and carbon sequestration.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, global analysis results indicate that agriculture, urbanization, aquaculture, and industry are primary causal factors in promoting the effective loss of wetlands [1]. Natural wetland ecosystems serve multiple ecological functions [1]: soil formation and stabilization support; food, water, and plant biomass supply; cultural/recreational services, landscape, and ecological tourism; climate regulation; and carbon sequestration. Wetland conversion leads to the loss of these functions.…”

Section: Introductionmentioning

confidence: 99%

Tidal Rice Yield Assessment in Central Kalimantan, Indonesia, under Different Cultural Practices

et al. 2022

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This research aimed to assess the performance of a technology package in relation to rice yield in a B-type tidal rice field in Central Kalimantan province (Indonesia). We selected four areas with different hydrological conditions, soil characteristics, and farmer management systems in Pulangpisau and Kapuas Regency. The introduced technology package covered water management, soil tillage, amelioration and fertilization, adaptive high-yielding varieties, and crop protection. The results showed that, on average, the rice yield of Terusan Karya was 5.52 t ha−1, followed by Belanti B (3.75 t ha−1), Belanti A (3.61 t ha−1), and Talio Hulu (3.27 t ha−1). Rice varieties that yielded more than 5 t ha−1 were Inpara 8 (6.28 t ha−1), Suppadi 89 (5.54 t ha−1), Inpara 3 (5.46 t ha−1), and Inpara 2 (5.36 t ha−1). The implementation of a site-specific technology package combined with intensive guidance for the farmer on its implementation is the key factor in increasing rice productivity in tidal rice farming.

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Factors Affecting Wetland Loss: A Review

Cited by 70 publications

References 164 publications

Aquatic Condition Index (ACI): A rapid wetland assessment tool for evaluating urban wetland health

Aquatic Condition Index (ACI): A rapid wetland assessment tool for evaluating urban wetland health

Machine and deep learning methods for detection and mapping of coastal wetlands of Crozon Peninsula (Brittany, France) used metric and sub-metric spatial resolution

Tidal Rice Yield Assessment in Central Kalimantan, Indonesia, under Different Cultural Practices

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