Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

[1]  Selangor Darul Ehsan,et al.  Mangrove carbon stock assessment by optical satellite imagery , 2013 .

[2]  S. Phinn,et al.  Assessment of multi-resolution image data for mangrove leaf area index mapping , 2016 .

[3]  D. Richards,et al.  Global trends in mangrove forest fragmentation , 2020, Scientific Reports.

[4]  Dongyang Fu,et al.  Remote sensing analysis of mangrove distribution and dynamics in Zhanjiang from 1991 to 2011 , 2018, Journal of Oceanology and Limnology.

[5]  Zhiqiang Xiao,et al.  Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling , 2011 .

[6]  W. Laurance,et al.  Impacts of roads and linear clearings on tropical forests. , 2009, Trends in ecology & evolution.

[7]  A. Huete,et al.  Assessment of biophysical properties of Royal Belum tropical forest, Malaysia , 2018 .

[8]  M. Spalding,et al.  Global patterns in mangrove recreation and tourism , 2019 .

[9]  D. Richards,et al.  Quantifying net loss of global mangrove carbon stocks from 20 years of land cover change , 2020, Nature Communications.

[10]  Kerrylee Rogers,et al.  Mangroves give cause for conservation optimism, for now , 2020, Current Biology.

[11]  Le Wang,et al.  A review of remote sensing for mangrove forests: 1956–2018 , 2019, Remote Sensing of Environment.

[12]  C. Woodcock,et al.  Multiscale analysis and validation of the MODIS LAI product: II. Sampling strategy , 2002 .

[13]  A. Jacobson,et al.  Global areas of low human impact (‘Low Impact Areas’) and fragmentation of the natural world , 2019, Scientific Reports.

[14]  M. Glaser Interrelations between mangrove ecosystem, local economy and social sustainability in Caeté Estuary, North Brazil , 2003, Wetlands Ecology and Management.

[15]  N. Pettorelli,et al.  Public Perceptions of Mangrove Forests Matter for Their Conservation , 2020, Frontiers in Marine Science.

[16]  Eric Vaz,et al.  Managing urban coastal areas through landscape metrics: An assessment of Mumbai's mangrove system , 2014 .

[17]  Liangpei Zhang,et al.  Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery , 2009, IEEE Geoscience and Remote Sensing Letters.

[18]  R. Didham,et al.  The Effect of Fragment Shape and Species' Sensitivity to Habitat Edges on Animal Population Size , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[19]  Anju Singh,et al.  Estimation of Mangrove Carbon Stocks by Applying Remote Sensing and GIS Techniques , 2015, Wetlands.

[20]  Shing Yip Lee,et al.  Improved estimates on global carbon stock and carbon pools in tidal wetlands , 2020, Nature Communications.

[21]  D. Wal,et al.  How to restore mangroves for greenbelt creation along eroding coasts with abandoned aquaculture ponds , 2020, Estuarine, Coastal and Shelf Science.

[22]  P. Cox,et al.  Leaf area index identified as a major source of variability in modeled CO2 fertilization , 2018, Biogeosciences.

[23]  A. Osorio,et al.  From local-to global-scale control factors of wave attenuation in mangrove environments and the role of indirect mangrove wave attenuation , 2020 .

[24]  Suhardjono,et al.  Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images: A case study from the Perancak estuary, Bali, Indonesia. , 2017, Marine pollution bulletin.

[25]  D. Alongi Carbon cycling and storage in mangrove forests. , 2014, Annual review of marine science.

[26]  Hartono,et al.  Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing , 2016 .

[27]  V. Kitsikoudis,et al.  Flow–Vegetation Interaction in a Living Shoreline Restoration and Potential Effect to Mangrove Recruitment , 2019, Sustainability.

[28]  Aurélie C. Shapiro,et al.  The Mangroves of the Zambezi Delta: Increase in Extent Observed via Satellite from 1994 to 2013 , 2015, Remote. Sens..

[29]  E. Webb,et al.  Improved estimates of mangrove cover and change reveal catastrophic deforestation in Myanmar , 2020, Environmental Research Letters.

[30]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[31]  R. Mackenzie,et al.  The impacts of degradation, deforestation and restoration on mangrove ecosystem carbon stocks across Cambodia. , 2019, The Science of the total environment.

[32]  J. L. Ooi,et al.  Aligning conservation and research priorities for proactive species and habitat management: the case of dugongs Dugong dugon in Johor, Malaysia , 2014, Oryx.

[33]  Saudamini Das Does mangrove plantation reduce coastal erosion? Assessment from the west coast of India , 2020, Regional Environmental Change.

[34]  C. Wirth,et al.  Reconciling Carbon-cycle Concepts, Terminology, and Methods , 2006, Ecosystems.

[35]  T. Quaife,et al.  Underestimation of Global Photosynthesis in Earth System Models Due to Representation of Vegetation Structure , 2019, Global Biogeochemical Cycles.

[36]  P. Bunting,et al.  Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally , 2020, One Earth.

[37]  Ellen I. Damschen,et al.  Habitat fragmentation and its lasting impact on Earth’s ecosystems , 2015, Science Advances.

[38]  Sharon K. Collinge,et al.  Ecological consequences of habitat fragmentation: implications for landscape architecture and planning , 1996 .

[39]  Jing Liu,et al.  Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[40]  Ashbindu Singh,et al.  Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .

[41]  J. Grace,et al.  Seasonal variations of net ecosystem (CO2) exchange in the Indian tropical mangrove forest of Pichavaram , 2020 .

[42]  W. Gong,et al.  Estimating leaf area index and photosynthetic production in canopies of the mangrove Rhizophora apiculata , 1997 .

[43]  G. Mace,et al.  Global recognition of the importance of nature-based solutions to the impacts of climate change , 2020, Global Sustainability.

[44]  N. Duke,et al.  Distinct characteristics of canopy gaps in the subtropical mangroves of Moreton Bay, Australia , 2019, Estuarine, Coastal and Shelf Science.

[45]  G. Lin,et al.  Differential Responses of Net Ecosystem Exchange of Carbon Dioxide to Light and Temperature between Spring and Neap Tides in Subtropical Mangrove Forests , 2014, TheScientificWorldJournal.

[46]  Kasturi Devi Kanniah,et al.  Quantifying green cover change for sustainable urban planning: A case of Kuala Lumpur, Malaysia , 2017 .

[47]  D. Murdiyarso,et al.  The potential of Indonesian mangrove forests for global climate change mitigation , 2015 .

[48]  Benjamin W. Heumann Satellite remote sensing of mangrove forests: Recent advances and future opportunities , 2011 .

[49]  Weimin Ju,et al.  Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink , 2019, Nature Communications.

[50]  T. Lacher,et al.  Impacts of Habitat Loss and Fragmentation on Terrestrial Biodiversity , 2013 .

[51]  Chandra Giri,et al.  Mangrove Forest Distributions and Dynamics in Madagascar (1975–2005) , 2008, Sensors.

[52]  Jeffrey A. Cardille,et al.  Strategies for Incorporating High-Resolution Google Earth Databases to Guide and Validate Classifications: Understanding Deforestation in Borneo , 2011, Remote. Sens..

[53]  Wataru Takeuchi,et al.  A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrasses and Salt Marshes during 2010–2018 , 2019, Sensors.

[54]  S. Hamilton,et al.  Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21) , 2014, 1412.0722.

[55]  Naoto Yokoya,et al.  Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges , 2019, Remote. Sens..

[56]  D. Richards,et al.  Identifying spatial patterns and interactions among multiple ecosystem services in an urban mangrove landscape , 2021 .

[57]  Hendri,et al.  Mangrove blue carbon stocks and dynamics are controlled by hydrogeomorphic settings and land‐use change , 2020, Global change biology.

[58]  Le Yu,et al.  Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives , 2012 .

[59]  V. K. Dadhwal,et al.  Seasonal Variations of Carbon Dioxide, Water Vapor and Energy Fluxes in Tropical Indian Mangroves , 2016 .

[60]  Pedram Ghamisi,et al.  Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[61]  Karen C. Seto,et al.  Mangrove conversion and aquaculture development in Vietnam: A remote sensing-based approach for evaluating the Ramsar Convention on Wetlands , 2007 .

[62]  Richard M. Lucas,et al.  Mapping Mangrove Extent and Change: A Globally Applicable Approach , 2018, Remote. Sens..

[63]  G. D’Urso,et al.  The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons , 2016, International Journal of Biometeorology.

[64]  L. P. Koh,et al.  Global potential and limits of mangrove blue carbon for climate change mitigation , 2021, Current Biology.

[65]  U. Krumme,et al.  Decadal trends in mangrove and pond aquaculture cover on Hainan (China) since 1966: mangrove loss, fragmentation and associated biogeochemical changes , 2020 .

[66]  Subash Dahal,et al.  Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[67]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[68]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[69]  Steffen Gebhardt,et al.  Remote Sensing of Mangrove Ecosystems: A Review , 2011, Remote. Sens..

[70]  Arthur P. Cracknell,et al.  Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia , 2015, Remote. Sens..

[71]  Benjamin W. Heumann An Object-Based Classification of Mangroves Using a Hybrid Decision Tree - Support Vector Machine Approach , 2011, Remote. Sens..