Mapping mangrove forest from LiDAR data using object-based image analysis and Support Vector Machine: The case of Calatagan, Batangas

David, Lawrence Charlemagne G.; Ballado, Alejandro H.

doi:10.1109/hnicem.2015.7393167

Cited by 9 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This optimum boundary can also be referred to as the Optimum Separation Hyperplane (OSH), while the training samples at the class distribution edge can be referred to as the support vectors [40,45]. The accuracy of SVM has been shown to be better than ML, Artificial Neural Networks (ANN), and decision trees, especially when training datasets are comparatively small [42,46,47]. However, the use of SVM and ML in detecting fringe invasive species is largely unstudied.…”

Section: Introductionmentioning

confidence: 99%

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

2020

View full text Add to dashboard Cite

Mangrove forests in the Niger Delta are very valuable, providing ecosystem services, such as carbon storage, fish nurseries, coastal protection, and aesthetic values. However, they are under threat from urbanization, logging, oil pollution, and the proliferation of the invasive Nipa Palm (Nypa fruticans). However, there are no reliable data on the current extent of mangrove forest in the Niger Delta, its rate of loss, or the rate of colonization by the invasive Nipa Palm. Here, we estimate the area of Nipa Palm and mangrove forests in the Niger Delta in 2007 and 2017, using 567 ground control points, Advanced Land Observatory Satellite Phased Array L-band SAR (ALOS PALSAR), Landsat and the Shuttle Radar Topography Mission Digital Elevation Model 2000 (SRTM DEM). We performed the classification using Maximum Likelihood (ML) and Support Vector Machine (SVM) methods. The classification results showed SVM (overall accuracy 93%) performed better than ML (77%). Producers (PA) and User’s accuracy (UA) for the best SVM classification were above 80% for most classes; however, these were considerably lower for Nipa Palm (PA—32%, UA—30%). We estimated a 2017 mangrove area of 801,774 ± 34,787 ha (±95% Confidence Interval) ha and Nipa Palm extent of 11,447 ± 7343 ha. Our maps show a greater landward extent than other reported products. The results indicate a 12% (7–17%) decrease in mangrove area and 694 (0–1304)% increase in Nipa Palm. Mapping efforts should continue for policy targeting and monitoring. The mangroves of the Niger Delta are clearly in grave danger from both rapid clearance and encroachment by the invasive Nipa Palm. This is of great concern given the dense carbon stocks and the value of these mangroves to local communities for generating fish stocks and protection from extreme events.

show abstract

Section: Introductionmentioning

confidence: 99%

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

2020

View full text Add to dashboard Cite

show abstract

“…Light detection and ranging (LiDAR) and optical remote sensing combined with the support vector machine (SVM) algorithm has been used to map and create a mangrove inventory (David, 2015;Pada, 2016). Other remotely sensed data like Landsat and ALOS-PALSAR have also been used to map mangroves with the random forest (RF) classification (Jhonnerie, 2015).…”

Section: Introductionmentioning

confidence: 99%

Mangrove classification using support vector machines and random forest algorithm: a comparative study

Campomanes

Pada

Silapan

2016

GEOBIA 2016: Solutions and Synergies

View full text Add to dashboard Cite

ABSTRACT:Mangrove forest ecosystems fulfil a number of important functions like supporting the conservation of biological diversity by providing habitats, nurseries, and nutrients for animal species. In the Philippines, mangrove forests are declining due to the growth of aquaculture production. Mangrove forests are slowly being replaced by fishponds. An accurate inventory of what are left of these natural resources is important to know how we can conserve and manage them. This study aims to compare the performance of support vector machines (SVM) with random forest (RF) algorithm in automatically classifying mangrove forests using LiDAR data and orthophotographs in an object based approach. The site is a 36 sq. km. coastal area in Manapla, Negros Occidental, Philippines. Various derivatives were created from the LiDAR data like the pit-free canopy height model (CHM) and intensity. The CHM was used in contrast split segmentation to distinguish between ground and non-ground objects. Only the non-ground objects were segmented further knowing that majority of mangroves are tall. Inventory of short mangroves is not yet included in this study. The non-ground objects were further segmented using multiresolution segmentation with the CHM and RGB bands of the orthophoto using a scale of 15. The non-ground class was further separated into four classes namely: mangrove, built-up, other trees, and sugarcane. 120 training points and 30 validation points per class were collected by visual inspection using the orthophoto as reference. Several features of the training objects were computed from both the LiDAR and orthophoto derivatives and used for classification. SVM with radial basis function was used to classify the rest of the image and resulted in an overall accuracy of 95.83%. For mangroves, its precision and recall reached 83.33% and 100%, respectively. In the SVM classification, mangroves were confused with other trees and sugarcane. Another machine learning algorithm, random forest (RF) was used to classify the same area to compare their performance and accuracy. Using the same features, the RF classification achieved an overall accuracy of 99.1667%. For mangroves, the RF classification obtained 100% and 96.70% for its precision and recall, respectively. The RF classifier confused other trees with mangroves which caused the error. The accuracies from both machine learning algorithms show that the RF classifier performed better than the SVM classifier and further implies the potential of using RF in classifying mangroves in other areas.

show abstract

“…Regarding conference proceedings papers, the Asian Conference on Remote Sensing had a higher contribution, with eleven articles [62,[95][96][97][98][99][100][101][102][103][104] representing 9% of the total publications. The main reasons for those results could be as it was a remote sensingfocused conference and the high interest and relevance of paddy production in Asia, as is represented in Section 3.3 of this article.…”

Section: Journals and Conference Proceedingsmentioning

confidence: 99%

“…Following the same pattern as with the peer-reviewed journals, all conference proceedings representing less than 3% were gathered in one category called "Other conferences", representing 16% of the total publications. Regarding conference proceedings papers, the Asian Conference on Remote Sensing had a higher contribution, with eleven articles [62,[95][96][97][98][99][100][101][102][103][104] representing 9% of the total publications. The main reasons for those results could be as it was a remote sensing-focused conference and the high interest and relevance of paddy production in Asia, as is represented in Section 3.3 of this article.…”

Section: Journals and Conference Proceedingsmentioning

confidence: 99%

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

View full text Add to dashboard Cite

Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.

show abstract

Mapping mangrove forest from LiDAR data using object-based image analysis and Support Vector Machine: The case of Calatagan, Batangas

Cited by 9 publications

References 6 publications

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

Mangrove classification using support vector machines and random forest algorithm: a comparative study

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Contact Info

Product

Resources

About