Mojokerto Regency is one of the regencies in East Java with a high population growth rate of around 0.96%, thus encouraging significant land use changes on built-up areas. Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information remains a challenge that depends on many factors such as complexity of the landscape, the remote sensing data selected, image processing, and classification methods. This study examined the accuracy assessment of LULC classification using Google Earth in Sadar Watershed, Mojokerto, East Java Indonesia for the years 2010, 2015, and 2020. The land use was classified into five categories; those are agriculture land (paddy field, field, and plantation), non-agriculture land (forest land, bushland, grazing land), bare land, settlement land, and water bodies. Around 85 random points were generated in ArcGIS and verified with Google Earth. The results showed that the Overall Accuracy of LULCC for 2010 was 80.2% and Kappa Coefficient was 0.74; for 2015, the Overall Accuracy was 85.3% and Kappa Coefficient was 0.8, and for 2020, the Overall Accuracy was 84.0%, and Kappa Coefficient was 0.79. All accuracy is considered as good categorized and acceptable in both overall accuracy and Kappa Coefficient.
Abstract. Rehabilitation of degraded forest land through implementation of carbon sink projects can increase terrestrial carbon stock. However, carbon emissions outside the project boundary, which is commonly referred to as leakage, may reduce or negate the sequestration benefits. This study assessed leakage from carbon sink projects that could potentially be implemented in the study area comprised of eleven sub-districts in the Batanghari District, Jambi Province, Sumatra, Indonesia.The study estimates the probability of a given land use/cover being converted into other uses/cover, by applying a logit model. The predictor variables were: proximity to the center of the land use area, distance to transportation channel (road or river), area of agricultural land, unemployment (number of job seekers), job opportunities, population density and income. Leakage was estimated by analyzing with and without carbon sink projects scenarios. Most of the predictors were estimated as being significant in their contribution to land use cover change.The results of the analysis show that leakage in the study area can be large enough to more than offset the project's carbon sequestration benefits during the period 2002-2012. However, leakage results are very sensitive to changes of carbon density of the land uses in the study area. By reducing C-density of lowland and hill forest by about 10% for the baseline scenario, the leakage becomes positive. Further data collection and refinement is therefore required. Nevertheless, this study has demonstrated that regional analysis is a useful approach to assess leakage.
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