Impacts of environmental pollution on mangrove phenology: Combining remotely sensed data and generalized additive models

Celis-Hernández, Omar; Villoslada, Miguel; Ward, Raymond D.; Bergamo, Thaisa; Pérez-Ceballos, Rosela; Girón-García, María Patricia

doi:10.1016/j.scitotenv.2021.152309

Cited by 23 publications

(4 citation statements)

References 96 publications

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“…In this study, vegetation phenological parameters were extracted using the D-L function method and dynamic threshold method based on MODIS data. While MODIS data are widely used in vegetation phenology parameter extraction [1], [50], it is important to note that phenology assessment based on remote sensing data represents an approximation of real vegetation phenology and entails a certain degree of uncertainty [16]. The extraction of vegetation phenological parameters from remote sensing data using smoothing algorithms and dynamic threshold methods is somewhat subjective, which may introduce biases into the phenological extraction results.…”

Section: A Vegetation Phenological Parametersmentioning

confidence: 99%

“…Therefore, research on the analysis of the non-linear relationships between vegetation phenology and external environmental changes is gradually increasing [12], [13], [14]. Meanwhile, non-linear models have been used for vegetation phenology prediction, which are suitable for exploring the non-linear impact of large-scale climate changes on phenology [15], and the prediction accuracy using non-linear models is higher [16], [17]. Motivated by these studies of using non-linear models to predict phenology, this research proposes the application of the generalized additive model (GAM) to analyze the non-linear relationships between vegetation phenology, climate change and urbanization, and evaluate the feasibility of vegetation phenology prediction.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Responses of Vegetation Phenology to Climate Change and Urbanization: A Case Study in Beijing, China

Yao,

Ding

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Vegetation serves as an indicator of ecological change and phenology is an important indicator for evaluating vegetation growth and development. Analyzing the spatiotemporal characteristics of vegetation phenology and its driving factors is of great significance for analyzing the carbon, water, and energy balance of terrestrial ecosystems. To better assess the impact of external environmental changes on vegetation phenology and comprehend the changing trends of phenology, this research employed phenological parameters as response variables and incorporated meteorological factors and urbanization factors as explanatory variables. A generalized additive model (GAM) was constructed meticulously to investigate how vegetation phenology responds to climate change and urbanization in Beijing, as well as to predict vegetation phenology. The results showed that there were non-linear relationships between the vegetation phenological parameters SOS (start of season) and EOS (end of season) and external environmental changes. GAM explained 68.1% and 73.9% of the variations in SOS and EOS, respectively. The R 2 of the GAM predicted SOS and EOS with respect to the observed data increased to 0.719 and 0.832, respectively. The prediction effects in each region of the urban-rural gradient zone were better with RMSE of approximately 4-7 d. This research demonstrates that GAM considers the non-linear relationships between vegetation phenology and external environmental factors. The validation results based on observed data show that the models are reliable, and the research findings can provide theoretical references for urban development planning and ecological environmental protection in Beijing.

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Section: A Vegetation Phenological Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Responses of Vegetation Phenology to Climate Change and Urbanization: A Case Study in Beijing, China

Yao,

Ding

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A high concentration of Nitrate and phosphate in the coastal area is derived from anthropological activities, including agriculture, plantations, industries, and households along the coast [40]. Pollution caused by anthropogenic waste could be a significant factor in the reduction of mangrove biodiversity [41]- [43].…”

Section: G Mangrove Environmental Conditionmentioning

confidence: 99%

Mangroves and the Sustainability of Longtail Shad Fish (Tenualosa macroura) in Riau Province Water, Indonesia

Seygita¹,

Sulistiono²,

Kusmana³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Mangroves are crucial to fisheries as nurseries; they can be used as spawning and feeding grounds for fish, including Longtail Shad fish (Tenualosa macroura). The objective of the study was to analyze the mangrove vegetation used as Longtail Shad fish spawning ground. It was carried out from January to May 2021, using a checkered line method with six sampling stations. Each station was placed in three transects with three plots for each transect. Density, relative density, frequency, relative frequency, dominance, relative dominance, and importance value index were included in the vegetation analysis. There were 13 true mangrove types and 1 mangrove associate type at the study locations. The highest mangrove density was found at Station 3 with 3300.48 Ind/Ha, categorized as good. The highest mangrove coverage was found at Station 2, with an exceptionally dense category (76.34%). The regression analysis revealed a substantial relationship between density and mangrove and the water salinity at a 0.002 significance value (<0.05). Based on the gut content analysis in establishing the fish's eating habits, the type of food consumed by the Longtail Shad fish was mostly mangrove litter, representing 44.66% of the total stomach contents. The result of the study clearly shows that mangroves are crucial for Longtail Shad fish. A good mangrove ecosystem will provide good water quality for the migration process of longtail shad fish and produce mangrove litter as fish food. Mangrove ecosystem management can support the management of Terubuk fisheries.

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“…The amplitude (seasonal variability), phase (travel time from the origin to the peak of the wave), and RMSE (non-seasonal variability) did not contribute to the robust detection of zonation. Nevertheless, these indicators could be of great importance for understanding the response of mangroves to environmental fluctuations Celis-Hernandez et al (2022). captured the impact of physicochemical variables on seasonal fluctuations of mangroves through phenological metrics.…”

mentioning

confidence: 99%

Mangrove zonation mapping in West Africa, at 10-m resolution, optimized for inter-annual monitoring

Lombard

Soumaré

Andrieu

et al. 2023

Ecological Informatics

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Impacts of environmental pollution on mangrove phenology: Combining remotely sensed data and generalized additive models

Cited by 23 publications

References 96 publications

Nonlinear Responses of Vegetation Phenology to Climate Change and Urbanization: A Case Study in Beijing, China

Nonlinear Responses of Vegetation Phenology to Climate Change and Urbanization: A Case Study in Beijing, China

Mangroves and the Sustainability of Longtail Shad Fish (Tenualosa macroura) in Riau Province Water, Indonesia

Mangrove zonation mapping in West Africa, at 10-m resolution, optimized for inter-annual monitoring

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