Assessing Spatial-Temporal Changes in Monetary Values of Urban Ecosystem Services through Remotely Sensed Data

Reckless urbanization in developing regions is leading to the deterioration of the urban environment. The ensuing impacts can place a burden on urban ecology, urban infrastructure, and residents. This scenario requires a combination of avoidance measures and a detailed assessment of the ecological sustainability of the city. While monetary assessments are certainly conceivable, in this study, the contributions of urban environmental infrastructure are weighed financially. Semi-planned (Jhang) and planned (Faisalabad) urban settlements provided the context for this survey. The study uses the Benefit Transfer Method (BTM) to assess changes in the monetary value of urban ecosystem services (UES) from remote sensing data. This finding suggests that urbanization in Pakistan is devouring productive ecological land in urban areas. The assessment shows that between 1989 and 2019, the agricultural area in Faisalabad shrank (−17.38%), and the built-up area increased (16.05%). Likewise, in Jhang City, the built-up area (4.44%) and wasteland (3.10%) swelled. However, during this period (1989–2019), the proportion of agricultural land in Jhang City decreased (−8.93%). As a result, prime areas of UES are falling back into low-return areas. It also found that provisioning ecosystem services (PES) accounted for a significant portion (68.12%) of the UES produced in Faisalabad and Jhang (69.72%), respectively. In contrast, Cultural Ecosystem Services (CES) contributed the smallest share of UES in Faisalabad (1.63%) and Jhang (1.65%). However, the remaining two services, regulatory and support services, made significant contributions. The assessment shows the role of incoherence, inconsistency, resource constraints, and neglect in compromising the urban environmental integrity of these cities. This situation requires a comprehensive assessment and coordinated effort. For this, it is feasible and useful to combine socioeconomic information with land cover data through computerized equipment.