Since the Industrial Revolution, social productivity has increased rapidly and human activities have become frequent. In addition, the growing population has intensified the demand for and transformation of land, and the relationship between man and land has become increasingly tense. In addition, the irrational development and utilization of land resources has caused a series of ecological and environmental problems such as soil erosion, vegetation degradation, water shortages, regional climate change, and sharp decline in biodiversity. How to optimize the land use model, maintain regional land ecological security, ease the contradiction between land supply and demand, and make the human-land relationship harmonious and symbiotic has become a key issue and has become a hot research topic at home and abroad.
https://mp.weixin.qq.com/s?__biz=Mzg2NDYxNjMyNA==&mid=2247539969&idx=4&sn=a2a78d033b6cc0d23adf93534bb902d6&chksm=ce64b7eaf9133efc7e6d33c333b2af63a0f 4b781a6be571b521404ec1b9e2223233161b4362f &token=361297460&lang=zh_CN#rdEcosystem services are the benefits that humans obtain directly or indirectly from ecosystems and play a vital role in addressing urban challenges and implementing sustainable development. With the rapid development of global urbanization, frequent human activities have led to rapid changes in land use, resulting in changes in ecosystem structure and function, affecting the supply of ecosystem services. Therefore, the integration of ecosystem services assessment and future urban land planning has become an important research topic in recent years.
Scenario analysis methods are currently one of the most mature methods for studying future ecosystem service trade-offs and synergies. By establishing different land use scenarios to analyze the changes between ecosystem services and the role of internal mutual responses, decision-making recommendations can be made for future land use planning scenarios. The PLUS model has two major modules, one is a rule mining framework based on land expansion analysis strategy, and the other is a CA model based on multi-type random patch seeds. In addition, the model also has a built-in Markov chain to facilitate prediction of land use quantity needs. . The PLUS model can accurately simulate the nonlinear relationship changes behind land use with a patch-level land use simulation model, achieving a more accurate impact of land use on potential ecosystem service functions under different future policy scenarios.
As future land scenario succession intensifies, it is necessary to carry out research that accurately simulates future land use development potential, multiple scenario planning that is consistent with policy guidelines, and reasonably and accurately simulates various functions and trade-offs of ecosystem services. The urgent need for sustained ecosystem services trade-off development concepts. The application of geospatial analysis technology will ensure the realization of this goal, and the use of PLUS models will help decision makers evaluate and plan land use policies in advance by setting development driving parameters under required scenario conditions. The InVEST model has been widely used to assess ecosystem services.
Multi-scenario forecasting of ecosystem services is explained from three aspects: data, methods and practice. The content covers the acquisition, selection and unification of multi-source data; ArcGIS spatial data processing, spatial analysis and mapping; the principles of the PLUS model and the InVEST model, parameter extraction, model operation and result analysis; spatiotemporal changes in land use and their impact on ecosystem services analyze;
You will be able to learn to: 1) predict future land use under multi-scenario models based on historical land use data; 2) use the InVEST model to quantify and evaluate ecosystem service functions; 3) predict and analyze spatiotemporal changes in spatial data; 4 ) Attribution analysis of spatial heterogeneity of ecosystem services. In specific practical cases, you will learn to apply the above principles and technical methods to improve the application capabilities of spatial information technology.
Chapter 1, theoretical basis and software explanation
1. Concept definition and theoretical basis
lLand use
lMultiple scenario simulation
lEcosystem services
2. Introduction to geographical data
lGeodatabase :
File geodatabase : A collection of GIS datasets of many types saved in a file system folder;
Personal geodatabase : The raw data format for ArcGIS geodatabases stored and managed in Microsoft Access data files
lRaster data: consists of a matrix of cells (or pixels) organized by rows and columns (or grid), where each cell contains an information value. The raster can be a digital aerial photograph, a satellite image, a digital picture or even a scanned map.
l Vector data: A non-topological simple format that stores the geometric location and attribute information of geographic features. Geographic features are represented by points, lines, or areas (regions).
3. Introduction and practice of ArcGIS spatial data processing and analysis
lArcGIS platform introduction
lArcGIS commonly used coordinate systems
lArcGIS spatial data processing and conversion
lArcGIS spatial analysis
lArcGIS mapping skills
4. Introduction and installation of PLUS model and InVEST model
lPLUS version introduction and installation;
lPLUS software interface, introduction to common functions;
lInVEST version introduction, installation;
lInVEST software interface, introduction to common functions;
Pitfalls that have been encountered in the past—common mistakes and usage precautions; path problems, etc.
Chapter 2, Data Acquisition and Preparation
1. Land use data
l Introduction to land use data sets and how to obtain them
lLand use data set selection
lLand use data preprocessing: image splicing, cropping, reprojection, etc.
2. Driving factor data
lClimate and environmental data
lSocioeconomic data
3. Different types of data preparation methods and practices
lRaster data processing:
Processing such as raster image splicing, cropping, reprojection and resampling;
lBasic geographic information data processing and spatial analysis:
üIntroduction and analysis of Euclidean distance algorithm
üIntroduction and analysis of density analysis algorithm
lTopography factor extraction
Principles and methods for extracting terrain factors such as slope, aspect, terrain relief, and mountain shadows
lSoil factor data extraction
üEditing and exporting attribute tables
üAttributes of the connection table
üReclassification: Various ways to reclassify input cell values or change input cell values to alternative values
üLookup table: Create a new raster by looking up the value of another field in the input raster data table
lMeteorological factor data processing:
üSite data download and extraction
Interpolation analysis: inverse distance weighting (IDW), automatic
Interpolation analysis of meteorological station data using random neighborhood method, trend surface method and spline function method;
üNetCDF data processing: Create raster layers based on NetCDF files
lConversion method of raster data
Chapter 3. Land use pattern simulation
1. Principle of PLUS model
lRule mining framework based on land expansion analysis strategy
lCA model based on multi-type random patch seeds
2. PLUS model construction and accuracy verification
lLand use expansion analysis
lSimulation parameter settings
(1) Restricted area
(2) Field effect
(3) Conversion cost
(4) Domain weight
(5) Land use needs
Use Markov model to predict completion.
In the formula: S t and S t+ 1 are the land use in periods t and t +1, P ij is the transition probability matrix, and n is the land use type.
lModel accuracy verification
overall accuracy
Kappa coefficient
3. Simulation of land use pattern in Hengduan Mountains under different scenarios
lLand use simulation under natural development scenario
lLand use simulation under ecological protection scenario
lLand use simulation under economic development priority scenario
Chapter 4. Ecosystem Service Assessment
1. InVEST model principles and modules
2. Water production services
lData requirements and preparation:
3. Soil conservation
lData requirements and preparation:
4. Carbon storage
lData requirements and preparation:
5. Habitat quality
lData requirements and preparation:
Chapter 5, Analysis of Spatiotemporal Changes and Driving Mechanisms
1. Analysis of spatial and temporal changes in land use
lAnalysis of changes in land use structure
lAnalysis of land use dynamics
lLand use transfer matrix analysis
lLand use standard deviation ellipse analysis
2. Spatial Autocorrelation (Global Moran's I) (Spatial Statistics) analysis principles and practices
3. Working principle and practice of high/low clustering (Getis-Ord General G) analysis
lUse the Getis-Ord General G statistic to measure the degree of clustering of high or low values.
4. Spatial stratified heterogeneity analysis
lPrinciple of geographical detector
lInstallation and introduction of geographical detector module
l factor detection
lInteractive detection
5. Local regression analysis
lIntroduction to geographically weighted regression model
lBasic guidelines for model establishment
(1) Determination of spatial weight coefficient
(2) Bandwidth selection criteria
lParameter and evaluation index analysis
lSpatial pattern analysis of regression coefficients
Chapter Six, Paper Writing Skills and Case Analysis
1. Structure of scientific papers
Introduce the writing points of abstract, introduction, methods, results, discussion and conclusion
2. Standards for figures and tables in scientific papers
3. Analysis of paper submission skills
4. SCI paper case analysis
5. Model application can be expanded in directions