A method for assessing regional climate change ecosystem service loss

Abstract

This invention discloses a method for assessing ecosystem service losses due to regional climate change. The region includes counties, cities, or important ecological function zones. The steps are as follows: S1. Collect remote sensing data and preprocess the data through band combination, geometric correction, and projection transformation. Select a standard color composite scheme, establish interpretation markers, and perform visual interpretation. S2. Calculate and statistically analyze the unit area yield value and equivalent factor of grain crops. The equivalent factor is 1 / 7 of the average market value of grain in the current year. S3. Statistically analyze land use types, calculate the magnitude of land use change, reflecting the changes in the total amount of different land use types and the overall trend of land use type changes. S4. Select vegetation cover as an indicator, and adjust the ecosystem service value based on two parameters: net primary productivity and vegetation cover. This invention is of great significance for formulating ecosystem management strategies under the background of future climate change.

Description

Technical Field

[0001] This invention relates to the field of ecological protection, restoration and assessment technology, and more specifically to a method for assessing regional climate change service losses. Background Technology

[0002] Ecosystem services refer to ecosystem products and functions that contribute to human survival and quality of life. All benefits humans derive from ecosystems include provisioning services, regulating services, cultural services, and support services (such as nutrient cycling to sustain life on Earth).

[0003] With the rapid development of human society, a series of problems have emerged, such as the rapid increase in population, excessive consumption of resources, and severe degradation of various ecosystems due to environmental pollution. The ecosystem's ability to provide services to humans is also declining, while human demand for economic development is causing its utilization of ecosystem services to grow at an unsustainable rate, threatening the sustainable development of human society. Against this backdrop, in order to achieve and maintain harmony between human society and natural ecosystems, scholars from various countries have begun to study the relationship between themselves and ecosystems from different perspectives. Ecosystem service assessment has gradually become one of the hot topics in ecology and ecological economics research.

[0004] Against the backdrop of global warming, the losses and damages caused by climate change have received significant attention, particularly as a focal point of heated debate between developed and developing countries. The impacts of climate change on my country's agriculture, water resources, ecology, and human health are becoming increasingly prominent. Therefore, understanding the response of ecosystems to climate change is crucial for deepening our understanding of global change and its impacts, developing scientific countermeasures to minimize its adverse effects, and ensuring that ecosystems evolve in a direction conducive to human survival and sustainable development. Summary of the Invention

[0005] In view of this, the present invention provides a method for assessing the loss of ecosystem services due to regional climate change.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the loss of ecosystem services due to regional climate change, comprising the following steps: S1, collecting remote sensing data, performing preprocessing such as band combination, geometric correction and projection conversion on the remote sensing data, selecting a standard color composite scheme, establishing interpretation markers, and performing visual interpretation;

[0007] S2. Calculate and statistically analyze the output value per unit area of ​​grain crops and the equivalent factor. The equivalent factor is 1 / 7 of the average market value of grain in the current year.

[0008] S3. Statistical analysis of land use types interpreted from remote sensing images is conducted to calculate the magnitude of land use change, reflecting the total changes in different land use types in the study area and the overall trend of change for each land use type. The formula is as follows:

[0009] W i =(L b -L a ) / L a ×100%

[0010] W i Land use change rate (%); L a L b The areas of a certain land type are respectively at the beginning and end of the study period;

[0011] S4. Select vegetation cover as an indicator. Based on the selected vegetation cover indicator, and using the two parameters of net primary productivity and vegetation cover, adjust the ecosystem service value using the following adjustment formula:

[0012]

[0013] In the formula, NPP mean and f mean These represent the mean net primary productivity (NPP) and vegetation cover of the ecosystem within the region, respectively. j and f j Net primary productivity (NPP) and vegetation cover (f) for pixel j v );

[0014] The calculation of NPP is based on the CASA light energy utilization model. Its estimation formula is as follows:

[0015] NPP(x,t) = APAR(x,t) × ε(x,t)

[0016] In the formula, APAR(x,t) represents the photosynthetically active radiation (g·C·m) absorbed by pixel x in month t. -2 ·month -1 ), ε(x,t) represents the actual light energy utilization rate (g·C / MJ) of pixel x in month t; APAR estimation: The value of APAR is determined by the effective solar radiation that the vegetation can absorb and the proportion of incident photosynthetically active radiation absorbed by the vegetation; Light energy utilization rate ε estimation: Light energy utilization rate is the ratio of the chemical potential energy contained in the dry matter produced per unit area in a certain period to the photosynthetically active radiation energy projected onto that area in the same period.

[0017] Vegetation coverage (f) v The calculation formula for ) is as follows:

[0018]

[0019] In the formula, the Normalized Difference Vegetation Index (NDVI) data uses 250m spatial resolution MODIS NDVI MOD13Q1 data provided by the U.S. Geological Survey, with a temporal resolution of 16 days. After data processing such as projection coordinate system transformation and spatial resampling, 30m spatial resolution data consistent with land use is obtained.

[0020] Preferably, in the above-mentioned method for assessing ecosystem service losses due to regional climate change, in S1, land use types are classified into cultivated land, forest land, grassland, urban and rural construction land, water area and unused land according to the Chinese remote sensing interpretation classification standard.

[0021] Preferably, in the above-mentioned method for assessing ecosystem service loss due to regional climate change, S2 defines 1 hm 2 The economic value of natural grain produced per unit area of ​​farmland per year is 1, calculated according to the formula based on the yield per unit area of ​​grain crops, the sown area, and the national average price of each grain crop.

[0022]

[0023] In the formula, E a 1hm 2 The annual economic value of grain crops in farmland (yuan / hm) 2 ); i represents the crop type, m i Let p be the national average price (yuan / t) of grain crop i; i Yield per unit area (t / hm) of i type of grain crop 2 );q i Let i be the area of ​​a certain type of grain crop (hm). 2 M represents the total area of ​​type i grain crops (hm²). 2 );

[0024] The formula for calculating the value of ecosystem services is:

[0025] ESV=∑A i ×VC i

[0026] ESV j =∑A i ×VC ij

[0027] In the formula, ESV represents the total ecosystem service value of the study area; ESV j The value of the j-th ecosystem service; A i VC represents the distribution area of ​​land use type i in the study area. iThe value of ecosystem services per unit area for land use type i; i is divided into five types: woodland, grassland, farmland, field, and water. Land use data are from Landsat TM / ETM and HJ CCD remote sensing data from 2010 (growing season and non-growing season), with a spatial resolution of 30m.

[0028] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for assessing the loss of ecosystem services due to regional climate change. Due to the complexity of the impact of climate change and the uncertainty in the value of ecosystem services, the present invention provides a research direction to understand the response to climate change, which helps to deepen the understanding of global change and its impact, formulate scientific countermeasures to minimize the adverse effects of climate change, and prevent and plan in advance. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the ecological functional zoning of the Altai Mountains. Detailed Implementation

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0032] A method for assessing the loss of ecosystem services due to regional climate change includes the following steps: S1. Collecting remote sensing data, performing preprocessing such as band combination, geometric correction and projection transformation on the remote sensing data, selecting a standard color composite scheme, establishing interpretation markers, and performing visual interpretation;

[0033] S2. Calculate and statistically analyze the output value per unit area of ​​grain crops and the equivalent factor. The equivalent factor is 1 / 7 of the average market value of grain in the current year.

[0034] S3. Statistical analysis of land use types interpreted from remote sensing images is conducted to calculate the magnitude of land use change, reflecting the total changes in different land use types in the study area and the overall trend of change for each land use type. The formula is as follows:

[0035] Wi =(L b -L a ) / L a ×100%

[0036] W i Land use change rate (%); L a L b The areas of a certain land type are respectively at the beginning and end of the study period;

[0037] S4. Select vegetation cover as an indicator. Based on the selected vegetation cover indicator, and using the two parameters of net primary productivity and vegetation cover, adjust the ecosystem service value using the following adjustment formula:

[0038]

[0039] In the formula, NPP mean and f mean These represent the mean net primary productivity (NPP) and vegetation cover of the ecosystem within the region, respectively. j and f j Net primary productivity (NPP) and vegetation cover (f) for pixel j v );

[0040] The calculation of NPP is based on the CASA light energy utilization model. Its estimation formula is as follows:

[0041] NPP(x,t) = APAR(x,t) × ε(x,t)

[0042] In the formula, APAR(x,t) represents the photosynthetically active radiation (g·C·m) absorbed by pixel x in month t. -2 ·month -1 ), ε(x,t) represents the actual light energy utilization rate (g·C / MJ) of pixel x in month t; APAR estimation: The value of APAR is determined by the effective solar radiation that the vegetation can absorb and the proportion of incident photosynthetically active radiation absorbed by the vegetation; Light energy utilization rate ε estimation: Light energy utilization rate is the ratio of the chemical potential energy contained in the dry matter produced per unit area in a certain period to the photosynthetically active radiation energy projected onto that area in the same period.

[0043] Vegetation coverage (f) v The calculation formula for ) is as follows:

[0044]

[0045] In the formula, the Normalized Difference Vegetation Index (NDVI) data uses 250m spatial resolution MODIS NDVI MOD13Q1 data provided by the U.S. Geological Survey, with a temporal resolution of 16 days. After data processing such as projection coordinate system transformation and spatial resampling, 30m spatial resolution data consistent with land use is obtained.

[0046] Preferably, in the above-mentioned method for assessing ecosystem service losses due to regional climate change, in S1, land use types are classified into cultivated land, forest land, grassland, urban and rural construction land, water area and unused land according to the Chinese remote sensing interpretation classification standard.

[0047] Preferably, in the above-mentioned method for assessing ecosystem service loss due to regional climate change, S2 defines 1 hm 2 The economic value of natural grain produced per unit area of ​​farmland per year is 1, calculated according to the formula based on the yield per unit area of ​​grain crops, the sown area, and the national average price of each grain crop.

[0048]

[0049] In the formula, E a 1hm 2 The annual economic value of grain crops in farmland (yuan / hm) 2 ); i represents the crop type, m i Let p be the national average price (yuan / t) of grain crop i; i Yield per unit area (t / hm) of i type of grain crop 2 );q i Let i be the area of ​​a certain type of grain crop (hm). 2 M represents the total area of ​​type i grain crops (hm²). 2 );

[0050] The formula for calculating the value of ecosystem services is:

[0051] ESV=∑A i ×VC i

[0052] ESV j =∑A i ×VC ij

[0053] In the formula, ESV represents the total ecosystem service value of the study area; ESV j The value of the j-th ecosystem service; A i VC represents the distribution area of ​​land use type i in the study area. iThe value of ecosystem services per unit area for land use type i; i is divided into five types: woodland, grassland, farmland, field, and water. Land use data are from Landsat TM / ETM and HJ CCD remote sensing data from 2010 (growing season and non-growing season), with a spatial resolution of 30m.

[0054] Specifically, in this embodiment, the key ecological function zone of the Altai Mountain forest and grassland is taken as the research object, and the method of the present invention is used to analyze the impact of climate change on the ecosystem service value of the key ecological function zone of the Altai Mountain forest and grassland and to estimate the loss of ecosystem service value.

[0055] The Altay Mountain Forest-Grassland Ecological Functional Zone (hereinafter referred to as the "Altai Ecological Functional Zone") is located in the northern part of Xinjiang Uygur Autonomous Region, with a total area of ​​approximately 118,000 km2. Its administrative area includes seven counties and cities: Altay City, Habahe County, Burqin County, Jimunai County, Fuhai County, Fuyun County, and Qinghe County (including the 10th Agricultural Division of Xinjiang Production and Construction Corps).

[0056] In this embodiment, the specific implementation process of step S1 is described as follows:

[0057] Landsat TM / ETM / OLI remote sensing images with a spatial resolution of 30m from April to September of 2000, 2005, 2010, 2015, and 2018 were selected from the U.S. Geological Survey website (http: / / earthxplorer.usgs.gov / ). The data orbital numbers are 140 / 28, 140 / 29, 141 / 27, 141 / 28, 141 / 29, 142 / 27, 142 / 28, 141 / 29, 143 / 26, 143 / 27, 143 / 28, and 144 / 27. Remote sensing data was preprocessed using ENVI and ArcGIS software, including band combination, geometric correction, and projection conversion. A standard false-color composite scheme was selected, interpretation markers were established, and visual interpretation was performed. Based on the Chinese Remote Sensing Interpretation Classification Standard (2017 latest version: Classification of Current Land Use (GBT 21010-2017)) and actual land use characteristics, the land use types in the study area were divided into six categories: cultivated land, forest land, grassland, urban and rural construction land, water area, and unused land. Vegetation NDVI data were obtained from the 2000-2018 Terra-MODIS13Q1, 16-day composite product data provided by NASA (http: / / ladswed.nascom.gov / ), with a spatial resolution of 250m. Digital elevation data (DEM) was obtained from the U.S. Geological Survey website (http: / / earthxplorer.usgs.gov / ), with a resolution of 30m.

[0058] Using data from statistical yearbooks such as the "Statistical Yearbook of Xinjiang Uygur Autonomous Region 2000-2019", the "Statistical Yearbook of Ili Kazakh Autonomous Prefecture 2000-2019", and the "Statistical Yearbook of Altay Region 2007-2019", the unit area output value and equivalent factor of grain crops in the study area were calculated.

[0059] The meteorological data comes from the Xinjiang Uygur Autonomous Region Meteorological Bureau, which provides daily temperature and precipitation data from 2000 to 2018.

[0060] This study statistically analyzed land use types interpreted from five periods of remote sensing images of the study area, calculated the magnitude of land use change, reflected the total changes in different land use types in the study area, and understood the overall trend of change in various land use types in the study area. The formula is as follows:

[0061] W i =(L b -L a ) / L a ×100%

[0062] W i Land use change rate (%); L a L b The areas of a certain land type are respectively at the beginning and end of the study period;

[0063] Based on Costanza's evaluation model and taking into account China's specific circumstances, Xie Gaodi et al. derived a table of equivalent ecosystem service value per unit area in China. This table defines the equivalent value of ecosystem services per unit area in 1 hm². 2 The economic value of natural grain produced per unit area of ​​farmland per year is 1 based on the national average yield, the sown area of ​​grain crops, and the national average price of each grain crop in the study area, and is calculated according to the formula.

[0064]

[0065] In the formula, E a 1hm 2 The annual economic value of grain crops in farmland (yuan / hm) 2 ); i represents the crop type, m i Let p be the national average price (yuan / t) of grain crop i; i Yield per unit area (t / hm) of i type of grain crop 2 );q i Let i be the area of ​​a certain type of grain crop (hm). 2 M represents the total area of ​​type i grain crops (hm²). 2 );

[0066] The formula for calculating the value of ecosystem services is:

[0067] ESV=∑A i ×VC i

[0068] ESV j =∑A i ×VC ij

[0069] In the formula, ESV represents the total ecosystem service value of the study area; ESV j The value of the j-th ecosystem service; A i VC represents the distribution area of ​​land use type i in the study area. i The value of ecosystem services per unit area for land use type i; i is divided into five types: woodland, grassland, farmland, field, and water. Land use data are from Landsat TM / ETM and HJ CCD remote sensing data from 2010 (growing season and non-growing season), with a spatial resolution of 30m.

[0070] Considering the absence of human and material inputs, the ecological value of a natural ecosystem is one-seventh of the value of grain provided per unit area of ​​farmland. Therefore, the ecological value equivalent factor of the Altai ecological functional zone is determined to be one-seventh of the average market value of grain in that year. Specifically, based on the average grain yield of 5610 kg / hm² in the functional zone from 2000 to 2018 and the average grain price of 2.1066 yuan / kg in 2015, the ecosystem service value equivalent factor of the functional zone is calculated to be 1688.29 yuan / hm².

[0071] To more accurately reflect the spatial differences in ecosystem service value, vegetation cover was selected as an indicator for adjusting ecosystem service value at the cell scale. The ecosystem service value was adjusted based on two parameters: net primary productivity (NDVI) and vegetation cover. Since water bodies have sparse vegetation, their NDVI is mostly negative; therefore, further adjustments were only made for the ecosystem service values ​​of cultivated land, forests, and grasslands.

[0072]

[0073] In the formula, the Normalized Difference Vegetation Index (NDVI) data uses 250m spatial resolution MODIS NDVI MOD13Q1 data provided by the U.S. Geological Survey, with a temporal resolution of 16 days. After data processing such as projection coordinate system transformation and spatial resampling, 30m spatial resolution data consistent with land use is obtained.

[0074] Ecosystems and their environments exhibit diversity, and the value of ecosystem services demonstrates spatial heterogeneity. Wang Yan et al. corrected the unit price of ecosystem service value by using the ratio of local biomass to the national average biomass of the same ecosystem in the study area. This paper calculated the NPP (Nuclear Power Product) for the same ecosystem and found that the NPP value in the Altai ecological functional zone was only 1 / 10 of the national NPP, which is related to the low local precipitation and does not reflect the actual situation.

[0075] This paper uses the ratio of the Altai ecological functional zone to the national NDVI from 2000 to 2018 to correct the ecosystem service value of the study area. The corrected ecosystem service value coefficients are: cultivated land (0.4617), forest (0.4545), grassland (0.7212), water area (0.4266), construction land (1.1737), and unused land (0.5192).

[0076] Referring to the equivalent factor table of China's terrestrial ecosystem service value by Xie Gaodi et al., a systematic table of land use ecosystem service value for functional zones was calculated (Table 1).

[0077] Table 1 Ecosystem service value coefficients of land use types in the Altai ecological functional zone

[0078]

[0079]

[0080] As shown in Table (2), the total area of ​​the Altai Ecological Functional Zone is approximately 17,764,600 hm2. From 2000 to 2015, the areas of grassland, forest land, and unused land showed a downward trend. The grassland area decreased from 4,447,600 hm2 in 2000 to 4,387,600 hm2 in 2015, and its proportion decreased from 37.80% to 37.29%. The forest land area decreased from 608,400 hm2 in 2000 to 607,600 hm2 in 2015, and its proportion remained basically the same at 5.17%. The unused land area decreased from 6,134,700 hm2 in 2000 to 6,024,900 hm2 in 2015, and its proportion decreased from 52.15% to 51.21%. From 2000 to 2015, the areas of cultivated land, water areas, and construction land showed an increasing trend. The cultivated land area increased from 370,800 hm2 in 2000 to 515,500 hm2 in 2015, with its proportion increasing from 3.15% to 4.38%. The water area increased from 181,900 hm2 in 2000 to 195,800 hm2 in 2015, with its proportion increasing from 1.55% to 1.66%. The construction land area increased from 21,200 hm2 in 2000 to 33,200 hm2 in 2015, with its proportion increasing from 1.55% to 1.66%. The dynamic degree of land use change was calculated based on the land use remote sensing interpretation data of the Altai ecological functional zone, and the results are shown in Table (3). In terms of area change, cultivated land saw the largest increase from 2000 to 2015, with an increase of 144,700 hm2, while unused land saw the smallest increase, decreasing by 109,800 hm2. In terms of magnitude of change, construction land saw the largest change from 2000 to 2015, at 56.42%, while unused land saw the smallest change, at -1.79%. In terms of rate of change, construction land saw the fastest change from 2000 to 2015, at 3.76%, while unused land saw the slowest change, at -0.12%. Looking at individual land use types, cultivated land, construction land, and water area showed a significant increasing trend from 2010 to 2015, while grassland and unused land showed a significant decrease, and forest land remained relatively stable overall.

[0081] Table 2. Land Use Type Changes in the Altai Ecological Functional Zone from 2000 to 2018

[0082]

[0083]

[0084] Table 3. Ecosystem Service Value of Land Use Types in the Altai Ecological Functional Zone, 2000-2015

[0085]

[0086]

[0087] Temperature changes in the Altai ecological functional zone from 2000 to 2018

[0088]

[0089]

[0090] Changes in precipitation in the Altai ecological functional zone from 2000 to 2018

[0091]

[0092]

[0093] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0094] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing ecosystem service loss due to regional climate change, characterized in that, Includes the following steps: S1. Collect remote sensing data, perform band combination, geometric correction and projection conversion preprocessing on the remote sensing data, select a standard color synthesis scheme, establish interpretation markers, and perform visual interpretation; S2. Calculate and statistically analyze the output value per unit area of ​​grain crops and the equivalent factor. The equivalent factor is 1 / 7 of the average market value of grain in the current year. S3. Statistical analysis of land use types interpreted from remote sensing images is conducted to calculate the magnitude of land use change, reflecting the total changes in different land use types in the study area and the overall trend of change for each land use type. The formula is as follows: ； W i The percentage change in land use is expressed as %; L a L b The areas of a certain land type are respectively at the beginning and end of the study period; S4. Select vegetation cover as an indicator. Based on the selected vegetation cover indicator, and using the two parameters of net primary productivity and vegetation cover, adjust the ecosystem service value using the following adjustment formula: ； In the formula, NPP mean and f mean These represent the mean net primary productivity (NPP) and vegetation cover of the ecosystem within the region, respectively. j and f j For pixel j, the net primary productivity (NPP) and vegetation cover (f) are... v ; The calculation of NPP is based on the CASA light energy utilization model, and its estimation formula is as follows: ； In the formula, APAR(x,t) represents the photosynthetically active radiation absorbed by pixel x in month t, with units of g·C·m. -2 ·month -1 , (x,t) represents the actual light energy utilization rate of pixel x in month t, in g·C / MJ; APAR estimation: The value of APAR is determined by the effective solar radiation absorbed by the vegetation and the proportion of incident photosynthetically active radiation absorbed by the vegetation; light energy utilization rate The estimation of light energy utilization rate is the ratio of the chemical potential energy contained in the dry matter produced per unit area in a certain period to the photosynthetically active radiation energy projected onto that area in the same period. Vegetation coverage f v The calculation formula is as follows: ； In the formula, the Normalized Difference Vegetation Index (NDVI) data uses the 250m spatial resolution MODISNDVI MOD13Q1 data provided by the U.S. Geological Survey, with a temporal resolution of 16 days. After projection coordinate system transformation and spatial resampling data processing, the spatial resolution of 30m data consistent with land use is obtained.

2. The method for assessing regional climate change ecosystem service loss according to claim 1, characterized in that... In S1, based on the Chinese remote sensing interpretation classification standard, land use types are divided into cultivated land, forest land, grassland, urban and rural construction land, water area and unused land.

3. The method for assessing regional climate change ecosystem service loss according to claim 1, characterized in that, S2 defines 1hm 2 The economic value of natural grain produced per unit area of ​​farmland per year is 1, calculated according to the formula based on the yield per unit area of ​​grain crops, the sown area, and the national average price of each grain crop. ； In the formula, i = 1, ..., n; E a 1hm 2 The economic value of grain crops on farmland each year is expressed in yuan / hm². 2 ; i represents the crop type, m i p represents the national average price of grain crop i, expressed in yuan / t. i Let be the yield per unit area of ​​type i grain crop, expressed in t / hm². 2 ;q i Let be the area of ​​type i grain crop, in hectares (hm²). 2 M represents the total area of ​​type i grain crop, in hectares (hm²). 2 ; The formula for calculating the value of ecosystem services is: ； In the formula, ESV represents the total ecosystem service value of the study area; ESV j The value of the j-th ecosystem service; A i VC represents the distribution area of ​​land use type i in the study area. i The value of ecosystem services per unit area for the i-th land use type; i is divided into five types: woodland, grassland, farmland, land, and water.

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