Assessing urban ecosystem condition account with object-based methods

•Methodology uses SEEA-EA for detailed urban ecosystem condition evaluation.•Urban condition accounts are spatially explicit and object-based.•High condition areas in Madrid have significant tree density and bird species.•Low condition areas are highly contaminated and impervious urban zones.•Urban condition accounts support policy-making in water, energy, climate, and biodiversity. We present a methodology based on the System of Environmental-Economic Accounting-Ecosystems Accounting (SEEA-EA) framework to measure urban ecosystem condition accounts. This methodology allows the condition accounts of urban ecosystems to be spatially and explicitly evaluated at a detailed scale. The reference area is determined using object-based evaluation, where the reference value for each variable is set in a real geographic context rather than individual pixels. This methodology consists of the following steps: 1. Delimitation of the urban categories to be evaluated; 2. Selection of the variables that characterize the abiotic and biotic environment; 3. Establishment of the reference polygon with which to compare the condition values; 4. Calculation of weighted condition indicators; 5. Generation of a single condition index from the aggregation of the indicators. In Madrid, the areas with the highest condition levels are characterised by a significant density of trees and bird species richness. In contrast, areas with the lowest condition levels are defined by high contamination, impervious surfaces, built-up areas and major communication routes. This innovative approach to calculating urban conditions represents an advancement in local-scale urban condition accounting and offers a potentially compatible tool with current urban policy frameworks. Its applications can be various, from identifying urban problems to reviewing the effectiveness of a plan already implemented. Several advantages have been identified over other ecosystem accounting metrics. These advantages include lower operating costs, a more integrative vision, adaptability to different spatial scales, flexibility in modifying its structure, the capacity to incorporate complex urban dynamics, reduced dependence on human judgment and easier interpretation of results.