No abstract

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.

Human activity and land-use changes have affected the water quality of Kwan Phayao, Upper Ing watershed, due to the associated high sediment load and eutrophication. This study aims to identify suitable LULC allocation scenarios for minimizing sediment and nutrient export into the lake. For this purpose, the LULC status and change were first assessed, based on classified LULC data in 2009 and 2019 from Landsat images, using the SVM algorithm. Later, the land requirements of three scenarios between 2020 and 2029 were estimated, based on their characteristics, and applied to predict LULC change using the CLUE-S model. Then, actual LULC data in 2019 and predicted LULC data under three scenarios between 2020 and 2029 were used to estimate sediment and nutrient export using the SDR and NDR models. Finally, the ecosystem service change index identified a suitable LULC allocation for minimizing sediment or/and nutrient export. According to the results, LULC status and change indicated perennial trees and orchards, para rubber, and rangeland increased, while forest land and paddy fields decreased. The land requirements of the three scenarios provided reasonable results, as expected, particularly Scenario II, which adopts linear programming to calculate the land requirements for maximizing ecosystem service values. For sediment and nutrient export estimation under the predicted LULC for the three scenarios, Scenario II led to the lowest yield of sediment and nutrient exports, and provided the lowest average ESCI value among the three scenarios. Thus, the LULC allocation under Scenario II was chosen as suitable for minimizing sediment or/and nutrient export into Kwan Phayao. These results can serve as crucial information to minimize sediment and nutrient loads for land-use planners, land managers, and decision makers.

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

Spatiotemporal particulate matter (PM) concentration prediction using MODIS AOD with significant PM factors in rural and urban landscapes in Thailand is necessary for public health and has been complicated by the limitations of PM monitoring stations. The research objectives were (1) to identify significant factors affecting PM10 concentrations in rural landscapes and PM2.5 in urban landscapes; (2) to predict spatiotemporal PM10 and PM2.5 concentrations using geographically weighted regression (GWR) and mixed-effect model (MEM), and (3) to evaluate a suitable spatiotemporal model for PM10 and PM2.5 concentration prediction and validation. The research methodology consisted of four stages: data collection and preparation, the identification of significant spatiotemporal factors affecting PM concentrations, the prediction of spatiotemporal PM concentrations, and a suitable spatiotemporal model for PM concentration prediction and validation. As a result, the predicted PM10 concentrations using the GWR model varied from 50.53 to 85.79 µg/m3 and from 36.92 to 51.32 µg/m3 in winter and summer, while the predicted PM10 concentrations using the MEM model varied from 50.68 to 84.59 µg/m3 and from 37.08 to 50.81 µg/m3 in both seasons. Likewise, the PM2.5 concentration prediction using the GWR model varied from 25.33 to 44.37 µg/m3 and from 16.69 to 24.04 µg/m3 in winter and summer, and the PM2.5 concentration prediction using the MEM model varied from 25.45 to 44.36 µg/m3 and from 16.68 and 23.75 µg/m3 during the two seasons. Meanwhile, according to Thailand and U.S. EPA standards, the monthly air quality index (AQI) classifications of the GWR and MEM were similar. Nevertheless, the derived average corrected Akaike Information Criterion (AICc) values of the GWR model for PM10 and PM2.5 predictions during both seasons were lower than that of the MEM model. Therefore, the GWR model was chosen as a suitable model for spatiotemporal PM10 and PM2.5 concentration predictions. Furthermore, the result of spatial correlation analysis for GWR model validation based on a new dataset provided average correlation coefficient values for PM10 and PM2.5 concentration predictions with a higher than the expected value of 0.5. Subsequently, the GWR model with significant monthly and seasonal factors could predict spatiotemporal PM 10 and PM2.5 concentrations in rural and urban landscapes in Thailand.