Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand

Songsom, Veeranun; Koedsin, Werapong; Ritchie, Raymond J.; Huete, Alfredo

doi:10.3390/rs11080955

Cited by 29 publications

(30 citation statements)

References 111 publications

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“…The method derived by [54] assumes that the selected model of phenology is: (1) correct, (2) already known and (3) is invariant through space and time [39,56]. Even recent studies [17] insist on these assumptions when applying smoothers and filter to the data prior to detecting the phenology without considering that the data they discard may provide insights into the phenomenon they are trying to model (i.e., phenology). This in itself is not a limitation of the parametric models, but of the analysis workflow selected by the authors.…”

Section: Gams Vs Parametric Methodsmentioning

confidence: 99%

“…Having done this, we calculated the Enhanced Vegetation Index (EVI) [36] for each pixel in each image. We chose EVI because studies show that this spectral index does not saturate with high vegetation densities [36], it is better suited than other indices for discriminating vegetation fraction in mangrove ecosystems [37], and it is commonly used for phenology investigations [16,17].…”

Section: Landsat Image Acquisition and Processingmentioning

confidence: 99%

“…[16] used MODIS (Moderate Resolution Imaging Spectroradiometer) data between 2000-2014 to detect mangrove phenology using four different spectral indices in the Yucatan peninsula in Mexico. Similarly, [17] compared the phenology of mangroves to that of the surrounding forests using MODIS imagery. While the temporal resolution of the MODIS sensors is very high (1-2 days), the spatial resolution is coarse (250-500 m).…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

et al. 2020

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Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes.

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Section: Gams Vs Parametric Methodsmentioning

confidence: 99%

Section: Landsat Image Acquisition and Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

et al. 2020

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“…Nevertheless, cloud interference in tropical areas can severely limit the use of satellite data for phenology studies, particularly when much of the mangrove forest growth occurs during the monsoon or wet season [31]. There have been some mangrove phenology studies carried out with remote sensing data [32,33]. Songsom et al [33] used the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) with 250-m resolution to monitor mangrove phenology in Southern Thailand.…”

Section: Introductionmentioning

confidence: 99%

Mangrove Phenology and Water Influences Measured with Digital Repeat Photography

et al. 2021

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The intertidal habitat of mangroves is very complex due to the dynamic roles of land and sea drivers. Knowledge of mangrove phenology can help in understanding mangrove growth cycles and their responses to climate and environmental changes. Studies of phenology based on digital repeat photography, or phenocams, have been successful in many terrestrial forests and other ecosystems, however few phenocam studies in mangrove forests showing the influence and interactions of water color and tidal water levels have been performed in sub-tropical and equatorial environments. In this study, we investigated the diurnal and seasonal patterns of an equatorial mangrove forest area at an Andaman Sea site in Phuket province, Southern Thailand, using two phenocams placed at different elevations and with different view orientations, which continuously monitored vegetation and water dynamics from July 2015 to August 2016. The aims of this study were to investigate fine-resolution, in situ mangrove forest phenology and assess the influence and interactions of water color and tidal water levels on the mangrove–water canopy signal. Diurnal and seasonal patterns of red, green, and blue chromatic coordinate (RCC, GCC, and BCC) indices were analyzed over various mangrove forest and water regions of interest (ROI). GCC signals from the water background were found to positively track diurnal water levels, while RCC signals were negatively related with tidal water levels, hence lower water levels yielded higher RCC values, reflecting brownish water colors and increased soil and mud exposure. At seasonal scales, the GCC profiles of the mangrove forest peaked in the dry season and were negatively related with the water level, however the inclusion of the water background signal dampened this relationship. We also detected a strong lunar tidal water periodicity in seasonal GCC values that was not only present in the water background, but was also detected in the mangrove–water canopy and mangrove forest phenology profiles. This suggests significant interactions between mangrove forests and their water backgrounds (color and depth), which may need to be accounted for in upscaling and coupling with satellite-based mangrove monitoring.

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“…Flavio et al [33] have tested the phenology-based vegetation mapping method and proved it effective. Some studies have calculated the phenological characteristics of mangroves to derive environmental driving factors that affect their growth [34,35]. Vegetation phenology can provide information about the vegetation dynamics and response after forest fires [36].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

et al. 2020

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Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

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Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand

Cited by 29 publications

References 111 publications

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

Mangrove Phenology and Water Influences Measured with Digital Repeat Photography

Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

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