Sustainable Management of Natural Resources at Disaggregated Levels with Insights from Landscape Dynamics
DOI:
https://doi.org/10.23917/forgeo.v39i2.6857Keywords:
Natural Resource Rich Regions (NRRRs), Arid regions, Land Use Land Cover (LULC), Machine Learning (ML), Random Forest (RF), landscape modellingAbstract
The burgeoning population, coupled with the resource demand and alterations in the climatic regime, have been posing serious challenges for the sustenance of natural resources. Natural Resource Rich Regions (NRRRs) are areas endowed with abundant natural resources, which maintain ecological balance and economic activities. These regions are pivotal for supporting the livelihoods of local communities by providing essential ecosystem services and resources. However, land degradation leading to deforestation due to unplanned developmental activities has escalated the carbon footprint, aggravated the vagaries of the climate, and posed significant challenges, especially for communities reliant on fragile, arid, and semi-arid ecosystems. The nexus of socio-economic disparity, persistent poverty, and unplanned developmental activities often poses severe challenges for realizing full economic potential with environmental sustainability. Land use (LU) changes with urbanization and agricultural expansion, leading to fragmentation, habitat loss, decline of native species, and disruption of ecological processes with a potential decline of biodiversity. The arid region in the northern part of Karnataka, located in Southern India, has been experiencing a sharp decline in the groundwater table due to frequent droughts and excessive groundwater extraction. The current study unveils actionable solutions for sustainable management of natural resource-rich regions by meticulously analyzing the nexus between rapid development, LU modifications, and their subsequent environmental ramifications. LU transitions are quantified using temporal-spatial data acquired through space-borne sensors through supervised machine learning classifiers based on the non-parametric algorithm Random Forest (RF). Land use dynamics assessment reveals that paved surfaces (area under buildings, roads) have increased from 186.22 sq. km (in 1973) to 1085.12 sq. km (in 2022). The study area has degraded forest patches, and the estimation through fragmentation metrics reveals that the intact forest has shown a decline from 3252.39 sq. km (1973) to 1508.12 sq. km (2022). The forests have continuously decreased from 2,154.20 sq. km (1973) to 1,096.34 sq. km (2022). In Northern Karnataka, the prioritization of NRRRs highlights the status of resource availability, with highly resource-rich zones represented by NRRR1 (67 grids) and NRRR2 (127 grids), followed by NRRR3 (304 grids) with moderate resource potential, and NRRR4 (522 grids) encompassing areas with comparatively scarcer resources. The prioritization of natural resource-rich regions emphasizes the need for prudent land management strategies, with holistic and integrated approaches considering social, economic, and environmental issues with degrees of sensitivity across arid regions.
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