MONITORING AND PREDICTING LAND USE-LAND COVER (LULC) CHANGES WITHIN AND AROUND KRAU WILDLIFE RESERVE (KWR) PROTECTED AREA IN MALAYSIA USING MULTI-TEMPORAL LANDSAT DATA

DOI: https://doi.org/10.14710/geoplanning.5.1.17-34
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Article Metrics: (Click on the Metric tab below to see the detail)

Article Info
Published: 25-04-2018
Section: Articles
Fulltext PDF Tell your colleagues Email the author
Natural and anthropogenic activities surrounding a Protected Area (PA) may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, there is need of environmental change monitoring within and around PA because of its significant values to ecosystem at conservation scales. Effects and influences of local community within and around PA turn into the major problems for natural resource and conservations management as well as environmental impact assessment. Ascertaining the complex interface in relations to changes and its driving factors over period of time within and around PA is significant in order to predict future LULC changes, build alternative scenarios and serve as tools for decision making.  The main objective of this work was to evaluate temporal change detection and prediction of LULC as well as the trends of changes from 1989 to 2016 within and around Krau Wildlife Reserve (KWR).  The cloud issues were mitigated by producing cloud free image and object-based image analysis (OBIA) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. The comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. The simulation model of Land Change Modeler (LCM) was utilized with digital elevation model (DEM) and past LULC maps to project future LULC pattern using Markov chain. The predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of KWR. The study is important for the conservation of habitat species and monitoring the current status of the KWR

Keywords

LULC; OBIA; Protected Area; Krau Wildlife Reserve; Land Change Modeler

  1. Jibrin Gambo 
    Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM) and School of General Studies, Binyaminu Usman Polytechnic, Hadejia P.M.B 013 Jigawa State, Nigeria, Nigeria
  2. Helmi Zulhaidi Mohd Shafri  Orcid
    Universiti Putra Malaysia (UPM), Malaysia
  3. Nur Shafira Nisa Shaharum 
    Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400, Serdang, Malaysia, Malaysia
  4. Fauzul Azim Zainal Abidin 
    Department of Wildlife and National Parks (DWNP), KM10 Jalan Cheras, 56100 Kuala Lumpur, Malaysia, Malaysia
  5. Mohd Taufik Abdul Rahman 
    Department of Wildlife and National Parks (DWNP), KM10 Jalan Cheras, 56100 Kuala Lumpur, Malaysia, Malaysia
  1. Ahmad, C. B., Abdullah, J., & Jaafar, J. (2012). Community Activities Around Protected Areas and the Impacts on the Environment at Krau Wildlife Reserve, Malaysia. Procedia-Social and Behavioral Sciences, 68, 383–394. [Crossref]

  2. Areendran, G., Raj, K., Mazumdar, S., & Sharma, A. (2017). Land Use and Land Cover Change Analysis for Kosi River Wildlife Corridor in Terai Arc Landscape of Northern India: Implications for Future Management. Tropical Ecology, 58(1).

  3. Balaji, S. A., Geetha, P., & Soman, K. P. (2016). Change Detection of Forest Vegetation using Remote Sensing and GIS Techniques in Kalakkad Mundanthurai Tiger Reserve - (A Case Study). Indian Journal of Science and Technology, 9(30), 1–6. [Crossref]

  4. Bozkaya, A. G., Balcik, F. B., Goksel, C., & Esbah, H. (2015). Forecasting land-cover growth using remotely sensed data: a case study of the Igneada protection area in Turkey. Environmental Monitoring and Assessment, 187(3). [Crossref]

  5. Bush, A., Sollmann, R., Wilting, A., Bohmann, K., Cole, B., Balzter, H., … Yu, D. W. (2017). Connecting Earth observation to high-throughput biodiversity data. Nature Ecology & Evolution, 1(7), 176. [Crossref]

  6. Conservation and Environmental Management Division. (2006). Biodiversity in Malaysia.

  7. DANCED, & Jabatan Perlindungan Hidupan Liar dan Taman Negara. (2001). Krau Wildlife Reserve Management Plan. Perhilitan.

  8. de Oliveira, S. N., de Carvalho Júnior, O. A., Gomes, R. A. T., Guimarães, R. F., & McManus, C. M. (2017). Deforestation analysis in protected areas and scenario simulation for structural corridors in the agricultural frontier of Western Bahia, Brazil. Land Use Policy, 61, 40–52. [Crossref]

  9. Desclée, B., Bogaert, P., & Defourny, P. (2006). Forest change detection by statistical object-based method. Remote Sensing of Environment, 102(1–2), 1–11. [Crossref]

  10. Despot Belmonte, K., & Bieberstein, K. (2016). Protected Planet Report 2016. How Protected Areas Contribute to Achieving Global Targets for Biodiversity.

  11. Dudley, N., & Stolton, S. (2008). Defining protected areas: An international conference in Almeria, Spain Mayo 2007. IUCN Protected Areas Categories Summit.

  12. Dutta, K., Reddy, C. S., Sharma, S., & Jha, C. S. (2016). Quantification and Monitoring of Forest Cover Changes in Agasthyamalai Biosphere Reserve, Western Ghats, India (1920-2012). Current Science, 110(4), 508. [Crossref]

  13. Hackman, K. O., Gong, P., & Wang, J. (2017). New land-cover maps of Ghana for 2015 using Landsat 8 and three popular classifiers for biodiversity assessment. International Journal of Remote Sensing, 38(14), 4008–4021. [Crossref]

  14. Hruby, F., Melamed, S., Ressl, R., Stanley, D., Balancing, C., Imagery, S., & Data, B. (2016). Mosaicking Mexico - The Big Picture of Big Data, XLI (July), 407–412. [Crossref]

  15. Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 37–47. [Crossref]

  16. Kindu, M., Schneider, T., Teketay, D., & Knoke, T. (2013). Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands. Remote Sensing, 5(5), 2411–2435. [Crossref]

  17. Kumar, K. S., Valasala, N. V. A. S. S., Subrahmanyam, J. V. V, Mallampati, M., Shaik, K., & Ekkirala, P. (2015). Prediction of Future Land Use Land Cover Changes of Vijayawada City Using Remote Sensing and Gis. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(3), 91–97.

  18. Lin, T. S. (2016). Sumber Air Orang Asli Tercemar Akibat Pembalakan. Pahang: Astro Awani. Retrieved May Tuesday, 2017, from http://www.astroawani.com/berita-malaysia/sumber-air-orang-asli- tercemar-akibat-pembalakan-111904. (2017).

  19. Mishra, V., Rai, P., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute Jovan Cvijic, SASA, 64(1), 111–127. [Crossref]

  20. Munthali, K. G., & Murayama, Y. (2011). Land use/cover change detection and analysis for Dzalanyama forest reserve, Lilongwe, Malawi. Procedia - Social and Behavioral Sciences, 21, 203–211. [Crossref]

  21. Nagendra, H., Lucas, R., Honrado, J. P., Jongman, R. H. G., Tarantino, C., Adamo, M., & Mairota, P. (2013). Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecological Indicators, 33, 45–59. [Crossref]

  22. Norawi, M. F. (2017). Alam dakwa 2,000 balak dicuri di Kuala Krau. Sinar Online, pp. 4–7.

  23. Ranjan, A. K., and Akash Anand, S, V., & Singh, R. K. (2016). LU/LC Change Detection and Forest Degradation Analysis in Dalma Wildlife Sanctuary Using 3S Technology: A Case Study in Jamshedpur-India. AIMS Geosciences, 2(4), 273–285. [Crossref]

  24. Reddy, C. S., Singh, S., Dadhwal, V. K., Jha, C. S., Rao, N. R., & Diwakar, P. G. (2017). Predictive modelling of the spatial pattern of past and future forest cover changes in India. Journal of Earth System Science, 126(1). [Crossref]

  25. Reveshty, M. A. (2011). The Assessment and Predicting of Land Use Changes to Urban Area Using Multi-Temporal Satellite Imagery and GIS: A Case Study on Zanjan, IRAN (1984-2011). Journal of Geographic Information System, 03(04), 298–305. [Crossref]

  26. Son, N.-T., Chen, C.-F., Chang, N.-B., Chen, C.-R., Chang, L.-Y., & Thanh, B.-X. (2015). Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using Landsat Data and Object-Based Image Analysis. IEEE  Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 503–510. [Crossref]

  27. Waiyasusri, K., Yumuang, S., & Chotpantarat, S. (2016). Monitoring and predicting land use changes in the Huai Thap Salao Watershed area, Uthaithani Province, Thailand, using the CLUE-s model. Environmental Earth Sciences, 75(6). [Crossref]

  28. Willis, K. S. (2015). Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233–242. [Crossref]

  29. Yu, W., Zhou, W., Qian, Y., & Yan, J. (2016). A new approach for land cover classification and change analysis: Integrating backdating and an object-based method. Remote Sensing of Environment, 177, 37–47. [Crossref]

  30. Zhang, C., Smith, M., Lv, J., & Fang, C. (2017). Applying time series Landsat data for vegetation change analysis in the Florida Everglades Water Conservation Area 2A during 1996{textendash}2016. International Journal of Applied Earth Observation and Geoinformation, 57, 214–223. [Crossref]