Historical landscape condition study of discontinued river corridor based on satellite image data analysis

2021 ◽  
Author(s):  
Xiaoming Xu
Keyword(s):  
Ecological Restoration ◽  
Landscape Pattern ◽  
Satellite Image ◽  
Morphological Characteristics ◽  
Image Data ◽  
High Definition ◽  
Landscape Type ◽  
Historical Landscape ◽  
Landscape Types ◽  
River Corridor

<p>The historical landscape condition of discontinued river before discontinued flow is one of the core research fields of river ecological restoration and an important historical reference for the ecological restoration of discontinued river corridor. In this paper, the landscape condition of Yongding River, a discontinued river in northern China, is analyzed before its cut-off. Through the early KEYHOLE satellite high-definition image data interpretation analysis, the landscape type map of the river corridor before its cut-off was drawn. The overall winding degree (1.27) and the overall horizontal and vertical structure of the river before its cut-off were determined. In addition, the area proportion of the key landscape types in river corridor, such as channel, mid-channel bar and floodplain, is 12.82%, 8.8% and 16.29% respectively, and the morphological characteristics and distribution of the above key landscape types in each section of the river can be determined by quantitative analysis. On this basis, the landscape pattern index analysis method can be used to analyze and calculate the overall landscape pattern of the river corridor before cut-off. Combined with relevant historical hydrological data, the historical state of the river before its cut-off can be restored to a certain extent. These results are of great support to the channel ecological restoration, floodplain ecological reconstruction and riverbank ecological restoration.</p>

scholarly journals Landscape change patterns at three stages of the construction and operation of the TGP

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruikang Li ◽  
Yangbing Li ◽  
Bo Li ◽  
Dianji Fu
Keyword(s):  
Landscape Pattern ◽  
Landscape Change ◽  
Ecological Security ◽  
Three Gorges ◽  
Temporal Characteristics ◽  
Landscape Type ◽  
The Three Gorges ◽  
Landscape Types ◽  
Landscape Ecological ◽  
Landscape Ecological Security

AbstractAnalyses of landscape change patterns that are based on elevation and slope can not only provide reasonable interpretations of landscape patterns but can also help to reveal evolutionary laws. However, landscape change patterns and their model in different landforms of the typical watershed in the Three Gorges Reservoir Area (TGRA) has not been quantified and assessed effectively. As a complex geographical unit, the ecological environment in the middle reach of the Yangtze River has experienced great changes due to the construction of the Three Gorges Project (TGP) and its associated human activities. Here, based mainly on a digital elevation model (DEM) and remotely sensed images from 1986, 2000, 2010, and 2017 and by using GIS technology, speeds/ trends of landscape change, the index of landscape type change intensity, landscape pattern indices, and landscape ecological security index, the spatial and temporal evolution characteristics of different elevations, slopes, and buffer landscape types were analyzed in typical watersheds, as well as an evolutionary model of the landscape pattern. The results indicated that (1) the landscape types along with the land classification and buffer zone that were influenced by the TGR construction have undergone a phased change, with the period 2000–2010 being the most dramatic period of landscape evolution during the impoundment period; (2) landscape type shifts from human-dominated farmland to nature-driven forestland and shrub-land as elevations, slopes and buffer distances increased. The landscape has shifted from diversity to relative homogeneity; (3) land types and buffer zones played essential roles in the landscape pattern index, which is reflected in the differences in landscape type indices for spatial extension and temporal characteristics. The results of this paper illustrate the spatial–temporal characteristics of various landscape types at three distinct stages in the construction of the TGR. These findings indicate that the landscape ecological security of the watershed is improving year by year. The follow-up development of the TGRA needs to consider the landscape change patterns of different landforms.

scholarly journals Landscape Pattern Evolution Processes of Wetlands and Their Driving Factors in the Xiong’an New Area of China

2021 ◽  
Vol 18 (9) ◽  
pp. 4403
Author(s):  
Miao Yang ◽  
Jiaguo Gong ◽  
Yong Zhao ◽  
Hao Wang ◽  
Cuiping Zhao ◽  
...  
Keyword(s):  
Landscape Pattern ◽  
Socioeconomic Development ◽  
Landscape Heterogeneity ◽  
Principal Component ◽  
Landscape Patterns ◽  
Hydrological Processes ◽  
Large Area ◽  
Landscape Type ◽  
Wetland Landscape ◽  
Landscape Types

Wetland landscape patterns are the result of various ecological and hydrological processes. Based on the land use landscape types from 1980 to 2017, a transfer matrix, landscape pattern analysis index, and principal component analysis were used to analyze the landscape pattern evolution in the Xiong’an New Area of China, which has a large area with a lake and river wetlands. The results showed that the wetland area has changed greatly since 2000 and the beach land has decreased greatly, while the area of the lake and river wetlands has increased slightly. Beach land was the dominant landscape type of the wetland. The dominant degree of the wetland landscape showed a slightly decreasing trend, and the patches tended to be scattered. The shape complexity of the ponds was the lowest, while that of rivers was the highest. The fragmentation degree of the wetland patches increased, the proportion of landscape types tended to be equalized, and the landscape heterogeneity increased. The leading factors of the wetland landscape change can be summarized as socioeconomic, meteorological, and hydrological processes, with a cumulative contribution rate of 85.3%, among which socioeconomic development was the most important factor. The results have important guiding significance for the ecological restoration and management of wetlands in the Xiong’an New Area and other wetland ecosystems with rivers and lakes.

scholarly journals Symmetry perception with spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan K. George ◽  
Cesare Soci ◽  
Mario Miscuglio ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Mirror Symmetry ◽  
Satellite Image ◽  
Image Data ◽  
Coincidence Detection ◽  
Spiking Neural Networks ◽  
Feed Forward Neural Network ◽  
Neuromorphic Computing ◽  
Symmetry Perception ◽  
High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

2018 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Image Data ◽  
Research Area ◽  
Rice Production ◽  
Landsat 8 ◽  
Estimation Model ◽  
Accuracy Test ◽  
Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

scholarly journals Application of the new Morphological Quality Index in the Cordevole river (BL, Italy)

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
E. Rigon ◽  
J. Moretto ◽  
F. Delai ◽  
L. Picco ◽  
D. Ravazzolo ◽  
...  
Keyword(s):  
Quality Index ◽  
Management Strategies ◽  
Morphological Characteristics ◽  
Site Investigation ◽  
Integrated Approach ◽  
Large Wood ◽  
River Types ◽  
River Corridor ◽  
The Eu

The evaluation of the morphological quality of rivers is essential to define the level of alteration and for implementing future management strategies that consider also hazards related to fluvial processes and channel dynamics. This type of evaluation is particularly significant for the Italian rivers, that, as in many other European countries, have a very high level of human pressure. Recently, in Italy, the National Institute for Environmental Protection and Research has promoted a methodology named IDRAIM for hydromorphological analysis of streams that pursues an integrated approach aimed at a harmonized implementation of both the EU Water Framework Directive (WFD, 2000/60/EC), and the EU Floods Directive (2007/60/EC). In this paper we present the application of the Morphological Quality Index (MQI) protocol, which is part of IDRAIM, to determine the assessment of the morphological quality of the Cordevole River. The water network (only collectors greater than thirdorder were considered), has been divided, through GIS software, into 132 river reaches of homogeneous morphological characteristics, according to the first phase of the method. At this stage the semi-automatic calculation of lateral confinement (defined by “degree of confinement” and a “confinement index”) was tried, in order to reduce the implementing time. The application of 28 indicators was made for 42 reaches representing the major river types and human pressures in the site investigation. The results showed that 48% of the analyzed reaches have a very good or good quality status, 38% have a moderate morphological quality, while only 14% have the characteristics of poor or very poor quality. The main causes that lead to a strong alteration of the terms of reference are linked to i) poor connectivity between hillslopes and river corridor, that is very important for the natural supply of sediment and large wood; ii) absence of vegetation in the river corridor, that is functional to a range of geomorphic processes; iii) presence of artificial elements, particularly the bedload interception structures in the catchment, bank protection along the reach, and the removal of sediment, large wood and vegetation.

scholarly journals MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

2018 ◽  
Vol 14 (2) ◽  
pp. 159 ◽  
Author(s):  
Aulia Ilham ◽  
Marza Ihsan Marzuki
Keyword(s):  
Machine Learning ◽  
Satellite Image ◽  
Image Data ◽  
Information Criterion ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
Distribution Type ◽  
Non Linear System ◽  
Image Data Classification

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for  wide data observation. In remote sensing, machine learning method could be  used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.

scholarly journals Pansformers: Transformer-Based Self-Attention Network for Pansharpening

2021 ◽  
Author(s):  
Nithin G R ◽  
Nitish Kumar M ◽  
Venkateswaran Narasimhan ◽  
Rajanikanth Kakani ◽  
Ujjwal Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Performance Metrics ◽  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Learning Models ◽  
Attention Network ◽  
Panchromatic Image ◽  
Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

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