Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm

In the complex process of urbanization, retrieving its dynamic expansion trajectories with an efficient method is challenging, especially for urban regions that are not clearly distinguished from the surroundings in arid regions. In this study, we propose a framework for extracting spatiotemporal change information on urban disturbances. First, the urban built-up object areas in 2000 and 2020 were obtained using object-oriented segmentation method. Second, we applied LandTrendr (LT) algorithm and multiple bands/indices to extract annual spatiotemporal information. This process was implemented effectively with the support of the cloud computing platform of Earth Observation big data. The overall accuracy of time information extraction, the kappa coefficient, and average detection error were 83.76%, 0.79, and 0.57 a, respectively. These results show that Karachi expanded continuously during 2000–2020, with an average annual growth rate of 4.7%. However, this expansion was not spatiotemporally balanced. The coastal area developed quickly within a shorter duration, whereas the main newly added urban regions locate in the northern and eastern inland areas. This study demonstrated an effective framework for extract the dynamic spatiotemporal change information of urban built-up objects and substantially eliminate the salt-and-pepper effect based on pixel detection. Methods used in our study are of general promotion significance in the monitoring of other disturbances caused by natural or human activities.

Spatiotemporal Change and the Natural–Human Driving Processes of a Megacity’s Coastal Blue Carbon Storage

Coastal blue carbon storage (CBCS) plays a key role in addressing global climate change and realizing regional carbon neutrality. Although blue carbon has been studied for some years, there is little understanding of the influence of a megacity’s complex natural and human-driven processes on CBCS. Taking the Shanghai coastal area as an example, this study investigated the spatiotemporal change in CBCS using the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model during 1990–2015, and analyzed the response of the CBCS to a megacity’s complex natural- and human-driven processes through a land use/land cover transition matrix and hierarchical clustering. The results were as follows: (1) Thirty-three driving processes were identified in the study area, including four natural processes (e.g., accretion, succession, erosion, etc.), two human processes (reclamation and restoration) and twenty-seven natural–human coupled processes; they were further combined into single and multiple processes with positive and negative influences on the CBCS into four types (Mono+, Mono−, Multiple+ and Multiple− driving processes). (2) Shanghai’s CBCS increased from 1659.44 × 104 Mg to 1789.78 ×104 Mg, though the amount of Shanghai’s coastal carbon sequestration showed a decreasing trend in three periods: 51.28 × 104 Mg in 1990–2000, 42.90 × 104 Mg in 2000–2009 and 36.15 × 104 Mg in 2009–2015, respectively. (3) There were three kinds of spatiotemporal patterns in the CBCS of this study area: high adjacent to the territorial land, low adjacent to the offshore waters in 1990; high in the central part, low in the peripheral areas in 2009 and 2015; and a mixed pattern in 2000. These patterns resulted from the different driving processes present in the different years. This study could serve as a blueprint for restoring and maintaining the CBCS of a megacity, to help mitigate the conflicts between socioeconomic development and the conservation of the CBCS, especially in the Shanghai coastal area.

Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.