|
US$219.00 · In stock
Delivery: <= 3 days. True-PDF full-copy in English will be manually translated and delivered via email.
HJ 1172-2021: (National Ecological Status Survey and Evaluation Technical Specifications-Ecosystem Quality Evaluation)
Status: Valid
| Standard ID | Contents [version] | USD | STEP2 | [PDF] delivered in | Standard Title (Description) | Status | PDF |
| HJ 1172-2021 | English | 219 |
Add to Cart
|
3 days [Need to translate]
|
(National Ecological Status Survey and Evaluation Technical Specifications-Ecosystem Quality Evaluation)
| Valid |
HJ 1172-2021
|
Standard similar to HJ 1172-2021 HJ 1347.1 HJ 1347.2 HJ 1346.1 Basic data | Standard ID | HJ 1172-2021 (HJ1172-2021) | | Description (Translated English) | (National Ecological Status Survey and Evaluation Technical Specifications-Ecosystem Quality Evaluation) | | Sector / Industry | Environmental Protection Industry Standard | | Word Count Estimation | 9,956 | | Issuing agency(ies) | Ministry of Ecology and Environment | HJ 1172-2021: (National Ecological Status Survey and Evaluation Technical Specifications-Ecosystem Quality Evaluation)
---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
(National Ecological Status Survey and Evaluation Technical Specifications-Ecosystem Quality Evaluation)
National Ecological Environment Standard of the People's Republic of China
National Ecological Status Survey and Evaluation Technical Specification
--Ecosystem quality assessment
Technical specification for investigation and assessment of national ecological
status
--Ecosystem quality assessment
This electronic version is the official standard text, which is reviewed and typeset by the Environmental Standards Institute of the Ministry of Ecology and Environment.
Published on 2021-05-12
2021-08-01 Implementation
Released by the Ministry of Ecology and Environment
directory
Foreword...ii
1 Scope...1
2 Normative references...1
3 Terms and Definitions...1
4 General...2
5 Technical process...2
6 Ecosystem quality assessment indicators and methods...2
7 Ecosystem quality classification...3
Appendix A (Informative Appendix) National Ecosystem Classification System Table...4
Appendix B (Normative Appendix) Calculation Methods of Key Ecological Parameters of Remote Sensing...5
National Ecological Status Survey and Evaluation Technical Specification
--Ecosystem quality assessment
1 Scope of application
This standard specifies the general principles, technical procedures, indicators and methods of regional natural ecosystem quality assessment, and ecosystem quality grading requirements.
begging.
This standard is mainly applicable to the quality assessment of vegetation-dominated natural ecosystems in the land areas of the national and provincial administrative regions. Other geographical areas may
Refer to this standard for implementation.
2 Normative references
This standard refers to the following documents or clauses thereof. For dated references, only the dated version applies to this standard.
For undated references, the latest edition (including all amendments) applies to this standard.
HJ 192 Technical Specification for Assessment of Ecological Environment Status
3 Terms and Definitions
The following terms and definitions apply to this standard.
3.1
ecosystem quality
It characterizes the pros and cons of the natural vegetation of the ecosystem, and reflects the overall condition of the vegetation in the ecosystem and the ecosystem.
3.2
assessment unit
According to the needs of the evaluation purpose and evaluation method, the geographic space unit for evaluation is divided.
3.3
leaf area index
The ratio of the total area of plant leaves to the land area per unit land area mainly represents the complexity of the vertical structure of vegetation.
3.4
gross primary productivity
In unit time and unit area, the total amount of organic carbon fixed by green plants through photosynthesis mainly represents the photosynthetic energy of vegetation
strength.
3.5
fractional vegetation cover
The percentage of the vertical projected area of vegetation (including leaves, stems, and branches) on the ground to the total area of the statistical area, which mainly represents the horizontal structure of vegetation
situation.
4 General
Ecosystem quality assessment should follow the principles of normativeness, operability, advancement, and economic and technical feasibility.
5 Technical process
Ecosystem quality assessment technical specification process. Based on remote sensing ecological parameters, carry out ecosystem quality assessment by region and ecosystem type
estimate (Figure 1).
6 Ecosystem quality assessment indicators and methods
Ecosystem quality assessment takes remote sensing ecological parameters (vegetation coverage, leaf area index, total primary productivity) as indicators, three parameters
See Appendix B for the calculation method. The ecosystem quality index is constructed by the method of selecting the reference value according to different types of ecosystems.
For the boundaries of 242 ecological function zones in the National Ecological Function Zoning, see Appendix A for ecosystem types. The specific process is as follows.
The maximum ecological parameters of the four types of vegetation ecosystems of forest, shrub, grassland and farmland in the functional area were used as reference values, and the scores were calculated in turn.
The ratio of the ecosystem parameter value of each vegetation type in the area to its reference value, to obtain the relative density of the ecological parameter in the area, the relative density
The closer to 1, the closer the ecological parameter of the pixel is to the reference value. The specific calculation method is according to formula (1).
According to this method, the relative density is calculated by selecting the reference value for the vegetation coverage, leaf area index, and total primary productivity by sub-category type.
The results are normalized to between 0 and 1, and the normalization method is as follows.
7 Ecosystem quality classification
According to the evaluation results of ecosystem quality, the ecosystem quality is divided into 5 grades, namely excellent, good, medium, low and poor. For details, please refer to HJ 192
For implementation, see Table 1.
Appendix B
(normative appendix)
Calculation method of key ecological parameters of remote sensing
B.1 Leaf Area Index (LAI)
The leaf area index (LAI) reflects the size of the leaf area per unit area in an ecosystem, and is a simulated terrestrial
important parameters of ecosystems, hydrothermal cycles, and biogeochemical cycles. At present, the methods of obtaining leaf area index based on optical data mainly include.
There are two types, one is a statistical method, which is commonly used to establish an empirical or semi-empirical relationship between the leaf area index and the vegetation index; the other is based on radiation
Remote sensing inversion methods for transport models.
(1) Statistical method
The empirical model method is a commonly used statistical method. This method uses the vegetation index to estimate the leaf area index. The general process is to establish the vegetation index and
The empirical relationship between the leaf area index and the observation data are used for fitting, and then the fitted model is used to estimate the leaf area index.
The empirical relationship between the area index and the vegetation index mainly has the following forms.
A, B, C and D - empirical parameters that vary with vegetation type.
(2) Canopy model
Canopy models can generally be divided into four categories. parametric models, geometric-optical models, mixed-media models, and computer simulation models. these models
The model has been widely used in the estimation of canopy morphology and optical characteristics. At present, the estimation of leaf area index based on the canopy model often adopts the inversion optimization algorithm.
method, neural network technology, genetic algorithm, Bayesian network algorithm and look-up table method, etc., according to the evaluation area and the actual conditions.
Choose appropriate models and methods to estimate leaf area index.
B.2 Vegetation Coverage (FVC)
Fractional vegetation cover (FVC) quantifies the density of vegetation and reflects the growth trend of vegetation.
It is an important basic data for describing ecosystems and is widely used in research fields such as hydrology, ecology, climate, and air pollution. remote sensing due to its
Large-scale data acquisition and continuous observation capabilities have become the main technical means for estimating vegetation coverage. Estimation of Vegetation Coverage Based on Remote Sensing
There are mainly the following methods.
(1) Regression (statistical) model method
The regression (statistical) model method is based on a certain band or band combination of remote sensing data or the vegetation index calculated by using remote sensing data.
A regression analysis was carried out on the vegetation index, soil-adjusted vegetation index, etc. and the vegetation coverage, and an empirical estimation model was established. linear regression model
The estimation model of the study area is obtained by linear regression between the vegetation coverage measured on the ground and the band or vegetation index of the remote sensing image; nonlinear
The regression model method mainly obtains a nonlinear regression model by fitting the band or vegetation index of the remote sensing data with the vegetation coverage.
(2) Mixed pixel decomposition method
Each pixel in a remote sensing image is generally composed of multiple components, and each component contributes to the information observed by the sensor.
The pixel decomposition model is used to estimate the vegetation coverage. Hybrid pixel decomposition models mainly include linear models, probability models, geometric optical models,
Stochastic geometric models and fuzzy analysis models, among which the linear decomposition model is the most widely used. The most commonly used linear pixel decomposition method is the pixel
The dichotomous model refers to the assumption that the pixels are composed of vegetation and non-vegetation, and the spectral information is a linear combination of these two components. Calculated
The proportion of pixels covered by vegetation is the vegetation coverage of the pixel, and the calculation method is as follows.
soil veg soilFVC NDVI NDVI NDVI NDVI (B.4)
In the formula. FVC--pixel vegetation coverage;
NDVI--NDVI value of mixed pixels;
NDVIsoil--the NDVI value of a pixel covered by pure bare soil;
NDVIveg--NDVI value of pure vegetation cover pixels.
Due to the influence of soil, vegetation type and other factors, NDVIsoil and NDVIveg are mainly determined by statistical analysis of images.
For example, the maximum and minimum values of NDVI in the image are directly used as the NDVI values of pure vegetation coverage and pure bare soil coverage, respectively.
(3) Machine learning method
With the development of computer technology, machine learning methods have been widely used in the estimation of vegetation coverage, including neural networks, decision trees,
Support Vector Machines, etc. The steps of the machine learning method are generally to determine the training samples, train the model and estimate the vegetation coverage. According to training samples
Depending on the selection, machine learning methods are divided into two categories. remote sensing image classification and radiative transfer models.
The methods based on remote sensing image classification first use high spatial resolution data to classify, distinguish vegetation and non-vegetation, and then classify the classification results.
If aggregated to a low spatial resolution scale, calculate the proportion of vegetation in low spatial resolution pixels as a training sample to train a machine learning model,
The vegetation coverage is then estimated.
The method based on the radiative transfer model first simulates the spectral reflectance value under different parameters from the radiative transfer model, and then
The spectral response function of the sensor resamples the simulated spectral reflectance values, and different parameters and simulated band values are used as training samples for the machine.
Learn the model for training. The key to machine learning methods is the selection of training samples to ensure accuracy and representativeness.
(4) Other methods
In addition to the above-mentioned common vegetation coverage remote sensing estimation methods, there are mainly physical model method, spectral gradient difference method, FCD (forest canopy method)
density) classification method, etc.
For the estimation of vegetation coverage, an appropriate estimation method can be selected according to the characteristics of the assessment area and existing conditions.
B.3 Gross Primary Productivity (GPP)
Gross primary productivity (GPP) refers to the production of green plants through photosynthesis per unit time and unit area.
The total amount of organic carbon fixed by the action. Total terrestrial primary productivity is an important parameter to describe terrestrial ecosystems and provides information about global climate change.
Quantitative description of the carbon cycle under conditions.
At present, the commonly used methods for estimating total primary productivity mainly include continuous observation of flux stations and estimation of terrestrial ecological process models. Flux Station
Continuous observation is to use the eddy correlation method to measure the exchange between the atmosphere and the ecosystem boundary, including carbon, water and other substances, so as to indirectly calculate the ecological environment.
The amount of total primary productivity of the system. Eddy correlation technology enables quantitative and continuous measurement of terrestrial biosphere-atmosphere carbon and water vapor exchange, which is
The most efficient method to explain land-air exchange at the system scale. The terrestrial ecological process model is a combination of terrestrial surface processes, vegetation canopy
models developed from ecosystem process elements such as The GPP estimation model combined with remote sensing data achieves spatial continuity and does not destroy vegetation.
Estimates of total primary productivity of vegetation. The models of total primary productivity estimated by remote sensing are mainly divided into three categories. empirical vegetation index models, vegetation ecological
process model and machine learning model, and can select appropriate models and methods according to the evaluation area and actual conditions to estimate the total primary students
productivity.
Tips & Frequently Asked Questions:Question 1: How long will the true-PDF of HJ 1172-2021_English be delivered?Answer: Upon your order, we will start to translate HJ 1172-2021_English as soon as possible, and keep you informed of the progress. The lead time is typically 1 ~ 3 working days. The lengthier the document the longer the lead time. Question 2: Can I share the purchased PDF of HJ 1172-2021_English with my colleagues?Answer: Yes. The purchased PDF of HJ 1172-2021_English will be deemed to be sold to your employer/organization who actually pays for it, including your colleagues and your employer's intranet. Question 3: Does the price include tax/VAT?Answer: Yes. Our tax invoice, downloaded/delivered in 9 seconds, includes all tax/VAT and complies with 100+ countries' tax regulations (tax exempted in 100+ countries) -- See Avoidance of Double Taxation Agreements (DTAs): List of DTAs signed between Singapore and 100+ countriesQuestion 4: Do you accept my currency other than USD?Answer: Yes. If you need your currency to be printed on the invoice, please write an email to [email protected]. In 2 working-hours, we will create a special link for you to pay in any currencies. Otherwise, follow the normal steps: Add to Cart -- Checkout -- Select your currency to pay.
|