Oil spill detection using optical sensors: a multi-temporal approach

Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated

中国における都市化総合評価及び環境への影響に関する研究

In Chapter one, research background and significance is investigated. In addition, previous studies and current situation in the research fields was reviewed and discussed. In Chapter two, an in-depth review of prior studies associated with the research topic was conducted. The literature review was carried out from three aspects: urbanization and eco-environment evalution and coordination, urban sprawl assessment and urban heat island investigation. In Chapter three, maximum entropy method was applied to help generate the evaluation system of eco-environment level and urbanization level at provincial scale. Comparison analysis and coordinate analysis was carried through to assess the development of urbanization and eco-environment as well as the balance and health degree of the city develops. In Chapter four, DMSP/OLS stable nighttime light dataset was used to measure and assess the urban dynamics from the extraction of built up area. Urban sprawl was evaluated by analyzing the landscape metrics which provided general understanding of the urban sprawl and distribution pattern characteristics could be got from the evaluation. In Chapter five, the investigation of surface urban heat island effects in Beijing city which derive from land surface temperature retrieval from remote sensing data of Landsat TM was carried out. In addition, spatial correlation and relationship between the urbanization level, vegetation coverage and surface urban heat island was carried out in this chapter. In Chapter six, all the works have been summarized and a conclusion of whole thesis is deduced.北九州市立大

Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images

AbstractUrbanization is a human-dominated process and has greatly impacted biodiversity, ecosystem processes, and regional climate. In this study we assess the effect of different degrees of urbanization on land surface temperature using remote sensing images. Landsat TM images were used for land surface temperature retrieval using the algorithm proposed by Artis and Carnahan. ALOS multispectral images were used for landcover classification using classification trees in three study areas, namely Xicheng district(A), Haidian district(B), Shijingshan district(C), of different degrees of urbanization in Beijing. Landcover-specific surface temperatures were estimated through an inversion alorithm. At the different degrees of urbanization, reducing the within-pixel coverage ratio of vegetations will result in an land surface temperature rise. Quantitative assessment of the relationship between different degrees of urbanization and land surface temperature was simulated by an urbanization index which integrates the coverage ratio of built-up landcover type and the cell-average NDVI. Urbanization indices of the Xicheng district, Haidian district, Shijingshan district were calculated to be 0.91, 0.72, and, 0.55 respectively. Such results are consistent with the trend of evaluation using quantitative estimation land surface temperature

Image Based Surface Temperature Extraction and Trend Detection in an Urban Area of West Bengal, India

Rapid urbanization and change of landuse/landcover results in changes of the thermal spectrum of a city even in small cities like English Bazaar Municipality (EBM) of Malda district. Monitoring the spatio-temporal surface temperature patterns is important, therefore, the present paper attempts to extract spatio-temporal surface temperature from thermal band of Landsat imageries and tries to validate it with factor based Land Surface Temperature (LST) models constructed based on six proxy temperature variables for selected time periods (1991, 2010 and 2014). Seasonal variation of temperature is also analyzed from the LST models over different time phases. Landsat TIRS based LST shows that in winter season, the minimum and maximum LST have raised up 2.32°C and 3.09°C in last 25 years. In pre monsoon season, the increase is much higher (2.80°C and 6.74°C) than in the winter period during the same time frame. In post monsoon season, exceptional situation happened due to high moisture availability caused by previous monsoon rainfall spell. Trend analysis revealed that the LST has been rising over time. Expansion and intensification of built up land as well as changing thermal properties of the urban heartland and rimland strongly control LST. Factor based surface temperature models have been prepared for the same period of times as done in case of LST modeling. In all seasons and selected time phases, correlation coefficient values between the extracted spatial LST model and factor based surface temperature model varies from 0.575 to 0.713 and these values are significant at 99% confidence level. So, thinking over ecological growth of urban is highly required for making the environment ambient for living

Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention

Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

Using multi-temporal satellite images to evaluate the changes of vegetation index of land cover in Thai Binh Province

224pThis chapter describes the current status of Thai Binh province in Vietnam and its agricultural development plans for 2010. The environmental and economic impacts of pig production are discussed. The various stakeholders and their active involvement in agricultural production are analysed. In addition, an innovative approach to sustainable development of animal produce commodity chains in northern Vietnam, is described. The 12-month E3P Project (Environmental Protection and Pig Production) was aimed to establish baseline work for designing and implementing a geographical information system. A large proportion of unknown factors concerning the issue of effluents in the province was studied at the farm, communal and district, and on a scientific levels. These unknown factors justify the regional diagnosis presented by the E3P Project

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status

Projection of land surface temperature considering the effects of future land change in the Taihu Lake Basin of China

Land surface temperature (LST) is an important environmental parameter that is significantly affected by land use and landscape composition. Despite the recent progress in LST retrieval algorithms and better knowledge of the relationship between LST and land coverage indices, predictive studies of future LST patterns are limited. Here, we project LST patterns in the Taihu Lake Basin to the year 2026 based on projected land use pattern and simulated land coverage indices that include normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). We derived the spatiotemporal LST patterns in the Taihu Lake Basin from 1996 to 2026 using thermal infrared data from Landsat imagery. A CA-Markov model was applied to project the 2026 land use pattern in the basin based on spatial driving factors, using the 2004 land use as the initial state. We simulated the NDBI, NDVI and NDWI indices for 2026 using the projected land use patterns, and then generated the 2026 LST in the study area. Our results showed that LST has been increasing and the warming areas have been expanding since 1996, especially in the Su-Xi-Chang urban agglomeration. The mean LST in Su-Xi-Chang has increased from 31 degrees C in 2004 and has risen to about 33 degrees C in 2016, and the projection suggests that LST will reach about 35 degrees C in 2026. Our results also suggest that mean LST increased by 2 degrees C per decade in this highly urbanized area between 1996 and 2026. We present a preliminary method to produce future LST patterns and provide reasonable LST scenarios in the Taihu Lake Basin, which should help develop and implement management strategies for mitigating the effects of urban heat island

Mapping regional land cover and land use change using MODIS time series

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations