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Mapping LULC Dynamics and Its Potential Implication on Forest Cover in Malam Jabba Region with Landsat Time Series Imagery and Random Forest Classification

Author

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  • Muhammad Junaid

    (Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic, State Monitoring, Lanzhou 730070, China
    Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China)

  • Jianguo Sun

    (Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic, State Monitoring, Lanzhou 730070, China
    Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China)

  • Amir Iqbal

    (Department of Space Sciences, Institute of Space Technology, Islamabad 44000, Pakistan)

  • Mohammad Sohail

    (Centre for Water Informatics and Technology, Lahore University of Management Science, Lahore 54792, Pakistan)

  • Shahzad Zafar

    (Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Azhar Khan

    (Department of Geology, University of Haripur, Haripur 22610, Pakistan)

Abstract

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km 2 and shrank to 152 km 2 by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km 2 /year from 1980 to 2020. The area of woodland forest decreased by 87 km 2 (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km 2 . The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales.

Suggested Citation

  • Muhammad Junaid & Jianguo Sun & Amir Iqbal & Mohammad Sohail & Shahzad Zafar & Azhar Khan, 2023. "Mapping LULC Dynamics and Its Potential Implication on Forest Cover in Malam Jabba Region with Landsat Time Series Imagery and Random Forest Classification," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1858-:d:1040076
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    References listed on IDEAS

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    1. Aboubakar Gasirabo & Chen Xi & Baligira R. Hamad & Umwali Dufatanye Edovia, 2023. "A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda," Land, MDPI, vol. 12(9), pages 1-20, September.
    2. Lizhong Hua & Haibo Wang & Huafeng Zhang & Fengqin Sun & Lanhui Li & Lina Tang, 2023. "A New Technique for Impervious Surface Mapping and Its Spatio-Temporal Changes from Landsat and Sentinel-2 Images," Sustainability, MDPI, vol. 15(10), pages 1-27, May.

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