Urban agglomeration is a symbol of a country's economic development level, and it is also a sign of a country's economic development to a certain stage. The construction of urban agglomerations in my country continues to increase and will become the core of the global economy. The construction of urban agglomerations in China has gradually led the world into the 21st century. new era of China. However, the rapid development of urbanization has inevitably brought about serious threats to the ecological environment. A healthy ecosystem is an important support for the development of urban agglomerations, especially the sustainable development of large urban agglomerations. Otherwise, the development of urbanization will May be subject to ecological and environmental constraints. In the current process of urbanization, urbanization and ecological environment protection must be well coordinated in order to achieve sustainable regional development. In the process of urbanization, it has inevitably disturbed all aspects of the ecosystem. With the proposal of "new urbanization" at the 18th National Congress of the Communist Party of China, more and more scholars have discussed the level of urbanization and the health of the ecosystem from different perspectives. The interaction between urbanization and ecosystem health is complex. It is necessary to comprehensively and scientifically analyze the spatial relationship between urbanization level and ecosystem health in the Chengdu-Chongqing region, which is conducive to proposing complementary and complementary effects of urbanization subsystems on ecosystem health. improvement measures to achieve sustainable development.
When spatial big data, cloud computing and artificial intelligence collide, the geographic service industry is also undergoing continuous changes and progress. ArcGIS Pro is a professional desktop GIS application that can explore, visualize, analyze and manage 2D and 3D data. R is a language and operating environment for statistical analysis and graphing. R is a free, free, open-source software belonging to the GNU system. It is an excellent tool for statistical computing and statistical graphics. This course explains hyperspectral remote sensing from three aspects: foundation, method and expansion. In the basic chapter, understand the model from the perspective of the students, explain the mechanism of the model, the basic concepts and theories of remote sensing and GIS, and help students understand the data acquisition and preprocessing schemes in depth. In the method section, the ecological model is combined with ArcGIS Pro tools, and the rich spatial analysis functions of ArcGIS Pro are used to quickly extract the parameters required for model operation and provide efficient feedback on the learned theories and methods. In the extended chapter, through technical service solutions such as spatial autocorrelation analysis, geographic detectors, and geographic weighted regression, combined with the R language package, an in-depth analysis of the relationship between urbanization and ecosystem health is conducted.
This tutorial will use case training to teach how to integrate multi-source data, relying on ArcGIS Pro and R language environment, using the "vitality-organization-resilience-contribution" (VORS) model to quantitatively measure the ecosystem health index (EHI); How to construct an urbanization index (UL) measurement model from economic urbanization (GDPD), population urbanization (POPD) and land urbanization (ULP); how to quantitatively measure long-term urbanization levels and ecosystem health, using coupling coordination Model (CCDM) to assess the level of coupling and coordination between urbanization and ecosystem health; how to use geographic and time-weighted regression (GTWR) to measure the interaction and spatio-temporal heterogeneity between urbanization and ecosystem health (UAEH) , how to evaluate the spatial relationship between urbanization and ecosystem health from a multi-dimensional and comprehensive perspective, and how to use geographic detectors to further analyze the impact of environmental variables on ecosystem health.
Through learning, you will learn the basics of GIS and RS, master the skills of spatial data acquisition and processing, and learn the scheme of map symbols and layout design; you can also master the selection and acquisition methods of various index parameters of the VORS model, urbanization index (UL) calculation model construction scheme; you can further master the method of studying the spatial relationship between urbanization and ecosystem health, and be able to analyze the spatial relationship and impact effects of urbanization and ecosystem health from multiple dimensions and perspectives.
Chapter 1 Ecosystem Health Theoretical Basis and Research Hotspot Analysis
1. Ecosystem health concept and connotation
2. Ecosystem health evaluation method and index system
3. Urbanization and ecosystem health
4. Research hotspots and future development directions
Chapter 2 GIS Application
1. ArcGIS software introduction and installation, common function introduction
ArcGIS version introduction,
ArcGIS software interface installation, common function introduction
2. Data types and loading
(1) Introduction to data types and acquisition methods
(2) ArcGIS Pro can use and integrate various dataset types: including spatial data based on elements and rasters (including images and remote sensing data), tabular data, LiDAR etc.
(3) data into ArcGIS Pro
(4) add data from ArcGIS Living Atlas, default geodatabase in project and local folder connection.
(5) Spatial geographic database establishment
(6) Data format conversion
(7) Preview and browse data, check its metadata, clip it to key areas of interest, and process it to ensure format and spatial reference consistency
3. Coordinate system and map projection
(1) geographic coordinate system
(2) projected coordinate system
4. Map symbols and layout design
(1) Create maps based on acquired or created spatial datasets
(2) Symbolize map layers (
3) Label maps
(4) Create charts
(5) Map layout: map layout design
(6) Insert map decoration elements: add text information to the map; use table frames, use graticules, and build spatial map series
(7) Study the production of regional maps
Chapter 3 Spatial Data Acquisition and Preprocessing
1. Data type
2. Data preprocessing
(1) Land use data
Reclassify land use according to requirements
The reclassification, mask extraction and projection transformation of land use data are all completed in ArcGIS.
(2) DEM data
Get the ASTER GDEM digital elevation data with a resolution of 30 m on the PIE ENGINE cloud platform or download it, and export it to the local after preprocessing such as splicing and cutting on the cloud platform.
Or download the ASTER GDEM digital elevation data with a resolution of 30 m on the geospatial data cloud platform, and stitch and crop it in ArcGIS.
The projection transformation and No data value processing were carried out in ArcGIS to obtain 30 m×30 m raster data.
(3) Socio-economic data
Socio-economic data mainly include three aspects of population, society and economy. Various statistical data are mainly obtained from "** Provincial Statistical Yearbook", "China City Statistical Yearbook" and statistics of various cities and districts (counties) Bulletins, etc. are obtained. The data of each indicator is sorted and processed based on the collected raw data.
(4) Remote sensing product data
Normalized Difference Vegetation Index (NDVI), GDP and population data can be used respectively by the Chinese Academy of Sciences Resource and Environmental Science Data Center product "China's annual vegetation index spatial distribution data set" "China's population spatial distribution kilometer grid data "Set" "China's GDP Spatial Distribution Kilometer Grid Dataset", the spatial resolution is 1 km × 1 km, mainly after the projection transformation in ArcGIS, and the corresponding raster data are extracted with the spatial range vector boundary .
Remote sensing data sets such as NDVI\NPP\GDP\POP can also be obtained on the PIE ENGINE cloud platform or downloaded, and then exported to the local after splicing, cutting and other preprocessing on the cloud platform.
3. Standardization of indicators
Since the types, dimensions and trends of the indicators in the evaluation model are different, each indicator has been standardized and normalized
Chapter 4 Model Parameter Extraction
1. Calculation of ecosystem health level
2. Calculation of urbanization level
Chapter 5 Calculation of Spatial Relationship Between Urbanization and Ecosystem Health
1. Spatial correlation analysis
Spatial autocorrelation refers to the statistical correlation between the similarity of a certain attribute value of a geographic object and the difference in spatial location. Spatial autocorrelation analysis includes global spatial autocorrelation and local spatial autocorrelation.
2. Coupling model
(1) Coupling degree model
Coupling refers to the phenomenon that multiple systems or various components within the system interact and influence each other, and promote or constrain each other, so as to unite. The degree of coupling is a measure of the closeness of interaction and interrelationship between systems or elements in a system.
(2) Coupling Coordination Degree Model (CCDM)
Coordination is a benign interrelationship between systems, which reflects the trend of system elements from chaotic to harmonious development. The coupling coordination degree model is used to consider the development coordination between urbanization and ecosystem health.
3. Effects of urbanization on ecosystem health
The geographical detector model is used to study the interpretation strength of urbanization subsystems on the spatial distribution characteristics of ecosystem health in the study area.
4. Effects of urbanization on ecosystem health
The geographically weighted regression model was used to analyze the spatial heterogeneity of the impact of urbanization on ecosystem health from a global perspective.
(1) Introduction to geographic weighted regression (GWR)
(2) Selection of six kernel functions:
(3) Global Model (mean kernel function), Gaussian (Gaussian kernel function), Exponential, Box-car (box-shaped kernel function), Bi-square (quadratic kernel function), Tri-cude (cube sum function)
(4) Determination of bandwidth
(5) Interpretation of regression results
Chapter 6 SCI Paper Writing and Expansion
1. Thesis writing ideas and experience sharing
2. Case analysis of SCI papers
The most comprehensive content related to ecosystem services:
The following original intentions are designed based on the ability to write a thesis, covering many areas of ecosystem services: [If you are just engaged in the research of ecosystem services, it is a must-have skill for you, Join us]
① Construct an ecological security pattern based on ecosystem services integrated with ArcGIS Pro, Python, USLE, and INVEST models
② Ecosystem service tradeoffs and collaborative dynamic analysis based on ArcGIS Pro, R, INVEST and other
technologies SRP model +" multi-technology integration in ecological environment vulnerability evaluation model construction, spatio-temporal pattern evolution analysis and RSEI index ecological quality evaluation and expanded application ④Based on "PLUS model+" ecosystem service
multi -scenario simulation prediction
⑤Based on VORS, CCDM model , GeoDetector, GWR model integration technology in the analysis of the spatial relationship between urbanization and ecosystem health and its impact effects.
⑥ Based on the equivalent factor method, InVEST, SolVES model and other multi-technology integration in the application of the social value assessment of ecosystem service functions and Thesis writing, extended analysis
Based on the "SRP model +" multi-technology integration in the construction of ecological environment vulnerability evaluation model, the analysis of spatio-temporal pattern evolution and the ecological quality evaluation of RSEI index and its expanded application Computing, GIS spatial analysis, and the advantages of R language statistical analysis focus on analyzing the spatio-temporal evolution of ecological environmental vulnerability. https://blog.csdn.net/WangYan2022/article/details/131434569?spm=1001.2014.3001.5502 Based on the latest SolVES model and multi-technology integration [QGIS, PostgreSQL, ARCGIS, MAXENT, R] to realize the social value assessment of ecosystem service functions And expand case analysis_WangYan2022's blog-CSDN blog Combined with relevant application cases, summarize the current research results, research hotspots, advantages and disadvantages of the model, and look forward to its future development trend, in order to better apply the SolVES model to the social value of ecosystem service functions Evaluation provides reference.
https://blog.csdn.net/WangYan2022/article/details/130827409?spm=1001.2014.3001.5502Based on the integration of ArcGIS Pro, R, INVEST and other technologies, the trade-off and collaborative dynamic analysis of ecosystem services_WangYan2022's Blog-CSDN Blog Systematically and comprehensively grasp the knowledge and technical content of spatial data processing, quantitatively explore the relationship between ecosystem service tradeoffs and synergies and social and ecological factors, and serve the coordinated and sustainable development of regional ecosystem protection and social economy.
https://blog.csdn.net/WangYan2022/article/details/130405562?spm=1001.2014.3001.5502Construct an ecological security pattern based on ecosystem services integrated with multiple technologies such as ArcGIS Pro, Python, USLE, and INVEST models Mapping skills; teach you from the topic: master the construction and calculation of the safety evaluation index system; master the role of the catastrophe model in the index integration; master the method of spatio-temporal process analysis and trend warning of ecological safety evaluation. Based on the understanding of the trend and internal relationship of regional ecological changes, this course combines ecological problem diagnosis, ecological function demand assessment and landscape pattern planning to help ensure the functionality and service of the ecosystem.
https://blog.csdn.net/WangYan2022/article/details/130361525?spm=1001.2014.3001.5502 Based on PLUS model + InVEST model Ecosystem service multi-scenario simulation prediction_WangYan2022's blog-CSDN blog covers the acquisition of multi-source data , selection and unification; ArcGIS spatial data processing, spatial analysis and mapping; the principle of PLUS model and InVEST model, parameter extraction and model operation and result analysis; spatial-temporal land use change and impact analysis on ecosystem services https://blog
. csdn.net/WangYan2022/article/details/128974502?spm=1001.2014.3001.5502