Documenting temporal changes to coastal zones is an essen­tial part of understanding and managing these environ­ments. The exclusive use of traditional surveying tools may not be practical for monitoring large, remote, or rapidly changing areas. This paper investigates the utility of multispectral Landsat Thematic Mapper satellite data for docu­menting changes to a Caribbean coastal zone using the change vector analysis processing technique. The area of study was the coastal region near the village of Buen Hombre on the north coast of the Dominican Republic. The primary habitats of interest were the intertidal mangrove for­ ests, and the shallow water seagrasses, macroalgae, and coral reefs. The change vector analysis technique uses any number of spectral bands from multidate satellite data to produce change images that yield information about both the magnitude and direction of differences in pixel values (which are proportional to radiance). The final products were created by appending color-coded change pixels onto a black-and-white base map. The advantages and limitations of the technique for coastal inventories are discussed.

The Island of Tongatapu, Tonga, was struck by a tsunami on January 15, 2022. Internet was cut off from the island, which made remote sensing a valuable tool for the assessment of damages. Through land cover classification, change vector analysis and log-ratio image differencing, damages caused by the tsunami were assessed remotely in this thesis. Damage assessment is a vital part of both assessing the need for humanitarian aid after a tsunami, but also lays the foundation for preventative measurements and reconstruction. The objective of this thesis was to assess damage in terms of square kilometers and create damage maps. It was also vital to assess the different methods and evaluate their accuracy. Results from this study could theoretically be combined with other damage assessments to evaluate different aspects of damage. It was also important to evaluate which methods would be good to use in a similar event. In this study Sentinel-1, Sentinel-2 and high-resolution Planet Imagery were used to conduct a damage assessment. Evaluating both moderate and high-resolution imagery in combination with SAR yielded plausible, but flawed results. Land cover was computed for moderate and high-resolution imagery using three types of classifiers. It was found that the Random Forest classifier outperforms both CART and Support Vector Machine classification for this study area.  Land cover composite image differencing for pre-and-post tsunami Sentinel-2 images achieved an accuracy of around 85%. Damage was estimated to be about 10.5 km^2. Land cover classification with high-resolution images gave higher accuracy. The total estimated damaged area was about 18 km^2. The high-resolution image classification was deemed to be the better method of urban damage assessment, with moderate-resolution imagery working well for regional damage assessment.  Change vector analysis provided plausible results when using Sentinel-2 with NDVI, NDMI, SAVI and BSI. NDVI was found to be the most comprehensive change indicator when compared to the other tested indices. The total estimated damage using all tested indices was roughly 7.6 km^2. Using the same method for Sentinel-1's VV and VH bands, the total damage was estimated to be 0.4 and 2.6 km^2 respectively. Log ratio for Sentinel-1 did not work well compared to change vector analysis. Issues with false positives occurred. Both log-ratios of VV and VH gave a similar total estimated damage of roughly 5.2 km^2.  Problems were caused by cloud cover and ash deposits. The analysis could have been improved by being consistent with the choice of dates for satellite images. Also, balancing classification samples and using high-resolution land cover classification on specific areas of interest indicated by regional methods. This would circumvent problems with ash, as reducing the study area would make more high-resolution imagery available.

Along with the increasing rate of urbanization takes place in the world, the population keeps shifting from rural to urban areas. China, as the country of the largest population, has the highest urban population growth in Asia, as well as the world. However, the urbanization in China, in turn, is leading to a lot of social issues which reshape the living environment and cultural fabric. A variety of these kinds of social issues emphasize the challenges regarding a healthy and sustainable urban growth particularly in the reasonable planning of urban land use and land cover features. Therefore, it is significant to establish a set of comprehensive urban sustainable development strategies to avoid detours in the urbanization process. Nowadays, faced with such as a series of the social phenomenon, the spatial and temporal technological means including Remote Sensing and Geographic Information System (GIS) can be used to help the city decision maker to make the right choices. The knowledge of land use and land cover changes in the rural and urban area assists in identifying urban growth rate and trend in both qualitative and quantitatively ways, which provides more basis for planning and designing a city in a more scientific and environmentally friendly way. This paper focuses on the urban sprawl analysis in Nanjing, Jiangsu, China that being analyzed by urban growth pattern monitoring during a study period. From 2001 to 2016, Nanjing Municipality has experienced a substantial increase in the urban area because of the growing population. In this paper, one optimal supervised classification with high accuracy which is Support Vector Machine (SVM) classifier was used to extract thematic features from multitemporal satellite data including Landsat 7 ETM+, Landsat 8, and Sentinel-2A MSI. It was interpreted to identify the existence of urban sprawl pattern based on the land use and land cover features in 2001, 2006, 2011, and 2016. Two different types of change detection analysis including post-classification comparison and change vector analysis (CVA) were performed to explore the detailed extent information of urban growth within the study region. A comparison study on these two change detection analysis methods was carried out by accuracy assessment. Based on the exploration of the change detection analysis combined with the current urban development actuality, some constructive recommendations and future research directions were given at last. By implementing the proposed methods, the urban land use and land cover changes were successfully captured. The results show there is a notable change in the urban or built-up land feature. Also, the urban area is increased by 610.98 km2 while the agricultural land area is decreased by 766.96 km2, which proved a land conversion among these land cover features in the study period. The urban area keeps growing in each particular study period while the growth rate value has a decreasing trend in the period of 2001 to 2016. Besides, both change detection techniques obtained the similar result of the distribution of urban expansion in the study area. According to the result images from two change detection methods, the expanded urban or built-up land in Nanjing distributes mainly in the surrounding area of the central city area, both side of Yangtze River, and Southwest area. The results of change detection accuracy assessment indicated the post-classification comparison has a higher overall accuracy 86.11% and a higher Kappa Coefficient 0.72 than CVA. The overall accuracy and Kappa Coefficient for CVA is 75.43% and 0.51 respectively. These results proved the strength of agreement between predicted and truth data is at ‘good’ level for post-classification comparison and ‘moderate’ for CVA. Also, the results further confirmed the expectation from previous studies that the empirical threshold determination of CVA always leads to relatively poor change detection accuracy. In general, the two change detection techniques are found to be effective and efficient in monitoring surface changes in the different class of land cover features within the study period. Nevertheless, they have their advantages and disadvantages on processing change detection analysis particularly for the topic of urban expansion.