Technical foundation research on high resolution remote sensing system of China's coastal zone

China's coastal zone is a region with a highly developed economy that contrasts clearly with the slow paced regular investigation on its natural environment,which cannot keep pace with the requirement of economic development and modern management.Laying a theoretical foundation for the modern management of China's costal zone is aimed at. This research focuses on the following processing and analyzing technologies for coastal zone high-resolution remote sensing data: organization and management of large amounts of high-resolution remote sensing data, quick and precise spatial positioning system,algorithms for image fusion in feature level and coastal zone feature extraction. They will form a technical foundation of the system. And, if combined with other research results such as coastal zone remote sensing classification system and its mapping subsystem, an advanced technical frame for remote sensing investigation of coastal zone resource will be constructed.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

The application of very high resolution satellite image in urban vegetation cover investigation:a case study of Xiamen City

With the technological improvements of satellite sensors, we will acquire more information about the earth so that we have reached a new application epoch of observation on earth environmental change and cartography. But with the enhancement of spatial resolution, some questions have arisen in the application of using traditional image processing and classification methods. Aiming for such questions, we studied the application of IKONOS very high resolution image (1 m) in Xiamen City on Urban Vegetation Cover Investigation and discussed the difference between the very high resolution image and traditional low spatial resolution image at classification, information abstraction etc. It is an advantageous test for the large-scale application of very high resolution data in the future.

CHEOS Major Special Project Promotes Development of Chinese Commercial Remote Sensing Cameras

High resolution remote sensing data has been applied in many fields such as national security, economic construction and in the daily life of the general public around the world, creating a huge market. Commercial remote sensing cameras have been developed vigorously throughout the world over the last few decades, resulting in resolutions down to 0.31 m. In 2010, the Chinese government approved the implementation of the China High-resolution Earth Observation System(CHEOS) Major Special Project, giving priority to development of high resolution remote sensing satellites. More than half of CHEOS has been constructed to date and 5 satellites operate in orbit. These cameras have different characteristics. A number of innovative technologies have been adopted, which have led to camera performance increasing in leaps and bounds. The products and the production capability enables the remote sensing technical level to increase making it on a par with Europe and the US.

Compilation of 1:50,000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the North-South transitional zone of China

The compilation of 1:250,000 vegetation type map in the North-South transitional zone and 1:50,000 vegetation type maps in typical mountainous areas is one of the main tasks of Integrated Scientific Investigation of the North-South Transitional Zone of China.In the past,vegetation type maps were compiled by a large number of ground field surveys.Although the field survey method is accurate,it is not only time-consuming,but also only covers a small area due to the limitations of physical environment conditions.Remote sensing data can make up for the limitation of field survey because of its full coverage.However,there are still some difficulties and bottlenecks in the extraction of remote sensing information of vegetation types,especially in the automatic extraction.As an example of the compilation of 1:50,000 vegetation type map,this paper explores and studies the remote sensing extraction and mapping methods of vegetation type with medium and large scales based on mountain altitudinal belts of Taibai Mountain,using multi-temporal high resolution remote sensing data,ground survey data,previous vegetation type map and forest survey data.The results show that:1)mountain altitudinal belts can effectively support remote sensing classification and mapping of 1:50,000 vegetation type map in mountain areas.Terrain constraint factors with mountain altitudinal belt information can be generated by mountain altitudinal belts and 1:10,000 Digital Surface Model(DSM)data of Taibai Mountain.Combining the terrain constraint factors with multi-temporal and high-resolution remote sensing data,ground survey data and previous small-scale vegetation type map data,the vegetation types at all levels can be extracted effectively.2)The basic remote sensing interpretation and mapping process for typical mountains is interpretation of vegetation type-groups→interpretation of vegetation formation groups,formations and subformations→interpretation and classification of vegetation types&subtypes,which is a combination method of top-down method and bottom-up method,not the top-down or the bottom-up classification according to the level of mapping units.The results of this study provide a demonstration and scientific basis for the compilation of large and medium scale vegetation type maps.

Two drifting paths of Sargassum bloom in the Yellow Sea and East China Sea during 2019-2020

The macroalgal blooms of floating brown algae Sargassum horneri are increasing in the Yellow Sea and East China Sea during the past few years.However,the annual pattern of Sargassum bloom is not well characterized.To study the developing pattern and explore the impacts from hydro-meteorologic environment,high resolution satellite imageries were used to monitor the distribution,coverage and drifting of the pelagic Sargassum rafts in the Yellow Sea and East China Sea from September 2019 to August 2020.Sargassum blooms were detected from October 2019 to June 2020 and presented two successive drifting paths that both initiated from around 37°N.The first path spanned smaller spatial scale and shorter period,starting with a bloom of 3 km^(2) distribution area near the eastern tip of Shandong Peninsula in late October 2019 and drifted southwards,hit the Pyropia aquaculture area in early January 2020,then vanished in the northwest of East China Sea(ca.32°N)around end of January.The second path began with a large distribution area of 23000 km^(2) east of 123°E in late January 2020,firstly moved southwards in the central Yellow Sea and northern East China Sea(north of 29°N)till late April,then turned northwards with monsoon wind and vanished from late June to August.The mean sea surface temperature of 8℃ to 20℃ in the Sargassum bloom areas corresponded to in situ observed temperature range for vegetative growth and floating of S.horneri.There was no observed floating Sargassum blooms during July through September in the Yellow Sea and East China Sea.The results indicate that floating S.horneri is unable to complete life cycle in the Yellow Sea and East China Sea,and provide insights to the future management of Sargassum blooms.Further studies are needed to validate the pattern and source of annual Sargassum bloom in the Yellow Sea and East China Sea.

A quantitative approach to monitoring new sand cay migration in Nansha Islands

Sand cay is a special kind of islet formed by coral detritus and bioclast, which is common in Nansha Islands of China. Some sand cays play an important role in ocean strategy and economy, but surprisingly we know little about them, especially those recently formed sand cays. In this research, we monitor migration of a new sand cay in Nanxun Jiao（Gaven Reef） using a series of Quick Bird and World View-2 satellite images between June 2006 and August 2013. We conduct a regression between migration distance and wind observational data to examine the migration patterns of the new sand cay. The migration distance is calculated based on the sand cay locations extracted based on Normalized Difference Water Index（NDWI）. The wind observational data downloaded from NOAA are reformed into four wind direction vectors. Based on the results of regression, we concluded that the migration of the new sand cay on Nanxun Jiao was significantly associated with the east, west and north wind.East wind was the main influence factor of the migration; its impact strength was almost twice as the west and north wind. The south wind has little effect on the migration of the sand cay, which is partly blocked by the artificial structure in the south.