In many areas of the world, population growth and land development have increased demand for land and other natural resources. Coastal areas are particularly susceptible since they are conducive for marine transportation, energy production, aquaculture, marine tourism and other activities. Anthropogenic activities in the coastal areas have triggered unprecedented land use change, depletion of coastal wetlands, loss of biodiversity, and degradation of other vital ecosystem services. The changes can be particularly drastic for small coastal islands with rich biodiversity. In this study, the influence of human modification on land surface temperature (LST) for the coastal island Hainan in Southern China was investigated. We hypothesize that for this island, footprints of human activities are linked to the variation of land surface temperature, which could indicate environmental degradation. To test this hypothesis, we estimated LST changes between 2000 and 2016 and computed the spatio-temporal correlation between LST and human modification. Specifically, we classified temperature data for the four years 2000, 2006, 2012 and 2016 into 5 temperature zones based on their respective mean and standard deviation values. We then assessed the correlation between each temperature zone and a human modification index computed for the year 2016. Apart from this, we estimated mean, maximum and the standard deviation of annual temperature for each pixel in the 17 years to assess the links with human modification. The results showed that: (1) The mean LST temperature in Hainan Island increased with fluctuations from 2000 to 2016. (2) The moderate temperature zones were dominant in the island during the four years included in this study. (3) A strong positive correlation of 0.72 between human modification index and mean and maximum LST temperature indicated a potential link between human modification and mean and maximum LST temperatures over the 17 years of analysis. (4) The mean value of human modification index in the temperature zones in 2016 showed a progressive rise with 0.24 in the low temperature zone, 0.33 in the secondary moderate, 0.45 in the moderate, 0.54 in the secondary high and 0.61 in the high temperature zones. This work highlighted the potential value of using large and multi-temporal earth observation datasets from cloud platforms to assess the influence of human activities in sensitive ecosystems. The results could contribute to the development of sustainable management and coastal ecosystems conservation plans.
Shorelines are vulnerable to anthropogenic activities including urbanization, land reclamation and sediment loading. Shoreline changes may be a reflection of the degradation of coastal ecosystems because of human activities. Understanding the shoreline dynamics is, therefore, a topic of global concern. Earth observation data, such as multi-temporal satellite images, are an important resource for assessing changes in coastal ecosystems. In this research, we used Google Earth Engine (GEE) to monitor and map historical shoreline dynamics in the Hangzhou Bay in China where the Qiantang River flows into the East China Sea. Specifically, we aimed to capture and quantify both the spatial and temporal shoreline changes and to assess the link between anthropogenic activities and shoreline changes on the integrity of this coastal area. We implemented a Tasselled Cap analysis (TCA) on Landsat imagery from 1985 to 2018 in GEE to calculate the wetness coefficient. We then applied Otsu method for automatic image thresholding on the wetness coefficient to detect waterbodies and shoreline changes. Further, we adopted the nighttime light data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) from 1992 to 2013 as a proxy of human activities. The results show that in the hotspot areas, the shoreline has moved by more than 5 km in the last decades, accounting for approximately 900 km 2 of land accretion. Within this area, the human activity, indicated by the intensity of nighttime light, increased significantly. The results of this work reveal the influence of human activities on the shoreline dynamics and can support policies that promote the sustainable use and conservation of coastal environments. Our methodology can be transferred and applied to other coastal zones in various regions and scaled up to larger areas.
Construction of transportation infrastructure is a vital step in boosting economic and societal opportunities and often results in land use changes. In this study, we focus on the land use dynamics of the urban agglomeration around Hangzhou Bay, where the Qiantang River flows into the East China Sea. The Hangzhou Bay Bridge crosses the bay since 2008. We used Interrupted Time Series Analysis (ITSA) to analyze the influence of the bridge on the land use and land cover (LULC) time series of the surrounding areas and on socio-economic indicators. We applied the Random Forest method to classify Landsat imagery from 2000 to 2017, thus enabling us to quantify LULC changes before and after the construction of the Hangzhou Bay Bridge. Google Earth Engine (GEE) was used for data acquisition, pre-processing, and classification. The results showed that during the period from 2000 to 2017, impervious surface areas expanded rapidly at the expense of agricultural land, and this transformation continued even more rapidly after 2008. ITSA showed that the driver behind the impervious surface area expansion switched from residential and industrial area growth in 2000-2008, to exclusively infrastructure area growth in 2008-2017. The construction of the bridge accelerated the expansion of impervious surface in the joint area of the bridge-connected cities of Ningbo and Jiaxing. With the Hangzhou Bay Bridge connection, various socio-economic factors, including tourism, GDP, tertiary industry, real estate investment, and highway freight, increased rapidly. The outcomes of this research could contribute to policymaking and impact assessments for sustainable urban development and land management. The methods used in this study are universal and therefore can also be used to assess the effect of any notable event that may impact LULC change.
Tourism is a primary socio-economic factor on many coastal islands. Tourism contributes to the livelihoods of the residents, but also influences natural resources and energy consumption and can become a significant driver of land conversion and environmental change. Understanding the influence of tourist-related activities is vital for sustainable tourism development. We chose Hainan Island in South China as a research area to study the influence of tourist-driven activities on environmental variables (as Land Surface Temperatures (LST) and related ecosystem variables) during the period of 2000 to 2019. In Hainan, the local economy relies heavily on tourism, with an ever-growing influx of tourists each year. We categorised location-based points of interest (POIs) into two classes, non-tourism sites and tourism-related sites, and utilised satellite data from the cloud-based platform Google Earth Engine (GEE) to extract LST and Normalized Difference Vegetation Index (NDVI) data. We analysed the LST variations, NDVI changes and the land use/land cover (LULC) changes and compared the relative difference in LST and NDVI between the tourism-related sites and non-tourism-related sites. The main findings of this study were: (1) The median LST in the tourism-related sites was relatively higher (1.3) than the LST in the non-tourism-related sites for the 20 years. Moreover, every annual mean LST of tourism-related sites was higher than the LST values in non-tourism-related sites, with an average difference of 1.2 °C for the 20 years and a maximum difference of 1.7 °C. We found higher annual LST anomalies for tourist-related sites compared to non-tourism sites after 2010, which indicated the likely positive differences in LST above the average LST during 20 years for tourism-related sites when compared against the non-tourism related sites, thus highlighting the potential influence of tourism activities on LST. (2) The annual mean NDVI value for tourism-related sites was significantly lower than for non-tourism places every year, with an average NDVI difference of 0.26 between the two sites. (3) The land cover changed significantly: croplands and forests reduced by 3.5% and 2.8% respectively, while the areas covered by orchards and urban areas increased by 2% and 72.3% respectively. These results indicate the influence of the tourism-driven activities includes the relatively high LST, vegetation degradation and land-use conversion particular to urban cover type. The outcome of this work provides a method that combines cloud-based satellite-derived data with location-based POIs data for quantifying the long-term influence of tourism-related activities on sensitive coastal ecosystems. It contributes to designing evidence-driven management plans and policies for the sustainable tourism development in coastal areas.
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