Estimating Mangrove Forest Density Using Gap Fraction Method and Vegetation Transformation Indices Approach

Khakhim, Nurul; Putra, Akbar Cahyadhi Pratama; Widhaningtyas, Tantri Utami

doi:10.14710/geoplanning.5.1.35-42

Cited by 4 publications

(7 citation statements)

References 10 publications

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“…In Mui Ca Mau, mangrove health status affected by complex interactions of both natural and artificial factors, especially local people activities included reclamation, charcoal production, timber production, the conversion to shrimp farms. The present study reviews four factors such as mangrove canopy width, mangrove fragmentation, mangrove density [ 36 ], mangrove plant diversity [ 37 ] from peer-review scientific articles ( Fig 6 ).…”

Section: Methodsmentioning

confidence: 99%

Mangrove health assessment using spatial metrics and multi-temporal remote sensing data

Hai¹,

Tinh

Son³

et al. 2022

PLoS ONE

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Mangrove forest plays a very important role for both ecosystem services and biodiversity conservation. In Vietnam, mangrove is mainly distributed in the Mekong delta. Recently, mangrove areas in this region decreased rapidly in both quality and quantity. The forest became bare, divided and scattered into many small patches, which was a major driver of ecosystem degradation. Without a quantitative method for effectively assessing mangrove health in the regional scale, the sustainably conserving mangrove is the challenge for the local governments. Remote sensing data has been widely used for monitoring mangrove distributions, while the characterization of spatial metrics is important to understand the underlying processes of mangrove change. The objectives of this study were to develop an approach to monitor mangrove health in Mui Ca Mau, Ca Mau province of Vietnam by utilizing satellite image textures to assess the mangrove patterns. The research result showed that mangrove areas increased double by 2015, but the forest had become more fragmented. We can be seen those changes in land use mainly come from land conversion from forest to shrimp farms, settlements areas and public constructions. The conserving existing mangrove forest in Mui Ca Mau should consider the relations between mangrove health and influencing factors indicated in the manuscript.

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Section: Methodsmentioning

confidence: 99%

Mangrove health assessment using spatial metrics and multi-temporal remote sensing data

Hai¹,

Tinh

Son³

et al. 2022

PLoS ONE

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“…Data biofisik bisa meliputi profile mangrove, spesies dominan, dan diameter at breast height (DBH) serta kerapatan kanopi mangrove yang sudah banyak dikaji melalui penginderaan jauh (Wijaya dkk, 2018). Salah satu metode untuk memetakan kerapatan kanopi mangrove yang cukup representatif dan efisien adalah menggunakan transformasi indeks vegetasi dan hemispherical photography seperti yang telah dilakukan oleh Khakim et al (2018) dan Taureau et al (2019). Perhitungan kerapatan kanopi mangrove dihitung dengan menggunakan beberapa software seperti CanEye dan Canopeo (Gambar 6), lalu nilai kerapatan tersebut dianalisis regresi linier dengan indeks vegetasi.…”

Section: Hasil Dan Pembahasan Pemetaan Mangrove Dengan Machine Learni...unclassified

Pemetaan Ekosistem Mangrove di Kabupaten Kubu Raya Menggunakan Machine Learning pada Google Earth Engine

Fariz

Permana²,

Daeni

et al. 2021

Jurnal Geografi

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Penyediaan data distribusi mangrove serta perubahannya membutuhkan waktu pemrosesan yang lama jika dilakukan dengan interpretasi citra secara konvensional, apalagi jika dilakukan pada area yang luas seperti Kabupaten Kubu Raya. Hadirnya platform yang bernama Google Earth Engine (GEE) bisa menjadi solusi permasalahan tersebut. GEE mempunyai akses data yang besar, mampu mengolah data berbasis cloud serta memiliki banyak algoritma machine learning. Oleh karena itu penelitian ini mencoba memetakan mangrove di Kabupaten Kubu Raya menggunakan machine learning yang tersedia di GEE, selain itu kami juga membahas beberapa future work terkait pemetaan mangrove di Kabupaten Kubu Raya menggunakan GEE. Machine learning yang digunakan dalam penelitian ini antara lain: CART, Random Forest, GMO Max Entropy, Voting SVM, Margin SVM. Hasil penelitian ini menunjukkan bahwa machine learning yang terbaik dalam memetakan mangrove di Kabupaten Kubu Raya adalah CART. Random Forest juga menjadi machine learning dengan akurasi tertinggi setelah CART, baik keduanya merupakan machine learning berbasis logika atau juga disebut machine learning berbasis pohon keputusan. Dari beberapa studi juga mendukung bahwa machine learning ini sangat cocok digunakan untuk pemetaan penutup lahan. Hasil pemetaan mangrove ini memiliki akurasi kappa yang baik walaupun masih terdapat misklasifikasi sehingga perlu dilakukan sentuhan manual seperti interpretasi visual. Penelitian ini masih terdapat banyak keterbatasan sehingga perlu dikembangkan penelitian dengan menggunakan input data yang lebih beragam dan pengujian hyperparamater antar machine learning.

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“…Mangrove forests represent coastal ecosystems in Indonesia (Khakhima et al, 2018). Mangrove forest is a type of forest located at the mouth of a river or along the coast whose life is strongly influenced by the tides (tidal) of seawater.…”

Section: A Introductionmentioning

confidence: 99%

“…The study of mangrove forests using satellite-based remote sensing techniques (Kamal et al, 2016) currently the most frequently used tool today. Remote sensing is considered more effective in mapping mangrove conditions compared to conventional or terrestrial methods (Khakhima et al, 2018). Currently there are many options to choose from regarding the satellite sensor, which is used.…”

Section: A Introductionmentioning

confidence: 99%

“…Currently there are many options to choose from regarding the satellite sensor, which is used. Mangrove vegetation also has a unique spectral reflection, where the spectral represents a combination of soil, water and vegetation because mangrove vegetation grows in coastal areas (Khakhima et al, 2018;Lee & Yeh, 2009). In addition, the mangrove canopy also affects the spectral reflection of mangroves (Kuenzer et al, 2011).…”

Section: A Introductionmentioning

confidence: 99%

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Mangrove Health Analysis Using Sentinel-2A Image with NDVI Classification Method

Purwanto

Eviliyanto

2021

geoeco

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This study aims to determine 1) the mangrove vegetation density index, 2) the health of mangrove plants in Sungai Batang Village to Kuala Secapah. The data used in this study is the image of Sentinel-2A, dated June 8, 2020. The data taken are vegetation density (NDVI) and mangrove health. The method in this study uses the vegetation index transformation (NDVI). Data analysis used the supervised classification method and the vegetation density index (NDVI). The results showed that the NDVI value of -1 – 0.32 indicates a sparse vegetation density, a value of 0.33 – 0.42 indicates a medium density and 0.43 – 1 indicates a dense density. From this NDVI index value, it can be used as a basis for classifying the health of mangrove vegetation. The health of mangrove vegetation based on the vegetation index value of 0.43 – 1 (meet) indicates that the health of the mangrove vegetation is very good. Vegetation value 0.33 – 0.42 (moderate) indicates good health of mangrove vegetation and vegetation index value -1 – 0.32 (rare) indicates poor vegetation health. Mangrove health level is very good with an area of 3.0314 km2, healthy has an area of 0.204806 km2 and poor health has an area of 0.625875 km2.

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Estimating Mangrove Forest Density Using Gap Fraction Method and Vegetation Transformation Indices Approach

Cited by 4 publications

References 10 publications

Mangrove health assessment using spatial metrics and multi-temporal remote sensing data

Mangrove health assessment using spatial metrics and multi-temporal remote sensing data

Pemetaan Ekosistem Mangrove di Kabupaten Kubu Raya Menggunakan Machine Learning pada Google Earth Engine

Mangrove Health Analysis Using Sentinel-2A Image with NDVI Classification Method

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