Ziarat's Forests in Transition: A Remote Sensing Perspective on Fragmentation

This study examines forest fragmentation dynamics in the Ziarat District of Balochistan, Pakistan, from 1994 to 2024 using multi-temporal Landsat imagery and advanced geospatial techniques. Support Vector Machine (SVM) classification was applied to generate accurate land-cover maps, followed by Morphological Spatial Pattern Analysis (MSPA) to characterize forest structure into core, edge, islet, and bridge classes. Results reveal a continuous decline in core forest areas accompanied by an increase in fragmented components, indicating reduced ecological connectivity over time. Fragmentation intensified notably after 2010, closely linked to agricultural expansion and infrastructural development. Although a slight increase in total forest area is observed in recent years, spatial integrity continues to deteriorate. Vegetation indices further suggest rising moisture stress, highlighting physiological degradation alongside spatial fragmentation. Despite limitations such as medium-resolution imagery and limited ground data, the findings underscore the urgent need for targeted, science-based conservation strategies to protect Ziarat’s globally significant juniper forests.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-04
DOI
https://doi.org/10.5281/zenodo.18148134
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Ziarat's Forests in Transition: A Remote Sensing Perspective on Fragmentation

Aman Amjad, Muhammad Zahid Khalil Rao, Bakhtawar Bhutto, Hasnain Abbas
Zenodo (CERN European Organization for Nuclear Research)
Remote Sensing in Agriculture
article

Ziarat's Forests in Transition: A Remote Sensing Perspective on Fragmentation

Aman Amjad, Muhammad Zahid Khalil Rao, Bakhtawar Bhutto, Hasnain Abbas
article en

Abstract

This study examines forest fragmentation dynamics in the Ziarat District of Balochistan, Pakistan, from 1994 to 2024 using multi-temporal Landsat imagery and advanced geospatial techniques. Support Vector Machine (SVM) classification was applied to generate accurate land-cover maps, followed by Morphological Spatial Pattern Analysis (MSPA) to characterize forest structure into core, edge, islet, and bridge classes. Results reveal a continuous decline in core forest areas accompanied by an increase in fragmented components, indicating reduced ecological connectivity over time. Fragmentation intensified notably after 2010, closely linked to agricultural expansion and infrastructural development. Although a slight increase in total forest area is observed in recent years, spatial integrity continues to deteriorate. Vegetation indices further suggest rising moisture stress, highlighting physiological degradation alongside spatial fragmentation. Despite limitations such as medium-resolution imagery and limited ground data, the findings underscore the urgent need for targeted, science-based conservation strategies to protect Ziarat’s globally significant juniper forests.

Zenodo (CERN European Organization for Nuclear Research)
Institute of Space Technology (PK)
Life in Land
Openalex Percentile: Top 97%
Remote Sensing in Agriculture
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Ziarat's Forests in Transition: A Remote Sensing Perspective on Fragmentation — Aman Amjad, Muhammad Zahid Khalil Rao, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS