scholarly journals Remote Sensing Based Spatial-Temporal Monitoring of the Changes in Coastline Mangrove Forests in China over the Last 40 Years

2021 ◽  
Vol 13 (10) ◽  
pp. 1986
Author(s):  
Junyao Zhang ◽  
Xiaomei Yang ◽  
Zhihua Wang ◽  
Tao Zhang ◽  
Xiaoliang Liu
Keyword(s):  
Remote Sensing ◽  
Landscape Pattern ◽  
Remote Sensing Data ◽  
Distribution Area ◽  
Mangrove Forests ◽  
Landscape Index ◽  
National Scale ◽  
Spatial Changes ◽  
Temporal And Spatial ◽  
Degree Of Fragmentation

As a developing country, China’s mangrove landscape pattern has undergone significant temporal and spatial changes over the last four decades. However, we know little about the changes in the mangrove landscape pattern characteristics other than the area at the national scale. The analysis of mangrove landscape pattern changes from different perspectives on a national scale can provide scientific support for mangrove protection and restoration. In this study, the temporal and spatial changes in the pattern of the mangrove landscape over the last 40 years in China were analyzed based on remote sensing data with high classification accuracy (99.3% of 2018). First, according to the natural geographical conditions of the coastal zone and the distribution of the mangroves, the distribution area of the mangroves in China was divided into 31 natural shores. Then, by selecting representative landscape indexes and constructing an integrated landscape index, the spatial-temporal changes in the landscape pattern of China’s mangroves over the last 40 years were analyzed based on five perspectives: Total area change, shape complexity, connectivity, fragmentation, and the integrated state of the landscape. From a temporal viewpoint, before 2000, the total area of each shore exhibited a downward trend, and the degree of connectivity deteriorated continuously, but the degree of fragmentation was stable at a low level. After 2000, although the total area improved, the degree of fragmentation gradually increased. The spatial changes are mainly reflected by the fact that the shores in Guangdong and Hainan exhibited significant differences within the same province. Based on the above analysis, corresponding scientific suggestions are proposed from temporal and spatial viewpoints to provide guidance for mangrove management and protection in China and to provide a reference for mangrove research in other regions of the world.

scholarly journals Modeling and Analysis of Ecological Urban Landscape Pattern Evolution Based on Multisource Remote Sensing Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhang Min ◽  
Wang Xuejie ◽  
Liu Yun
Keyword(s):  
Remote Sensing ◽  
Landscape Pattern ◽  
Green Space ◽  
Urban Landscape ◽  
Remote Sensing Data ◽  
Modeling And Analysis ◽  
Sensing Data ◽  
Development Level ◽  
Modeling Analysis ◽  
Degree Of Fragmentation

Considering that the development of urbanization cannot be separated from the application of landscape pattern evolution, in order to improve the development level of ecocity, a modeling analysis of ecological urban landscape pattern evolution based on multisource remote sensing data is proposed. Taking ecotype city as the research object, the remote sensing images of ecological urban landscape pattern are screened by using multisource remote sensing data and nonremote sensing data as the basic data. CA-Markov model is constructed and the evolution of ecological urban landscape pattern is analyzed. The experimental results show that, from 2005 to 2020, the development level of urbanization process is higher and higher and the area of building land patches is increasing, which reduces the fragmentation of building land patches. However, the landscape of cultivated land and green space is less and less, and the distribution of the patches also causes uneven phenomenon, which leads to the gradual decline of ecological urban landscape diversity. In the ecological urban landscape pattern, the degree of fragmentation and diversity of urban landscape is reduced due to the high connection and coverage of the construction land.

scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

scholarly journals Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

2021 ◽  
Vol 10 (1) ◽  
pp. 14
Author(s):  
Mengyu Ge ◽  
Shenghui Fang ◽  
Yan Gong ◽  
Pengjie Tao ◽  
Guang Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landscape Pattern ◽  
Local Development ◽  
3D Structure ◽  
Remote Sensing Data ◽  
Landscape Patterns ◽  
Surface Pattern ◽  
Entire Study ◽  
The City

Urbanization is changing the world’s surface pattern more and more drastically, which brings many social and ecological problems. Quantifying the changes in the landscape pattern and 3D structure of the city is important to understand these issues. This research study used Melbourne, a compact city, as a case study, and focused on landscape patterns and vertical urban volume (volume mean (VM), volume standard deviation (VSD)) and investigate the correlation between them from the scope of different scales and functions by Remote Sensing (RS) and Geographic Information System (GIS) techniques. We found: (1) From 2000 to 2012, the landscape pattern had a trend of decreasing fragmentation and increasing patch aggregation. The growth of VM and VSD was more severe than that of landscape metrics, and presented a “high–low” situation from the city center to the surroundings, maintaining the structure of “large east and small west”. (2) Landscape pattern was found closely associated with the urban volume. In the entire study area, landscape pattern patches with low fragmentation and high aggregation were directly proportional to VM with high value, which represented high urbanization, and patches with high connectivity and fragmentation had a positive relationship with high VSD, which represented strong spatial recognition. (3) The urban volumes of different urban functional areas were affected by different landscape patterns, and the analysis based on the local development situation can explain the internal mechanism of the interaction between the landscape pattern and the urban volume.

scholarly journals Temporal Geomorphological Changes in Nanak Sagar Reservoir (INDIA) Using Satellite Data

2016 ◽  
Vol 11 (2) ◽  
pp. 458-465
Author(s):  
Nandkishor A. Ingole ◽  
R. N Ram ◽  
A. S Nain ◽  
Prem Kumar
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Landsat 8 ◽  
Spatial Changes ◽  
Spread Area ◽  
Liss Iii ◽  
Dead Storage ◽  
Large Water ◽  
Geomorphological Changes

An attempt was made to assess the sedimentation in Nanak Sagar reservoir using multi-dated Remote Sensing (RS) images. IRS LISS III and Landsat-8 (OLI) remote sensing data were taken in the year 2007 and 2015, respectively. The original capacity curve (1962) was optimized for the study of the capacity loss to various water levels. Results showed that the morphology of the reservoir has been changed significantly due to sedimentation from 1962 to 2015. The reservoir has lost a total of 2934.64 hectares (ha) by dint of sedimentation which is accounted for 59.89 % of the total water spread area during last 53 years and average deposition was computed as 1.13% per year. From the year1962 to 2007 maximum siltation deposited in Full Storage Level (FRL) of the reservoir and from 2007 to 2015 in Dead Storage Level (DRL)in the period 2007 to 2015. It can be concluded that multi-dated remote sensing images can be used as a valuable tool for the study of temporal as well as spatial changes in any large water body.

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