SSP 시나리오 기반 토지피복변화 예측 자료를 활용한 지자체 공간환경계획 적용 방향: 울산광역시 사례를 중심으로

초록

Climate change and rapid urbanization are accelerating land-cover change, increasing the need for integrated land-use and environmental planning. However, quantitative tools capable of proactively diagnosing future environmental degradation remain limited. This study uses Ulsan Metropolitan City, where urban, industrial, and natural land uses coexist, as a case study to quantitatively assess future conservation-grade degradation across multiple spatial scales, including administrative units (city, county, district, town, and village) and 100-m grid cells. To achieve this, SSP-based future land-cover projections generated by the FLUS-SD model were integrated with two official environmental assessment datasets: the Environmental Conservation Value Assessment Map (ECVAM) and the Ecosystem and Nature Map. We developed two novel indicators-the Environmental Grade Degradation Index (EDI) and the Natural Grade Degradation Index (NDI)-to quantify future conservation-grade degradation while simultaneously evaluating changes in forest patch fragmentation. Because the two indices are derived from partially overlapping datasets, they showed high spatial agreement (IoU = 59.7%; Cohen’s κ = 0.70); nevertheless, the areas identified by EDI alone indicate that the two indices provide complementary information for assessing future environmental risks. Forest fragmentation consistently increased under both scenarios analyzed. Furthermore, approximately 3,198 ha were identified as scenario-sensitive areas that remained conserved under the environmentally friendly scenario but were degraded under the high-development scenario, while 1,922 potential degradation risk areas (16,786 ha in total), each covering at least 1 ha of projected degradation under SSP5-8.5 in 2100, were identified as priority monitoring targets. This study demonstrates that scenario-based environmental diagnosis can extend conventional spatial environmental planning beyond static assessments toward proactive risk prediction. By integrating future land-cover projections with national environmental assessment systems, the proposed framework provides a quantitative basis for identifying conservation priorities before development occurs, thereby supporting integrated land-use and environmental planning. The framework is consistent with the growing policy emphasis on data-driven and spatially customized environmental management and offers practical evidence for strengthening the implementation of local governments’ spatial environmental plans.

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