Human activity and ecological environment space-time coupling and coordination relation evaluation method

Abstract

The invention discloses a human activity and ecological environment space-time coupling and coordination relation evaluation method, and belongs to the technical field of ecological environment monitoring and evaluation and computer programs. The method comprises the steps of human activity and ecological environment data extraction, index system construction, human activity and ecological environment coupling coordination relation model construction, and two-dimensional dynamic four-quadrant evaluation framework division and evaluation. According to the method, remote sensing data, geographic information system (GIS) data and social economic statistical data are integrated and processed by utilizing a multi-source data fusion technology, so that multi-dimensional analysis on an ecological environment and human activities is realized.

Description

Technical Field

[0001] The invention belongs to the field of ecological environment monitoring and evaluation and computer program technology, and relates to a method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment. Background Art

[0002] With the development of social economy, the impact and pressure of human activities on the ecological environment are increasing. In addition, 30% of the world's nature reserves are facing huge pressure from human activities. The diversity of human activities and the complexity of internal collaboration have led to different impacts and results. Different types of human activities, such as farming, logging, transportation, mining and tourism, have become the main factors affecting the ecological environment of nature reserves. Human survival and development are closely linked to the ecological environment. When the quality of the ecological environment is good, it is conducive to human survival and development, and vice versa. Scientifically and impartially comprehensively evaluating and analyzing the relationship between human activities and the coupling and coordination of the ecological environment is crucial to the protection of ecosystems. At present, there are many studies on quantifying the impact of human activities on the ecological environment, but there is a problem of incomplete selection of evaluation indicators, and the research is mainly focused on large and medium scales, with fewer fine spatial scales.

[0003] Human activities and the ecological environment have been interacting with each other, providing feedback, balance and adaptation. Studying the coupling and coordination relationship between the two is a focus and challenge. Most previous studies have examined the relationship between specific types of human activities and specific ecological and environmental elements, focusing on analyzing their static distribution patterns, lacking comprehensive consideration of human activities and the ecological environment, and limited research on the dynamic adaptation process between human activities and the ecological environment as a whole, making it difficult to accurately reflect the complex relationship between the two. In addition, the ecological environment is a dynamic process that changes over time. Therefore, this invention is based on remote sensing images, society, economy, environment and other multi-source data, and by extracting and organizing 12 indicators related to human activities and the ecological environment, it constructs a human activity intensity index and an ecological environment status index, and proposes a new method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment, and conducts evaluation and analysis from the perspectives of time and space. Summary of the invention

[0004] In view of the problems in the prior art, the present invention provides a method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment.

[0005] A method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment, comprising the following steps:

[0006] Step 1. Extract human activity and ecological environment data.

[0007] Step 2. Construct indicator system.

[0008] Step 3. Construct a model of the coupling and coordination relationship between human activities and the ecological environment.

[0009] Step 4. Division and evaluation of the two-dimensional dynamic four-quadrant assessment framework.

[0010] The advantages of the present invention are: using multi-source data fusion technology to integrate and process remote sensing data, geographic information system (GIS) data and socio-economic statistics data, to achieve multi-dimensional analysis of the ecological environment and human activities, to fully consider the indicators of various aspects covered by human activities and the ecological environment, to intuitively observe the spatiotemporal changes in the intensity of human activities and the spatiotemporal changes in the ecological environment through quantitative analysis, to evaluate and analyze the coupling coordination relationship between human activities and the ecological environment, to identify areas and time periods with low and uncoordinated coordination, to visualize the results through ArcGIS software, to be able to systematically and comprehensively evaluate the impact of human activities on the ecological environment, to analyze from the spatiotemporal dimensions, and to make the evaluation results more scientific and accurate.

[0011] The present invention comprehensively considers the indicators related to human activities and ecological environment, and clearly displays the spatiotemporal changes of human activities and ecological environment in the study area. It not only deeply analyzes the coupling and coordination relationship between human activities and ecological environment, but also better identifies the areas of coordination and conflict in the study area. The results provide an important reference for local governments to propose targeted ecological restoration and environmental management measures, and also provide a method for other regional studies. By identifying and adjusting uncoordinated areas, it promotes the coordinated development of human activities and ecological environment, which is of great significance to sustainable development. According to the evaluation results, potential problems are found and targeted suggestions and measures for social and economic development and ecological protection are put forward, providing a reference for achieving harmonious coexistence between man and nature. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. As shown in the figure:

[0013] Figure 1 , is a flow chart of the present invention.

[0014] Figure 2 , two Schematic diagram of dynamic four-quadrant evaluation.

[0015] Fig. 3(a) Distribution map of human activity intensity on Hainan Island in 2000.

[0016] Figure 3(b) Distribution map of human activity intensity on Hainan Island in 2010.

[0017] Figure 3(c) Distribution map of human activity intensity on Hainan Island in 2020.

[0018] Figure 3(d) shows the spatiotemporal changes in human activity intensity on Hainan Island from 2000 to 2020.

[0019] Figure 4(a) is the distribution map of the ecological environment status of Hainan Island in 2000.

[0020] Figure 4(b) is the distribution map of the ecological environment status of Hainan Island in 2010.

[0021] Figure 4(c) Distribution map of the ecological environment status of Hainan Island in 2020.

[0022] Figure 4(d) shows the spatiotemporal changes in the ecological environment of Hainan Island from 2000 to 2020.

[0023] Fig. 5(a) shows the coupling coordination relationship between human activities and the ecological environment on Hainan Island in 2000.

[0024] Fig. 5(b) is the diagram of the coupling coordination relationship between human activities and the ecological environment on Hainan Island in 2010.

[0025] Figure 5(c) shows the coupling coordination relationship between human activities and the ecological environment on Hainan Island in 2020.

[0026] Figure 5(d) shows the percentage of the area of ​​the coordinated relationship between human activities and the ecological environment in Hainan Island in 2000, 2010 and 2020.

[0027] Fig. 6(a) Two-dimensional four-quadrant assessment distribution map of human activities and ecological environment in Hainan Island from 2000 to 2010.

[0028] Figure 6(b) is a two-dimensional four-quadrant assessment distribution map of human activities and ecological environment in Hainan Island from 2010 to 2020.

[0029] Figure 6(c) is a two-dimensional four-quadrant assessment distribution map of human activities and ecological environment in Hainan Island from 2000 to 2020.

[0030] Figure 6(d) shows the two-dimensional four-quadrant distribution area of ​​human activities and ecological environment in Hainan Island from 2000 to 2010, 2010 to 2020, and 2000 to 2020.

[0031] Figure 7 , two-dimensional four-quadrant assessment map of human activities and ecological environment in Hainan Tropical Rainforest National Park. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Example 1: Figure 1 , Figure 2 , Figure 3(a), Figure 3(b), Figure 3(c), Figure 3(d), Figure 4(a), Figure 4(b), Figure 4(c), Figure 4(d), Figure 5(a), Figure 5(b), Figure 5(c), Figure 5(d), Figure 6(a), Figure 6(b), Figure 6(c), Figure 6(d) and Figure 7 As shown in the figure, a method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment makes up for the defects of incomplete selection of traditional evaluation indicators for human activities and ecological environment conditions, realizes the comprehensive evaluation of the coupling and coordination relationship between human activities and the ecological environment, comprehensively analyzes the impact of human activities on the ecological environment, and provides a scientific basis for the sustainable development of ecological environment protection and management.

[0034] It includes data extraction of human activities and ecological environment, construction of indicator system, establishment of coupling coordination relationship model between human activities and ecological environment, division and evaluation of two-dimensional dynamic four-quadrant assessment framework. The overall technical process is as follows: Figure 1 shown.

[0035] The steps are as follows:

[0036] Step 1. Extract human activity and ecological environment data.

[0037] (1) Data acquisition and processing.

[0038] Multi-temporal remote sensing image data were obtained through the Geospatial Cloud (http: / / www.gscloud.cn). The images were radiometrically calibrated and geometrically calibrated based on ENVI software to convert the digital quantization value (DN) of the image into the actual radiance value. Then, the FLASSH atmospheric correction model was applied to reduce the impact of atmospheric conditions on the true surface reflectance.

[0039] (2) Human activity indicator data.

[0040] The extracted human activity index data include population density data, GDP data, night light data, land use data, and road network data. Among them, the land use data is based on pre-processed multi-temporal remote sensing image data. First, the training samples are selected, and then the support vector machine classification algorithm provided by ENVI is used to establish a mathematical model based on the characteristics and category information of the samples to classify the land use of the images. Finally, the images are classified into six categories: forest, grassland, farmland, building, water area, and unused land. The acquisition and processing methods of the remaining indicators are shown in the data description, source, and processing methods of the human activity indicators in Table 1.

[0041] Table 1

[0042]

[0043] (3) Ecological and environmental indicator data.

[0044] The extracted ecological environment index data include vegetation coverage data, humidity data, bare soil and building data, biological abundance data, net primary productivity data, soil erosion data and PM2.5 data. Based on the preprocessed multi-temporal remote sensing images, according to the formula in Table 2, the band calculation tool of ArcGIS and ENVI software was used to calculate the vegetation coverage, humidity and bare soil and building data, the raster calculator was used to calculate the biological abundance index, and the resampling tool was used to resample all data to 1 km resolution and normalize them.

[0045] Data description and processing methods are shown in Table 2, which shows the data description, source and processing methods of ecological environment indicators.

[0046] Table 2

[0047]

[0048] Step 2. Construct indicator system.

[0049] (1) Human activity intensity index.

[0050] Five factors, namely population density (PD), gross domestic product (GDP), night lights (NL), land use (LULC), and road network (RN), were selected to construct the human activity intensity index. After removing outliers, projecting, and normalizing the data, the five indicators were converted into grid data with a resolution of 1 km, ranging from 0 to 100. The analytic hierarchy process was used to calculate the weights of PD, LULC, GDP, NL, and RN. The final formula for calculating the human activity intensity is as follows:

[0051]

[0052] Among them, α represents the five indicators, and ω represents the weights corresponding to the five indicators.

[0053] The difference in human activity intensity between 2020 and 2000 is used to construct the dynamic human activity index (HAI), and the formula is as follows:

[0054] ΔHAI=HAI 2020 -HAI 2000

[0055] In the formula, negative and positive values ​​of HAI represent the decrease and increase of human activities from 2020 to 2000, respectively.

[0056] (2) Ecological environment status index.

[0057] Seven factors, namely, fractional vegetation cover (FVC), humidity (WET), bare soil buildings (NDBSI), biological abundance (BRI), net primary productivity (NPP), soil erosion (soil), and PM2.5 (PM), were selected to construct the ecological environment status index. After removing outliers, projecting, and normalizing the data, the seven indicators were converted into grid data with a resolution of 1 km, ranging from 0 to 100. The analytic hierarchy process was then used to calculate the weights of each indicator. The final ecological environment status index calculation formula is as follows:

[0058]

[0059] Among them, β represents 7 indicators, and ω represents the weights corresponding to the 7 indicators.

[0060] Using the difference in ecological environment conditions between 2020 and 2000, a dynamic ecological environment index (EECI) is constructed, and the formula is as follows:

[0061] ΔEECI=EECI 2020 -EECI 2000

[0062] In the formula, negative and positive values ​​of EECI represent the decline and increase of the ecological environment from 2020 to 2000, respectively.

[0063] Step 3. Construct a model of the coupling and coordination relationship between human activities and the ecological environment.

[0064] The established human activity index and ecological environment status index are taken as two systems, and the HAI-EECI coupling coordination model is constructed to quantify the coupling coordination state achieved by the interaction and impact between human activities and the ecological environment. The coupling coordination model formula is as follows:

[0065]

[0066] T=αHAI+βEECI

[0067] In the formula, HAI and EECI represent human activities and ecological environment conditions, respectively. C is the coupling level, which ranges from 0 to 1, indicating the intensity of interaction and influence between the two systems represented by human activities and ecological environment quality. D is the coupling coordination degree, and T is a comprehensive coordination index that can reflect the status of human activities and ecological environment, which can more comprehensively evaluate the development status of the two systems. α and β represent the relative importance of the two subsystems of ecological environment quality and human activities, respectively. It is believed that human activities and ecological environment are equally important, and both values ​​are set to 0.5. Finally, the coupling coordination degree is divided into 4 categories, and the classification criteria are shown in Table 3.

[0068] Table 3 Classification of coupling coordination degree types

[0069]

[0070] Step 4. Division and evaluation of the two-dimensional dynamic four-quadrant assessment framework.

[0071] From the perspective of dynamic evolution, a two-dimensional dynamic assessment framework based on human activities and ecological environment conditions is constructed with the changing human activity intensity index as the horizontal axis and the changing ecological environment status index as the vertical axis. The ecological environment status is divided into four types (HAI up EECI up 、HAI down EECI up 、HAI down EECI down and HAI up EECI down ). According to the increase (positive value) or decrease (negative value) of HAI and EECI from 2000 to 2020, these four dynamic modes represent the coordination stage, nature-friendly stage, destruction stage and conflict stage of the ecological environment status. The schematic diagram of the two-dimensional dynamic four-quadrant assessment is shown in Figure 2 shown.

[0072] Example 2: Figure 1 , Figure 2 , Figure 3(a), Figure 3(b), Figure 3(c), Figure 3(d), Figure 4(a), Figure 4(b), Figure 4(c), Figure 4(d), Figure 5(a), Figure 5(b), Figure 5(c), Figure 5(d), Figure 6(a), Figure 6(b), Figure 6(c), Figure 6(d) and Figure 7 As shown, a method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment, and an example of evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment in Hainan Island.

[0073] Step 1. Data acquisition.

[0074] Based on the downloaded multi-temporal remote sensing image data of Hainan Island, support vector machine was used to classify land use and obtain land use data; vegetation coverage data, humidity data and bare soil and building data were obtained through band calculation tools; biological abundance data were calculated through raster calculator and extracted data; by collating the Hainan Island Statistical Yearbook data and performing kernel density analysis based on ArcGIS software, population density, gross domestic product, net primary productivity and PM2.5 data were obtained; the remaining data were downloaded from the website. Finally, all data were reclassified and normalized.

[0075] The final human activity indicators in 2000, 2010 and 2020 include: population density data, GDP data, night light data, land use data, and road network data; ecological environment status indicators include: vegetation coverage data, humidity data, bare soil building data, biological abundance data, net primary productivity data, soil erosion data and PM2.5 data.

[0076] Based on the extracted human activity indicators of the three years, the weights of population density, land use, GDP, night lights and road network were calculated using the analytic hierarchy process and were 0.28, 0.25, 0.21, 0.14 and 0.12 respectively.

[0077] The calculation formula is as follows:

[0078]

[0079] Among them, α represents the five indicators, ω represents the weights corresponding to the five indicators, HAI is the intensity of human activities, PD is the population density factor, GDP is the gross domestic product, NL is the night light, LULC is the land use, and RN is the road network.

[0080] The ArcGIS software raster calculator tool was used to obtain the spatiotemporal changes in the intensity of human activities on Hainan Island in 2000, 2010, and 2020, as well as the dynamic changes from 2000 to 2020. The results are shown in Figures 3(a), 3(b), 3(c), and 3(d). It can be seen from the figure that the human activity intensity values ​​on Hainan Island in 2000 were mainly concentrated above 60, and in 2010 and 2020, they were mainly concentrated between 0 and 52, indicating a decrease in human activities. However, in general, from 2000 to 2020, the intensity of human activities on Hainan Island showed an overall increasing trend.

[0081] Step 3. Construction of ecological environment status index.

[0082] Based on the extracted ecological environment indicators of the three years, the weights of FVC, WET, NDBSI, BRI, NPP, soil and PM were calculated by analytic hierarchy process, and the weights were 0.17, 0.15, -0.11, 0.19, 0.15, -0.12 and -0.11 respectively. The calculation formula is as follows:

[0083]

[0084] Among them, β represents 7 indicators, ω represents the corresponding weights of the 7 indicators, EECI is the ecological environment status index, FVC is the vegetation cover, WET is the humidity, NDBSI is the bare soil building, BRI is the biological abundance, NPP is the net primary productivity, soil is soil erosion, and PM is fine particulate matter.

[0085] The ArcGIS software raster calculator tool was used to obtain the spatiotemporal changes in the ecological environment of Hainan Island in 2000, 2010, and 2020, as well as the dynamic changes from 2000 to 2020. The results are shown in Figure 4(a), Figure 4(b), Figure 4(c), and Figure 4(d). It can be seen that the overall spatial situation of Hainan Island is relatively stable, and the dynamic changes from 2000 to 2020 show that the ecological environment is gradually improving.

[0086] Step 4. Construct coupling coordination relationship model.

[0087] Based on the established human activity index and ecological environment status index, the coupled coordination model formula was used to calculate and construct the HAI-EECI coupled coordination model for Hainan Island in 2000, 2010 and 2020 using the raster calculator in ArcGIS software to analyze the interactive impact between human activities and the ecological environment. The coupled coordination relationship between the two is shown in Figure 5(a), Figure 5(b), Figure 5(c) and Figure 5(d). As can be seen from the figure, in 2000, the slightly disordered areas of Hainan Island accounted for 91.28% of the total area of ​​the island. By 2010 and 2020, the basically coordinated areas accounted for 91.28% of the total area of ​​the island. From 2000 to 2020, the highly coordinated areas increased from 0.04% to 0.06%, the well-coordinated areas increased from 1.06% to 3.13%, the basically coordinated areas increased from 35.36% to 87.64%, the slightly disordered areas decreased from 62.67% to 8.77%, and the severely disordered areas decreased from 0.87% to 0.4%. Overall, the relationship between human activities and the ecological environment on Hainan Island has gradually become regionally coordinated in the past 20 years.

[0088] Step 5. Two-dimensional four-quadrant dynamic evaluation framework.

[0089] Based on the constructed human activity index and ecological environment status index in 2000, 2010 and 2020, a two-dimensional four-quadrant dynamic assessment framework was used to analyze the dynamic relationship between human activities and the ecological environment in the three periods of 2000-2010, 2010-2020 and 2000-2020. The changes in human activities were used as the horizontal axis and the changes in the ecological environment were used as the vertical axis, which were divided into four quadrants. In the first quadrant, it is a hopeful state of common prosperity between humans and ecosystems. In the second quadrant, with the decrease of HAI and the increase of EECI, it is beneficial to the environment, and the ecosystem is experiencing recovery and development. In the third quadrant, both HAI and EECI show a downward trend, which may be related to the degradation of the ecosystem and socio-economic conditions. The fourth quadrant represents the disharmony between humans and the ecosystem.

[0090] As shown in Figure 6(a), Figure 6(b), Figure 6(c) and Figure 6(d), from 2000 to 2010, the coordination area accounted for 17.6%, mainly concentrated around major transportation hubs such as Dongfang and Wenchang, the area conducive to nature accounted for 43.8%, mainly located around the central cities of Hainan Island, the degradation area accounted for 34.5%, mainly distributed in central cities such as Qiongzhong, and the conflict area accounted for a small proportion. From 2020 to 2020, the coordination area increased to 56.9%, mainly distributed in coastal cities such as Dongfang and Sanya. From 2000 to 2020, the ratio of coordination area to conflict area in Hainan Island was 6:1. The conflict areas were mainly concentrated in Baisha, Danzhou and other places. The development of these areas may be unsustainable. It is crucial to pay close attention to these areas. The area conducive to nature accounted for 16.5% of the total area, mainly distributed in Wenchang, Baoting and other places. In general, the relationship between human activities and ecological environment conditions in Hainan Island shows a coordinated trend.

[0091] Hainan Tropical Rainforest National Park is an important area for the conservation of tropical biodiversity in China. It is crucial to carefully study the relationship between human activities and the ecological environment in the area. Through the two-dimensional four-quadrant evaluation framework, a clear and intuitive result can be obtained, such as Figure 7 As shown in the data, in the Hainan Tropical Rainforest National Park, the intensity of human activities increased by 0.44 from 2000 to 2020, and showed a downward trend from 2010 to 2020. The ecological environment status index experienced a slight decline from 2000 to 2010, but showed an upward trend from 2010 to 2020. The ratio of overall coordination areas to conflict areas is 7:1. The areas where human activities have significantly increased are mainly concentrated on both sides of the highway. The ecological environment conditions in these areas tend to decline, but these areas are almost coordinated. In addition, although the road construction has changed the land cover in some areas, the overall ecological environment quality along the highway has improved after the completion of the railway.

[0092] It can be seen from the example results that the method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment proposed in the present invention has a good effect. It comprehensively considers the indicators related to human activities and the ecological environment, and clearly displays the spatiotemporal changes of human activities and the ecological environment in the study area. It not only deeply analyzes the coupling and coordination relationship between human activities and the ecological environment, but also better identifies the areas of coordination and conflict in the study area. The tool evaluation results can put forward targeted suggestions and measures for social and economic development and ecological protection, providing a reference for achieving harmonious coexistence between man and nature.

[0093] Example 3: Figure 1 , Figure 2 , Figure 3(a), Figure 3(b), Figure 3(c), Figure 3(d), Figure 4(a), Figure 4(b), Figure 4(c), Figure 4(d), Figure 5(a), Figure 5(b), Figure 5(c), Figure 5(d), Figure 6(a), Figure 6(b), Figure 6(c), Figure 6(d) and Figure 7 As shown, a method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment includes the following steps:

[0094] Step 1. Extract human activity and ecological environment data.

[0095] Step 2. Construct indicator system.

[0096] Step 3. Construct a model of the coupling and coordination relationship between human activities and the ecological environment.

[0097] Step 4. Division and evaluation of the two-dimensional dynamic four-quadrant assessment framework.

[0098] The human activity and ecological environment data extraction in step 1 also includes the following steps:

[0099] Step 1.1: Data acquisition and processing:

[0100] The administrative division data of the study area and multi-view remote sensing image data covering the study area for multiple periods were downloaded through the Geospatial Cloud (http: / / www.gscloud.cn). Firstly, the images were radiometrically calibrated and geometrically calibrated based on the ENVI software to convert the digital quantization value (DN) of the image into the actual radiation brightness value. Then, the FLASSH atmospheric correction model was applied to correct it to reduce the influence of atmospheric conditions on the real surface reflectivity. Finally, the images were stitched to obtain the remote sensing image data of the study area.

[0101] Step 1.2: Extract human activity indicator data:

[0102] The extracted human activity indicator data include population density data, GDP data, night light data, land use data, and road network data.

[0103] (1) Land use data

[0104] Based on the preprocessed multi-temporal remote sensing image data, we first select training samples, and then use the support vector machine classification algorithm provided by ENVI to establish a mathematical model according to the characteristics and category information of the samples to classify the land use of the images. Finally, the images are classified into six categories: forest, grassland, farmland, building, water and unused land.

[0105] (6) Population density data

[0106] The 1km×1km multi-period Chinese population density raster data was downloaded from https: / / hub.worldpop.org / . Based on the administrative division data of the study area, the data was clipped using the Toolbox-Data Management Tools-Raster-Raster Processing-Clipping tool in ArcGIS software, and the minimum-maximum normalization method was used to scale the data to the range of 0-100. The population density data of the study area was obtained.

[0107] (7) Gross domestic product data

[0108] Download statistical yearbook data of multiple periods to obtain the GDP values ​​of various regions, link the values ​​to the administrative division data of the study area, use ArcGIS software for hierarchical display, resample to 1km×1km raster data, and use the minimum-maximum normalization method to scale the data to the range of 0-100. Obtain the GDP data of the study area.

[0109] (8) Night light data

[0110] Download 1km×1km multi-period Chinese night light raster data from https: / / www.noaa.gov / , use ArcGIS software to perform image correction, denoising and enhancement on the data, and use Toolbox-Data Management Tools-Raster-Raster Processing-->Crop tool to crop based on the administrative division data of the study area, and use the minimum-maximum normalization method to scale the data to the range of 0-100. The night light data of the study area was obtained.

[0111] (9) Road network data

[0112] The spatial distribution vector data of roads in China in multiple periods were downloaded from the platform of Resources and Environmental Science and Data Center of the Chinese Academy of Sciences. Four types of roads, namely expressways, national highways, provincial highways and railways, were screened out using ArcGIS software. Based on the administrative division data of the study area, toolbox, analysis tools, extraction and clipping tools were used for clipping. Buffer zones of 0.2 km, 0.5 km, 1 km, 1.5 km and 2 km were set for expressways, national highways, provincial highways and railways respectively, and values ​​of 100, 80, 60, 40 and 20 were assigned. Finally, the data were resampled to 1 km × 1 km raster data to obtain the road network data of the study area.

[0113] Step 1.3: Extract ecological environment indicator data:

[0114] The extracted ecological and environmental indicator data include vegetation coverage data, humidity data, bare soil building data, biological abundance data, net primary productivity data, soil erosion data and PM2.5 data.

[0115] (8) Vegetation coverage data

[0116] Based on the preprocessed multi-period remote sensing image data, the vegetation coverage index (NDVI) was first calculated using the (NIR-R) / (NIR+R) formula using the toolbox-band algebra-band operation tool in the ENVI software. soil ) / (NDVI veg -NDVI soil ) formula to calculate vegetation coverage, resample the vegetation coverage data to a resolution of 1km×1km, and finally use the minimum-maximum normalization method to scale the data to a range of 0-100.

[0117] (9) Humidity data

[0118] Based on the preprocessed multi-period remote sensing image data, the toolbox, band algebra, and band operation tools in ENVI software were used to analyze the WET TM =0.0315ρ Red +0.2021ρ Green +0.3012ρ Blue +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2 (TM sensor) or WET OLI =0.1511ρ Red +0.1973ρ Green +0.3283ρ Blue +0.3407ρ NIR -0.7117ρ SWIR1 -0.4599ρ SWIR2The humidity was calculated using the (OLI sensor) formula, the humidity data was resampled to a resolution of 1 km × 1 km, and finally the minimum-maximum normalization method was used to scale the data to a range of 0-100.

[0119] (10) Bare earth building data

[0120] Based on the preprocessed multi-period remote sensing image data, the building index IBI = [2ρ SWIR1 / (ρ SWIR1 +ρ NIR )-ρ NIR / (ρ NIR +ρ Red )-ρ Green / (ρ Green +ρ SWIR1 )] / [2ρ SWIR1 / (ρ SWIR1 +ρ NIR )+ρ NIR / (ρ NIR +ρ Red )+ρ Green / (ρ Green +ρ SWIR1 )], and bare soil index SI = [(ρ SWIR1 +ρ Red )-(ρ Blue +ρ NIR )] / [(ρ SWIR1 +ρ Red )+(ρ Blue +ρ NIR )], and then calculate the bare soil building index NDBSI = (IBI + SI) / 2, resample the bare soil building index to a resolution of 1 km × 1 km, and finally use the minimum-maximum normalization method to scale the data to the range of 0-100.

[0121] (11) Biological abundance data

[0122] Based on the land use data extracted in 1.2, the biological abundance was calculated using the ArcGIS software toolbox-spatial analysis tools-map algebra-raster calculator tool according to the formula 511.264×(0.35×forest+0.21×grassland+0.28×water+0.11×farmland+0.04×building+0.01×unused land) / area. The biological abundance data was resampled to a resolution of 1 km×1 km, and finally the minimum-maximum normalization method was used to scale the data to the range of 0-100.

[0123] (12) Net primary productivity data

[0124] The 1 km × 1 km multi-period Chinese net primary productivity raster data were downloaded from https: / / Ipdaac.usgs.gov / products / mod17a3hgfv061 / . Based on the administrative division data of the study area, the net primary productivity data were resampled to a resolution of 1 km × 1 km using the Toolbox-Data Management Tools-Raster-Raster Processing-->Clip tool. Finally, the minimum-maximum normalization method was used to scale the data to a range of 0-100.

[0125] (13) Soil erosion data

[0126] The multi-period soil erosion raster data of China with a resolution of 1 km × 1 km was downloaded from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. Based on the administrative division data of the study area, the data were clipped using the Toolbox-Analysis Tools-Extraction-Clipping tool, and the minimum-maximum normalization method was used to scale the data to the range of 0-100 to obtain the soil erosion data.

[0127] (14) PM2.5 data

[0128] The multi-period PM2.5 raster data of China with a resolution of 1 km × 1 km was downloaded from the website https: / / sites.wustl.edu / acag / datasets / surface-pm2-5 / #V5.GL.02. Based on the administrative division data of the study area, the data were clipped using the Toolbox-Analysis Tools-Extraction-Clipping tool, and the minimum-maximum normalization method was used to scale the data to the range of 0-100 to obtain the PM2.5 data.

[0129] The construction of the indicator system in step 2 also includes the following steps:

[0130] Step 2.1: Construct human activity intensity index:

[0131] Five factors, namely population density (PD), gross domestic product (GDP), night lights (NL), land use (LULC), and road network (RN), were selected to construct the human activity intensity index. After outliers were removed, projected, and normalized, the five indicators were converted into grid data with a resolution of 1 km, ranging from 0 to 100. The weights of PD, LULC, GDP, NL, and RN were calculated using the hierarchical analysis method. The final calculation formula for human activity intensity is as follows:

[0132]

[0133] Among them, α represents the five indicators, and ω represents the weights corresponding to the five indicators.

[0134] The difference in human activity intensity between 2020 and 2000 is used to construct the dynamic human activity index (HAI), and the formula is as follows:

[0135] ΔHAI=HAI 2020 -HAI 2000

[0136] In the formula, negative and positive values ​​of HAI represent the decrease and increase of human activities from 2020 to 2000, respectively.

[0137] Step 2.2: Construct the Ecological Environment Status Index:

[0138] Seven factors, namely fractional vegetation cover (FVC), humidity (WET), bare soil building (NDBSl), biological abundance (BRI), net primary productivity (NPP), soil erosion (soil) and PM2.5 (PM), were selected to participate in the construction of the ecological environment status index. After outliers were removed, projected and normalized, the seven indicators were converted into grid data with a resolution of 1 km, ranging from 0 to 100. The weight of each indicator was then calculated using the hierarchical analysis method. The final calculation formula for the ecological environment status index is as follows:

[0139]

[0140] Among them, β represents 7 indicators, and ω represents the weights corresponding to the 7 indicators.

[0141] Using the difference in ecological environment conditions between 2020 and 2000, a dynamic ecological environment index (EECI) is constructed, and the formula is as follows:

[0142] ΔEECI=EECI 2020 -EECI 2000

[0143] In the formula, negative and positive values ​​of EECI represent the decline and increase of the ecological environment from 2020 to 2000, respectively.

[0144] The construction of the coupling coordination relationship model between human activities and the ecological environment in step 3 also includes the following steps:

[0145] The established human activity index and ecological environment status index are regarded as two systems. The HAI-EECI coupling coordination model is constructed to quantify the coupling coordination state achieved by the interaction and impact of human activities and ecological environment. The coupling coordination model formula is as follows:

[0146]

[0147] T=αHAI+βEECI

[0148] In the formula, HAI and EECl represent human activities and ecological environment conditions, respectively; C is the coupling level, with a value of 0-1, indicating the intensity of interaction and influence between the two systems represented by human activities and ecological environment quality; D is the coupling coordination degree; T is a comprehensive coordination index that can reflect the status of human activities and ecological environment, and can more comprehensively evaluate the development status of the two systems; α and β represent the relative importance of the two subsystems of ecological environment quality and human activities, respectively. It is believed that human activities and ecological environment are equally important, and both values ​​are set to 0.5. Finally, the coupling coordination degree types are divided into four categories: when the coupling coordination degree is greater than 0 and less than 0.2, it is an extremely disordered type; when it is greater than 0.2 and less than 0.4, it is a slightly disordered type; when it is greater than 0.4 and less than 0.6, it is a basic coordination type; when it is greater than 0.6 and less than 0.8, it is a good coordination type; when it is greater than 0.8 and less than 1, it is an extremely coordinated type.

[0149] The two-dimensional dynamic four-quadrant assessment framework division and evaluation in step 4 also includes the following steps:

[0150] From the perspective of dynamic evolution, a two-dimensional dynamic assessment framework based on human activities and ecological environment conditions is constructed with the human activity intensity index of two years as the horizontal axis and the ecological environment status index as the vertical axis. The ecological environment status is divided into four types (HAI up EECI up 、HAI down EECI up 、HAI down EECI down and HAI up EECI down ), according to the increase (positive value) or decrease (negative value) of HAI and EECI in the two years, the first and second quadrants are considered sustainable development, and the third and fourth quadrants are considered unsustainable. First, in the first quadrant, both the human activity intensity index and the ecological environment status index increase, which is a hopeful state of common prosperity between man and ecosystem, that is, the evaluation result is the coordination stage; in the second quadrant, the human activity intensity index decreases, and the ecological environment status index increases, which is beneficial to nature, and the ecosystem is restored and developed, that is, the evaluation result is favorable to nature stage; in the third quadrant, both the human activity intensity index and the ecological environment status index tend to decrease, which is related to the degradation of the ecosystem and social economy, that is, the evaluation result is the destruction stage; the fourth quadrant is a state of conflict between man and ecosystem, that is, the evaluation result is the conflict stage.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment, characterized in that: Contains the following steps: Step 1. Extract human activity and ecological environment data. Step 2. Construction of indicator system, Step 3. Construct a model of the coupling coordination relationship between human activities and the ecological environment. Step 4. Division and evaluation of the two-dimensional dynamic four-quadrant assessment framework.

2. The method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment according to claim 1 is characterized in that: The human activity and ecological environment data extraction in step 1 also includes the following steps: Step 1.1: Data acquisition and processing: The administrative division data of the study area and multi-view remote sensing image data covering the study area in multiple periods were downloaded through the Geospatial Cloud (http: / / www.gscloud.cn). First, the images were radiometrically calibrated and geometrically calibrated based on the ENVI software to convert the digital quantization value (DN) of the image into the actual radiation brightness value. Then, the FLASSH atmospheric correction model was used for correction to reduce the impact of atmospheric conditions on the real surface reflectivity. Finally, the images were stitched to obtain the remote sensing image data of the study area. Step 1.2: Extract human activity indicator data: The extracted human activity indicator data include population density data, GDP data, night light data, land use data, road network data, (1) Land use data Based on the pre-processed multi-temporal remote sensing image data, we first selected training samples, and then used the support vector machine classification algorithm provided by ENVI to establish a mathematical model based on the characteristics and category information of the samples to classify the land use of the images. Finally, the images were classified into six categories: forest, grassland, farmland, building, water area and unused land. (2) Population density data The 1km×1km multi-period population density raster data of China was downloaded from https: / / hub.worIdpop.org / . Based on the administrative division data of the study area, the data was clipped using the toolbox, data management tools, raster, raster processing and clipping tools in ArcGIS software. The minimum and maximum normalization methods were used to scale the data to the range of 0-100 to obtain the population density data of the study area. (3) GDP data Download statistical yearbook data from multiple periods to obtain the GDP values ​​of various regions, link the values ​​to the administrative division data of the study area, use ArcGIS software for hierarchical display, resample to 1km×1km raster data, use the minimum and maximum normalization methods to scale the data to the range of 0-100, and obtain the GDP data of the study area. (4) Night light data The 1km×1km multi-period Chinese night light raster data was downloaded from https: / / www.noaa.gov / . The data was rectified, denoised and enhanced using ArcGIS software. Based on the administrative division data of the study area, the data was clipped using toolbox, data management tools, raster, raster processing and clipping tools. The minimum and maximum normalization methods were used to scale the data to a range of 0-100 to obtain the night light data of the study area. (5) Road network data The spatial distribution vector data of roads in China in multiple periods were downloaded from the platform of Resource and Environmental Science and Data Center of Chinese Academy of Sciences. Four types of roads, namely expressways, national roads, provincial roads and railways, were selected using ArcGIS software. Based on the administrative division data of the study area, toolbox, analysis tools, extraction and clipping tools were used for clipping. Buffers of 0.2 km, 0.5 km, 1 km, 1.5 km and 2 km were made for expressways, national roads, provincial roads and railways respectively, and the buffers were assigned values ​​of 100, 80, 60, 40 and 20. Finally, the data were resampled to 1 km × 1 km grid data to obtain the road network data of the study area. Step 1.3: Extract ecological environment indicator data: The extracted ecological environment indicator data include vegetation coverage data, humidity data, bare soil building data, biological abundance data, net primary productivity data, soil erosion data and PM2.5 data. (1) Vegetation coverage data Based on the preprocessed multi-period remote sensing image data, the vegetation coverage index (NDVI) was first calculated using the (NIR-R) / (NIR+R) formula using the toolbox, band algebra and band operation tools in the ENVI software. soil ) / (NDVI veg -NDVI soil ) formula to calculate vegetation coverage, resample the vegetation coverage data to a resolution of 1km×1km, and finally use the minimum and maximum normalization methods to scale the data to a range of 0-100. (2) Humidity data Based on the preprocessed multi-period remote sensing image data, the toolbox, band algebra, and band operation tools in ENVI software were used to analyze the WET TM =0.0315ρ Red +0.2021ρ Green +0.3012ρ Blue +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2 (TM sensor) or WET OLI =0.1511ρ Red +0.1973ρ Green +0.3283ρ Blue +0.3407ρ NIR -0.7117ρ SWIR1 -0.4599ρ SWIR2 The humidity is calculated using the (OLI sensor) formula, the humidity data is resampled to a resolution of 1km×1km, and finally the minimum and maximum normalization methods are used to scale the data to a range of 0-100. (3) Bare earth building data Based on the preprocessed multi-period remote sensing image data, the building index IBI = [2ρ SWIR1 / (ρ SWIR1 +ρ NIR )-ρ NIR / (ρ NIR +ρ Red )-ρ Green / (ρ Green +ρ SWIR1 )] / [2′ SWIR1 / (ρ SWIR1 +ρ NIR )+ρ NIR / (ρ NIR +ρ Red )+ρ Green / (ρ Green +ρ SWIR1 )], and bare soil index SI = [(ρ SWIR1 +ρ Red )-(ρ Blue +ρ NIR )] / [(′ SWIR1 +ρ Red )+(′ Blue +ρ NIR )], and then calculate the bare soil building index NDBSI = (IBI + SI) / 2, resample the bare soil building index to a resolution of 1km × 1km, and finally use the minimum-maximum normalization method to scale the data to a range of 0-100. (4) Biological abundance data Based on the land use data extracted in 1.2, the toolbox, spatial analysis tools, map algebra and raster calculator tools in ArcGIS software were used to calculate the biological abundance according to the formula of 511.264×(0.35×forest+0.21×grassland+0.28×water+0.11×farmland+0.04×building+0.01×unused land) / area. The biological abundance data was resampled to a resolution of 1km×1km, and finally the minimum and maximum normalization methods were used to scale the data to a range of 0-100. (5) Net primary productivity data The 1km×1km multi-period Chinese net primary productivity raster data were downloaded from https: / / Ipdaac.usgs.gov / products / mod17a3hgfv061 / . Based on the administrative division data of the study area, the net primary productivity data were resampled to a resolution of 1km×1km using the toolbox, data management tools, raster, raster processing and clipping tools. Finally, the minimum and maximum normalization methods were used to scale the data to a range of 0-100. (6) Soil erosion data The multi-period soil erosion raster data of China with a resolution of 1 km × 1 km was downloaded from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. Based on the administrative division data of the study area, the data were clipped using the toolbox, analysis tools, extraction and clipping tools. The minimum and maximum normalization methods were used to scale the data to a range of 0-100 to obtain soil erosion data. (7) PM2.5 data The multi-period PM2.5 raster data of China with a resolution of 1 km × 1 km was downloaded from the website https: / / sites.wustl.edu / acag / datasets / surface-pm2-5 / #V5.GL.

02. Based on the administrative division data of the study area, the data were clipped using the toolbox, analysis tools, extraction and clipping tools. The minimum and maximum normalization methods were used to scale the data to the range of 0-100 to obtain the PM2.5 data.

3. The method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment according to claim 1 is characterized in that: The construction of the indicator system in step 2 also includes the following steps: Step 2.1: Construct human activity intensity index: Five factors, namely population density (PD), gross domestic product (GDP), night lights (NL), land use (LULC), and road network (RN), were selected to construct the human activity intensity index. After outliers were removed, projected, and normalized, the five indicators were converted into grid data with a resolution of 1 km, ranging from 0 to 100. The weights of PD, LULC, GDP, NL, and RN were calculated using the hierarchical analysis method. The final calculation formula for human activity intensity is as follows: Among them, a represents the five indicators, ω represents the weights corresponding to the five indicators, The difference in human activity intensity between 2020 and 2000 is used to construct the dynamic human activity index (HAI), and the formula is as follows: ΔHAI=HAI 2020 -HAI 2000 In the formula, negative and positive values ​​of HAI represent the decrease and increase of human activities from 2020 to 2000, respectively. Step 2.2: Construct the Ecological Environment Status Index: Seven factors, namely, fractional vegetation cover (FVC), humidity (WET), bare soil buildings (NDBSI), biological abundance (BRI), net primary productivity (NPP), soil erosion (soil) and PM2.5 (PM), were selected to construct the ecological environment status index. After outliers were removed, projected and normalized, the seven indicators were converted into grid data with a resolution of 1 km, ranging from 0 to 100. The weight of each indicator was then calculated using the hierarchical analysis method. The final calculation formula for the ecological environment status index is as follows: Among them, β represents 7 indicators, and ω represents the weights corresponding to the 7 indicators. Using the difference in ecological environment conditions between 2020 and 2000, a dynamic ecological environment index (EECI) is constructed, and the formula is as follows: ΔEECI=EECI 2020 -EECI 2000 In the formula, negative and positive values ​​of EECI represent the decline and increase of the ecological environment from 2020 to 2000, respectively.

4. The method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment according to claim 1 is characterized in that: The construction of the coupling coordination relationship model between human activities and the ecological environment in step 3 also includes the following steps: The established human activity index and ecological environment status index are regarded as two systems. The HAI-EECI coupling coordination model is constructed to quantify the coupling coordination state achieved by the interaction and impact of human activities and ecological environment. The coupling coordination model formula is as follows: T=αHAI+βEECI In the formula, HAI and EECI represent human activities and ecological environment conditions, respectively; C is the coupling level, with a value of 0-1, indicating the intensity of interaction and influence between the two systems represented by human activities and ecological environment quality; D is the coupling coordination degree; T is a comprehensive coordination index that can reflect the status of human activities and ecological environment, and can more comprehensively evaluate the development status of the two systems; α and β represent the relative importance of the two subsystems of ecological environment quality and human activities, respectively. It is believed that human activities and ecological environment are equally important, and both values ​​are set to 0.

5. Finally, the coupling coordination degree type is divided into four categories: when the coupling coordination degree is greater than 0 and less than 0.2, it is an extremely disordered type; when it is greater than 0.2 and less than 0.4, it is a slightly disordered type; when it is greater than 0.4 and less than 0.6, it is a basic coordination type; when it is greater than 0.6 and less than 0.8, it is a good coordination type; when it is greater than 0.8 and less than 1, it is an extremely coordinated type.

5. The method for evaluating the spatiotemporal coupling and coordination relationship between human activities and the ecological environment according to claim 1 is characterized in that: The two-dimensional dynamic four-quadrant assessment framework division and evaluation in step 4 also includes the following steps: From the perspective of dynamic evolution, a two-dimensional dynamic assessment framework based on human activities and ecological environment conditions is constructed with the human activity intensity index of two years as the horizontal axis and the ecological environment status index as the vertical axis. The ecological environment status is divided into four types (HAI up EECI up 、HAI down EECI up 、HAI down EECI down and HAI up EECI down ), according to the increase (positive value) or decrease (negative value) of HAI and EECI in the two years, the first and second quadrants are considered sustainable development, and the third and fourth quadrants are considered unsustainable. First, in the first quadrant, both the human activity intensity index and the ecological environment status index increase, which is a hopeful state of common prosperity between people and ecosystems, that is, the evaluation result is the coordination stage; in the second quadrant, the human activity intensity index decreases, and the ecological environment status index increases, which is beneficial to nature, and the ecosystem is restored and developed, that is, the evaluation result is the favorable nature stage; in the third quadrant, both the human activity intensity index and the ecological environment status index tend to decrease, which is related to the degradation of the ecosystem and social economy, that is, the evaluation result is the destruction stage; The fourth quadrant is the state of conflict between humans and the ecosystem, that is, the assessment result is the conflict stage.

Images