Object-based land use and land cover mapping from LiDAR data and orthophoto application of decision tree-based data selection for SVM classification

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

doi:10.1109/r10-htc.2016.7906854

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Considering the usefulness of this technique, it is widely studied in agriculture, coastal, and resource mappings, risk management, etc. With the integration of LiDAR data and high spatial resolution orthoimages, more input data layers are available for object-based SVM classification [30,31]. However, different classes of objects are not separable by direct thresholding.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor Fusion Workflow for Accurate Classification and Mapping of Sugarcane Crops

Villareal

Tongco

2019

Eng. Technol. Appl. Sci. Res.

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This study aims to assess the classification accuracy of a novel mapping workflow for sugarcane crops identification that combines light detection and ranging (LiDAR) point clouds and remotely-sensed orthoimages. The combined input data of plant height LiDAR point clouds and multispectral orthoimages were processed using a technique called object-based image analysis (OBIA). The use of multi-source inputs makes the mapping workflow unique and is expected to yield higher accuracy compared to the existing techniques. The multi-source inputs are passed through five phases: data collection, data fusion, image segmentation, accuracy validation, and mapping. Data regarding sugarcane crops were randomly collected in ten sampling sites in the study area. Five out of the ten sampling sites were designated as training sites and the remaining five as validation sites. Normalized digital surface model (nDSM) was created using the LiDAR data. The nDSM was paired with Orthophoto and segmented for feature extraction in OBIA by developing a rule-set in eCognition software. A rule-set was created to classify and to segment sugarcane using nDSM and Orthophoto from the training and validation area sites. A machine learning algorithm called support vector machine (SVM) was used to classify entities in the image. The SVM was constructed using the nDSM. The height parameter nDSM was applied, and the overall accuracy assessment was 98.74% with Kappa index agreement (KIA) 97.47%, while the overall accuracy assessment of sugarcane in the five validation sites were 94.23%, 80.28%, 94.50%, 93.59%, and 93.22%. The results suggest that the mapping workflow of sugarcane crops employing OBIA, LiDAR data, and Orthoimages is attainable. The techniques and process used in this study are potentially useful for the classification and mapping of sugarcane crops.

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Section: Introductionmentioning

confidence: 99%

Multi-sensor Fusion Workflow for Accurate Classification and Mapping of Sugarcane Crops

Villareal

Tongco

2019

Eng. Technol. Appl. Sci. Res.

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Approaches, potential, and challenges in the use of remote sensing to study mangrove and other tropical wetland forests

Chávez,

López-Portillo,

Gallardo-Cruz

et al. 2023

Bot. Sci.

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Tropical wetland forests are fragile ecosystems facing critical risks due to global warming and other anthropogenic threats. Hence, gathering accurate and reliable information on them is urgent. Although remote sensing has demonstrated great potential in studying terrestrial ecosystems, remote sensing-based wetland forest research is still in an early stage of development. Mapping wetland forests, particularly mangrove forests, was an initial goal of this approach and is a task that still faces methodological challenges. Initially based on aerial photography only, wetland forest mapping through remote sensing underwent explosive diversification after the launching of artificial satellites in the 1970s. Later, precision in wetland forest mapping increased with the combination of hyperspectral, multispectral, and high and very high resolution imagery. Accurate delimitation of wetland forest extent is also necessary to assess their temporal dynamics (losses, gains, and horizontal displacement). Despite the prevalence of mapping studies, current remote sensing-based research on wetland forests addresses new questions and novel aims, such as describing and predicting wetland forest attributes through mathematical modeling. Although this approach has made substantial progress in recent decades, modeling and predicting wetland forest attributes remain insufficiently explored fields of research. Combining active and passive sensors is a promising alternative to provide a more accurate picture of these communities’ attributes. In particular, LiDAR and radar-based technologies may help overcome difficulties encountered in older studies. In the future, we will witness conceptual and methodological progress that will enable us to surmount the remaining challenges.

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Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

Pillodar,

Suson,

Aguilos

et al. 2023

Forests

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In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions.

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Object-based land use and land cover mapping from LiDAR data and orthophoto application of decision tree-based data selection for SVM classification

Cited by 3 publications

References 14 publications

Multi-sensor Fusion Workflow for Accurate Classification and Mapping of Sugarcane Crops

Multi-sensor Fusion Workflow for Accurate Classification and Mapping of Sugarcane Crops

Approaches, potential, and challenges in the use of remote sensing to study mangrove and other tropical wetland forests

Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

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