Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region

Pham, Tien Dat; Bui, Dieu Tien; Yoshino, Katsumi; Le, Nga Nhu

doi:10.1007/s12665-018-7373-y

Cited by 36 publications

(23 citation statements)

References 70 publications

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“…Remote sensing products beyond optical imagery have also been used to discriminate mangrove species. A recent study by Pham et al [31] showed that the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data can be employed for classifying mangrove species in Vietnam using an object-based logistic model tree (LMT) algorithm with an accuracy of >80%. A more recent study conducted by Valderrama-Landeros et al [32] showed the potential use of normalized difference vegetation index (NDVI) pixel-based classifier for mapping mangrove species in Mexico using different remote sensing data sources from medium to very high spatial resolution (i.e., Landsat-8 OLI, SPOT-5, Sentinel-2A, and WorldView-2) and concluded that the higher spatial resolutions produce higher accuracy (Table 1).…”

Section: Traditional Approaches To Discriminate Mangrove Speciesmentioning

confidence: 99%

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

et al. 2019

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The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

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Section: Traditional Approaches To Discriminate Mangrove Speciesmentioning

confidence: 99%

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

et al. 2019

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“…Logistic Model Trees (LMTree), which is a relatively new machine learning algorithm, is developed based on the integration of tree induction algorithm and additive logistic regression [52]. The difference of LMTree when compared to the other decision tree algorithms is that the tree growing process is carried out using the LogitBoost algorithm [52,55] and the tree pruning is performed using Classification And Regression Tree (CART) [56].…”

Section: Logistic Model Treementioning

confidence: 99%

“…">Logistic Model TreeLogistic Model Trees (LMTree), which is a relatively new machine learning algorithm, is developed based on the integration of tree induction algorithm and additive logistic regression [52]. The difference of LMTree when compared to the other decision tree algorithms is that the tree growing process is carried out using the LogitBoost algorithm [52,55] and the tree pruning is performed using Classification And Regression Tree (CART) [56].Given a training dataset T = (x i , y i ) ds i=1 with x i ∈ R D is the input vector, ds is the number of data samples, D is the dimension of the training dataset, and y i ∈ (1, 0) is the label class. In this research context, the input vector consists of eight variables (slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river), whereas the label class contains two classes, landslide (LS) and non-landslide (Non-LS).…”

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confidence: 99%

Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree

et al. 2018

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The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e., slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the classification accuracy is 93.81% on the training dataset and the prediction capability is 83.4% on the on the validation dataset. When compared to the support vector machine model and the LMTree model, the proposed BE-LMTree model performs better; therefore, we concluded that the BE-LMTree could prove to be a new efficient tool that should be used for landslide modeling. This research could provide useful results for landslide modeling in landslide prone areas.3 of 22 BE and LMTree has resulted in a new powerful prediction method, and to the best of our knowledge, this is the first time that the BE-LMTree is studied for landslide susceptibility. Theoretical Background of the Methods Logistic Model TreeLogistic Model Trees (LMTree), which is a relatively new machine learning algorithm, is developed based on the integration of tree induction algorithm and additive logistic regression [52]. The difference of LMTree when compared to the other decision tree algorithms is that the tree growing process is carried out using the LogitBoost algorithm [52,55] and the tree pruning is performed using Classification And Regression Tree (CART) [56].Given a training dataset T = (x i , y i ) ds i=1 with x i ∈ R D is the input vector, ds is the number of data samples, D is the dimension of the training dataset, and y i ∈ (1, 0) is the label class. In this research context, the input vector consists of eight variables (slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river), whereas the label class contains two classes, landslide (LS) and non-landslide (Non-LS). The landslide class is coded as "1" and the non-landslide is coded as "0". The objective of LMTree is to construct a tree-like structure model that is capable of classifying the training dataset into the two above classes in term of probability. The predicted numeric value to the landslide class of sample is used as susceptibility index.Structurally, ...

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“…Redwood conservation has a long study using GBRT to map giant sequoia trees from CAO hyperspectral imagery from the southern Sierra Nevada mountains in California yielded 95.2-98% overall accuracy [22], and a study comparing SVM with GBRT's for detecting Ohi'a crowns infected with rapid Ohi'a death on Hawaii island found that using a combined SVM and GBRT approach yielded higher performance than either algorithm independently [19]. Other decision tree algorithms such as rotation forest [23] and logistic model tree algorithms [7] have recently been successfully applied for mapping mangrove species.…”

Section: Introductionmentioning

confidence: 99%

“…Passive sensors such as the Landsat Multispectral Scanner (MSS), have been used for mapping vegetation types at the community level and regional scales [6]. Active remote sensing has also been applied for wetland vegetation classification, for example through the identification of mangroves in Vietnam from the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data [7]. With imagery at higher spatial resolution, discrimination of individual tree species is possible.…”

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High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests

Francis

Asner

2019

Remote Sensing

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High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence.We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81-98% true positive rate and 90-98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0-70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California. history in California, beginning with the founding of the first state park in 1901 following intensive and unregulated logging of old-growth redwoods during the second half of the eighteenth century. While some old-growth forests were protected, logging in many redwood forest regions in California continued until the 1970s, when the passing of the Endangered Species Act increased the cost of obtaining a permit to harvest redwoods [4]. Currently, approximately 90% of extant redwood forest is second-growth forest that has been logged at some point during the last two centuries. Redwood forests are fragmented by past harvest history and current land use pressures, and accordingly, there is a need for local-scale, high resolution management of redwood forest land. Maps of redwood distributions are currently available at 1-km spatial resolution, but due to the complex topography, geology, and climate of coastal California, the abundance and properties of redwoods can vary enormously within a 1 km 2 area. Higher-resolution maps have not yet been produced due to the challenge of conducting field work to identify trees over large areas in topographically complex regions.Remote sensing image analysis provides an array of potential tools for mapping vegetation properties [5]. Variation in sensor type, the spectral resolution of the sensor and the spatial resolution of the data have different applications. Passive sensors such as the Landsat Multispectral Scanner (MSS), have been used for mapping vegetation types at the community level and regional scales [6]. Active remote sensing has also been applied f...

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Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region

Cited by 36 publications

References 70 publications

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree

High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests

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