APPLYING REMOTE SENSING AND LANDSCAPE METRICS TO ANALYZE THE TREND OF LAND USE/LANDCOVER CHANGE AT VAN CHAN DISTRICT, YEN BAI PROVINCE DURING THE PERIOD OF 2008-2017
Main Article Content
Abstract
Landscape change is one of the main driving forces to modify the state of territorial socio-economic development, especially in the land use/landcover change in short-term. At the high and mountainous terrain, the analysis of its process is the basic step in the monitoring, managing, and planning of sustainable resource use. Based on the remote sensing technology, the information about LULC change at Van Chan district, Yen Bai province during the period of 2008-2017 is detailed in GIS environment. Additionally, the structural characteristics of seven objectives are measured through six landscape metrics show that (i) an increase in Plantation Forest area (2,664 ha) is the main reason of the decline in Natural Forest and Woodlands area (3,527.9 ha); (ii) the fastest rate of change is 392 ha/year of Natural Forest and Woodlands; and (iii) based on the quantitative results of landscape metric, the diversity trends of LULC change for each specific LULC. Moreover, the results provided an effective approach in analyzing the structural change of the landscape.
Keywords
remote sensing, landscape metrics, LULC change, Van Chan District
Article Details
References
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Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16.
Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E.,..., & Tiede, D. (2014). Geographic Object-Based Image Analysis – Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191.
Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62, 1025-1036.
Clinton, N., Holt, A., Scarborough, J., Yan, L., Gong, P. (2010). Accuracy assessment measures for object-based image segmentation goodness. Photogrammetric Engineering & Remote Sensing, 76(3), 289-299.
Collins, J., & Dronova, I. (2019). Urban Landscape Change Analysis Using Local Climate Zones and Object-Based Classification in the Salt Lake Metro Region, Utah, USA. Remote Sensing, 11(13), 1615.
Debolini, M., Marraccini, E., Dubeuf, J. P., Geijzendorffer, I. R., Guerra, C., ..., & Napoléone, C. (2018). Land and farming system dynamics and their drivers in the Mediterranean Basin. Land Use Policy, 75, 702-710.
Ding Y., & Peng, J. (2018). Impacts of Urbanization of Mountainous Areas on Resources and Environment: Based on Ecological Footprint Model. Sustainability, 10(3), 765-778.
Fenta, A. A., Yasuda, H., Haregeweyn, N., Belay, A. S., Hadush, Z., Gebremedhin, M. A., & Mekonnen, G. (2017). The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia. International Journal of Remote Sensing, 38(14), 4107-4129.
Forman, R. T. T. (1995). Land Mosaics: The Ecology of Landscapes and Regions. Cambridge University Press, 632 pages.
Heidhüs, F.J., Herrmann, L., Neef, A., Neidhart, S., Pape, J., Sruamsiri, P., Thu, D.C. & Zarate, A.V. (2007). Sustainable Land Use in Mountainous Regions of Southeast Asia: Meeting the Challenges of Ecological, Socio-Economic and Cultural Diversity. Springer-Verlag Berlin Heidelberg, 404 pages.
Herold, M., Goldstein, N. C., & Clarke, K.C. (2003). The spatiotemporal form of urban growth: measurement, analysis and modelling. Remote Sensing of Environment, 86(3), 286-302.
Labib, S. M., & Harris, A. (2018). The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method. European Journal of Remote Sensing, 51(1), 231-240.
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2008). Remote Sensing and Image Interpretation, 6th Edition, John Wiley & Sons, Hoboken.
Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293.
McGarigal, K., Cushman, S.A., Neel, M.C., & Ene, E. (2002). FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst.
Mitchell, J. J., Shrestha, R., Moore-Ellison, C. A., & Glenn N. F. (2013). Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts. Remote Sensing, 5, 4857-4876.
Moran, E. F. (2010). Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery. Photogrammetric engineering and remote sensing, 76(10), 1159-1168.
Myint, S. W., Gobera, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145-1161.
Nguyen, A. T., Pham, M. T., & Dang, T. H. G. (2015). Best Management Practices (BMPs) for Sloping Agricultural Land Use in the Northern Mountainous Region of Vietnam: a case study of Van Chan District, Yen Bai Province. Proceedings of the International Conference on “Livelihood Development and Sustainable Environment Management in the Context of Climate Change” (LDEM), November 13-15, 2015, Thai Nguyen University of Agriculture and Forestry, Thai Nguyen City, VIETNAM.
Phiri D., & Morgenroth, J. (2017). Developments in Landsat Land Cover Classification Methods: A Review. Remote Sensing, 9(9), 967.
Shafiq M. U., Mir, A. A., Ahmed, P., & Bhat, P. A. (2016). Landuse/ Land cover Analysis in Hamal Watershed of North western Himalaya's using Remote Sensing & GIS. International Research Journal of Engineering and Technology (IRJET), 3(4), 2799-2805.
Sundaresan, A., Varshney, P. K., & Arora, M. K. (2007). Robustness of change detection algorithms in the presence of registration errors. Photogrammetric Engineering & Remote Sensing, 73, 375-383.
Toure, S., Stow, D., Shih, H., Coulter, L., Weeks, J., Engstrom, R., & Sandborn, A. (2016). An object-based temporal inversion approach to urban land use change analysis. Remote Sensing Letters 7(5), 503-512.
Wieland, M., Torres, Y., Pittore, M., & Benito, B. (2016). Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine. International Journal of Remote Sensing, 37(17), 4059-4083.
Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R. L. & Evangelista, P., (2017). A survival guide to Landsat preprocessing. Ecology, 98(4), 920-932.
Yu Y., Guan, H., Zai, D. & Ji, Z. (2016). Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests. ISPRS Journal of Photogrammetry and Remote Sensing, 112, 50-64.
Zhou, W., Troy, A., & Grove, M. (2008). Object-based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution Remote Sensing Data. Sensors (Basel, Switzerland), 8(3), 1613-1636.