MANGROVE FOREST COVER EXTRACTION OF THE COASTAL AREAS OF NEGROS OCCIDENTAL, WESTERN VISAYAS, PHILIPPINES USING LIDAR DATA

被引:4
|
作者
Pada, A. V. [1 ]
Silapan, J. [2 ]
Cabanlit, M. A. [1 ]
Campomanes, F. [1 ]
Garcia, J. J. [1 ]
机构
[1] Univ Phlippines Cebu Phil LiDAR 2, Gorordo Ave, Cebu, Philippines
[2] Univ Phlippines Cebu, Gorordo Ave, Cebu, Philippines
来源
XXIII ISPRS CONGRESS, COMMISSION I | 2016年 / 41卷 / B1期
关键词
Feature Extraction; SVM; Image Processing; Coastal Resources; LIDAR;
D O I
10.5194/isprsarchives-XLI-B1-73-2016
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Mangroves have a lot of economic and ecological advantages which include coastal protection, habitat for wildlife, fisheries and forestry products. Determination of the extent of mangrove patches in the coastal areas of the Philippines is therefore important especially in resource conservation, protection and management. This starts with a well-defined and accurate map. LiDARwas used in the mangrove extraction in the different coastal areas of Negros Occidental in Western Visayas, Philippines. Total coastal study area is 1,082.55 km(2) for the 14 municipalities/ cities processed. Derivatives that were used in the extraction include, DSM, DTM, Hillshade, Intensity, Number of Returns and PCA. The RGB bands of the Orthographic photographs taken at the same time with the LiDAR data were also used as one of the layers during the processing. NDVI, GRVI and Hillshade using Canny Edge Layer were derived as well to produce an enhanced segmentation. Training and Validation points were collected through field validation and visual inspection using Stratified Random Sampling. The points were then used to feed the Support Vector Machine (SVM) based on tall structures. Only four classes were used, namely, Built-up, Mangroves, Other Trees and Sugarcane. Buffering and contextual editing were incorporated to reclassify the extracted mangroves. Overall accuracy assessment is at 98.73% (KIA of 98.24%) while overall accuracy assessment for Mangroves only is at 98.00%. Using this workflow, mangroves can already be extracted in a large-scale level with acceptable overall accuracy assessments.
引用
收藏
页码:73 / 79
页数:7
相关论文
共 22 条
  • [1] LAND COVER INFORMATION EXTRACTION USING LIDAR DATA
    Shaker, Ahmed
    El-Ashmawy, Nagwa
    XXII ISPRS CONGRESS, TECHNICAL COMMISSION VII, 2012, 39 (B7): : 167 - 172
  • [2] MONITORING CONCEPTS FOR COASTAL AREAS USING LIDAR DATA
    Schmidt, A.
    Rottensteiner, F.
    Soergel, U.
    ISPRS HANNOVER WORKSHOP 2013, 2013, 40-1 (W-1): : 311 - 316
  • [3] Canopy Cover Mapping in Ratai Bay Mangrove Forests using Airborne LiDAR Data
    Mulyanto, M.
    Kamal, Muhammad
    Wijaya, Muhammad Sufwandika
    EIGHTH GEOINFORMATION SCIENCE SYMPOSIUM 2023: GEOINFORMATION SCIENCE FOR SUSTAINABLE PLANET, 2024, 12977
  • [4] An Object-Based Supervised Nearest Neighbor Method for Extraction of Rhizophora in Mangrove Forest from LiDAR Data and Orthophoto
    Sarraga Alon, Alvin
    Festijo, Enrique D.
    Juanico, Drandreb Earl O.
    2019 IEEE 9TH INTERNATIONAL CONFERENCE ON SYSTEM ENGINEERING AND TECHNOLOGY (ICSET), 2019, : 102 - 107
  • [5] UNSUPERVISED ANOMALY WEED DETECTION IN RIPARIAN FOREST AREAS USING HYPERSPECTRAL DATA AND LIDAR
    Peerbhay, Kabir
    Mutanga, Onisimo
    Lottering, Romano
    Ismail, Riyad
    2016 8TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2016,
  • [6] LARGE-SCALE WATER CLASSIFICATION OF COASTAL AREAS USING AIRBORNE TOPOGRAPHIC LIDAR DATA
    Smeeckaert, Julien
    Mallet, Clement
    David, Nicolas
    Chehata, Nesrine
    Ferraz, Antonio
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 61 - 64
  • [7] MANGROVE PLANTATION FOREST ASSESSMENT USING STRUCTURAL ATTRIBUTES DERIVED FROM LIGHT DETECTION AND RANGING (LiDAR) DATA
    Faelga, R. A. G.
    Paringit, E. C.
    Perez, G. J. P.
    Ibanez, C. A. G.
    Argamosa, R. A. L.
    Posilero, M. A. V.
    Zaragosa, G. P.
    Tandoc, F. A. M.
    Malabanan, M. V.
    XXIII ISPRS CONGRESS, COMMISSION VIII, 2016, 41 (B8): : 617 - 623
  • [8] A new hierarchical method for automatic road centerline extraction in urban areas using LIDAR data
    Tejenaki, Sayyed Abdullah Kianejad
    Ebadi, Hamid
    Mohammadzadeh, Ali
    ADVANCES IN SPACE RESEARCH, 2019, 64 (09) : 1792 - 1806
  • [9] Estimating forest canopy cover dynamics in Valles Caldera National Preserve, New Mexico, using LiDAR and Landsat data
    Humagain, Kamal
    Portillo-Quintero, Carlos
    Cox, Robert D.
    Cain, James W., III
    APPLIED GEOGRAPHY, 2018, 99 : 120 - 132
  • [10] The extraction of forest CO2 storage capacity using high-resolution airborne lidar data
    Lee, Sang Jin
    Kim, Jung Rack
    Choi, Yun Soo
    GISCIENCE & REMOTE SENSING, 2013, 50 (02) : 154 - 171