The Klong U-Tapao watershed is the main source of water supply for agriculture, industry, and household consumption of the Songkhla province and it frequently contributes serious problems to lowland areas, particularly flood and soil erosion. Therefore, land use and land cover (LULC) scenario identification for optimum water yield and sediment retention ecosystem services are necessary. In this study, LULC data in 2010 and 2017 were firstly classified from Landsat data using random forests classifiers, and they were then used to predict LULC change during 2018 –2024 under three different scenarios by CLUE-S model. Later, actual LULC data in 2017 and predictive LULC data of three scenarios were further used to estimate water yield and sediment retention services under the InVEST and LULC scenario for optimum water yield and sediment retention ecosystem services were finally identified using the ecosystem service change index (ESCI). The result of the study showed the major increasing areas of LULC types during 2010–2017 were rubber plantation and urban and built-up area while the major decreasing areas of LULC classes were evergreen forest and miscellaneous land. In addition, the derived LULC prediction of three different scenarios could provide realistic results as expected. Likewise, water yield and sediment retention estimation of three different scenarios could also provide expected results according to characteristics of scenarios’ definitions and climates, soil and terrain, and LULC factors. Finally, LULC of Scenario II was chosen for optimum water yield and sediment retention ecosystem services. In conclusion, the integration of remote sensing technology with advanced classification methods and geospatial models can be used as proficient tools to provide geospatial data on water yield and sediment retention ecosystem services from different scenarios.
Land use and land cover change (LULCC) by unplanned and uncontrolled urban expansion have a significant effect on ecosystem service values (ESVs). Objectives of the study were (1) to extract LULC status and its change between 2006 and 2016; (2) to predict two different LULC scenarios in 2026 and; (3) to assess LULCC impact on ESVs. Herein, Landsat imageries in 2006, 2011 and 2016 were used to classify LULC types by object-based image analysis (OBIA) and the derived results were applied to predict LULC in 2026 of two scenarios by CLUE-S model and to assess the impact of LULCC on ESVs. Results revealed that paddy field and field crop notably decreased while urban and built-up areas and rangeland dramatically increased over the study periods whereas total ESVs declined from about 145 MM USD in 2006 to 132 MM USD in 2026 of Scenario II and the ESVs of three dominant ecosystem service functions (waste treatment, water supply, and climate regulation) continuously decreased. The impact of LULCC on ESVs remarkably differed among the LULC types. In conclusion, land use and city planners should try to minimize the effect of LULCC on ESVs during the planning process. Keywords:Land use and land cover change/ CLUE-S model/ Ecosystem service values/ Khon Kaen Province
An understanding of historical and present land use and land cover (LULC) information and its changes, such as urbanization and urban growth, is critical for city planners, land managers and resource managers in any rapidly changing landscape. To deal with this situation, the development of a new supervised classification method for multitemporal LULC mapping with long-term reliable information is necessary. The ultimate goal of this study was to develop a new classification method using harmonic analysis with a minimum spectral distance algorithm for multitemporal LULC mapping. Here, the Jiangning District of Nanjing City, Jiangsu Province, China was chosen as the study area. The research methodology consisted of two main components: (1) Landsat data selection and time-series spectral reflectance reconstruction and (2) multitemporal LULC classification using HA with a minimum spectral distance algorithm. The results revealed that the overall accuracy and Kappa hat coefficients of the four LULC maps in 2000, 2006, 2011, and 2017 were 97.03%, 90.25%, 91.19%, 86.32% and 95.35%, 84.48%, 86.74%, 80.24%, respectively. Further, the average producer accuracy and user accuracy of the urban and built-up land, agricultural land, forest land, and water bodies from the four LULC maps were 92.30%, 90.98%, 94.80%, 85.65% and 90.28%, 93.17%, 84.40%, 99.50%, respectively. Consequently, it can be concluded that the newly developed supervised classification method using harmonic analysis with a minimum spectral distance algorithm can efficiently classify multitemporal LULC maps.
Floods represent one of the most severe natural disasters threatening the development of human society worldwide, including in Thailand. In recent decades, Chaiyaphum province has experienced a problem with flooding almost every year. In particular, the flood in 2010 caused property damage of 495 million Baht, more than 322,000 persons were affected, and approximately 1046.4 km2 of productive agricultural area was affected. Therefore, this study examined how to optimize land use and land cover allocation for flood mitigation using land use change and hydrological models with optimization methods. This research aimed to allocate land use and land cover (LULC) to minimize the surface for flood mitigation in Mueang Chaiyaphum district, Chaiyaphum province, Thailand. The research methodology consisted of six stages: data collection and preparation, LULC classification, LULC prediction, surface runoff estimation, the optimization of LULC allocation for flood mitigation and mapping, and economic and ecosystem service value evaluation and change. According to the results of the optimization and mapping of suitable LULC allocation to minimize surface runoff for flood mitigation in dry, normal, and wet years using goal programming and the CLUE-S model, the suitable LULC allocation for flood mitigation in 2049 under a normal year could provide the highest future economic value and gain. In the meantime, the suitable LULC allocation for flood mitigation in 2049 under a drought year could provide the highest ecosystem service value and gain. Nevertheless, considering future economic and ecosystem service values and changes with surface runoff reduction, the most suitable LULC allocation for flood mitigation is a normal year. Consequently, it can be concluded that the derived results of this study can be used as primary information for flood mitigation project implementation. Additionally, the presented conceptual framework and research workflows can be used as a guideline for government agencies to examine other flood-prone areas for flood mitigation in Thailand.
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