Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl. (January 2019) - Grădinaru, Simona R.; Kienast, Felix; Psomas, Achilleas
Highlights: We present a Landsat-based method for mapping abandoned land in heterogeneous and fragmented urban landscapes. The method makes use of NDVI values and decision trees. Vegetation phenology is accounted for indetermining which period of the growing season fits best for mapping. Scenes encompassing the beginning of the vegetation growing season enhance classification accuracy. Monitoring land abandonment and urban sprawl can support evaluation of land-use regulations and planning strategies. Abstract: Studies have shown that spatial information on abandoned land could play an important role in urban land management, as land abandonment was proven capable of revealing future trajectories of change. However, mapping land abandonment with traditional methods (e.g., field work, digitization of aerial images) can be time consuming, expensive, and require considerable man-power. In this context, Landsat imagery proves to be a reliable source of data. To our knowledge, the potential of Landsat imagery for mapping abandoned land has not been tested in heterogeneous and highly fragmented urban settings. The aim of our paper is to propose a resource-efficient (i.e., in terms of time and manpower) method for the assessment of land abandonment in areas affected by urban sprawl, by using seasonal time series of Landsat data. Bucharest, Romania was chosen as case study area. Landsat scenes from the year 2013 are grouped based on vegetation phenology into four PGSs (periods of the growing season). NDVI values corresponding to land abandonment are analyzed with Classification and Regression Trees. A total of 23 models—representing combinations of PGSs—are tested in order to determine what period of the vegetation growing season fits best for mapping abandoned land, and for the purpose of deriving such a map for Bucharest. Finally, results are validated against independent data and a resource estimation for the entire mapping process is performed. Results show that abandoned land can be mapped with Landsat imagery with accuracies above 80%. Higher accuracies are obtained when scenes encompassing the beginning of the vegetation growing season are included in the models. We also observed that accuracy tends to decrease from models which include PGSs representing the beginning of the vegetation growing season towards those representing the end. Estimation showed that mapping land abandonment with Landsat data could reduce time and workforce resources by almost half compared with aerial imagery and field work. As our method is rapid, easy to implement, and based on freely available data, it can be used by local authorities that cannot allocate significant resources for land change monitoring. Furthermore, the approach could provide objective information to municipalities where official statistics on land abandonment are unreliable or of low quality.