Analysis of land-use change in the Dianchi watershed
This paper reclassified and area-counted the land use data of the three periods to obtain the land-use changes, as listed in Table 2.
Table 2 indicate that the main land types in the Dianchi watershed are arable land, forest land, grassland, and construction land, which cover more than 85% of the total study area. Between 2000 and 2010, the area of forest land, grassland, construction land, and wetland increased, whereas the arable land and watershed area decreased. This is because the study area is located in the urban center of Yunnan Province, where economic and population development has led to an increase in the area of built-up land. Construction land increased to 6,669.71 hectares, rising from 7.65% to 9.92% between 2000 and 2010. By 2020, construction land increased significantly to 22.18%. The area of arable land decreased from 38.03% to 24.55% of the total study area, and the area of forest land and grassland did not change much.
The land use transfer matrix can reflect the interconversion relationship between various land types in the study area. Moreover, its ranks represent the land classification categories in different years. The Intersect tool ArcGIS was used to process the data of three phases of land use from 2000–2010 and 2010–2020; the land use transfer matrix was generated in an Excel sheet (Table 3).
The results indicated that arable and water lands decreased from 2000 to 2010, with varying increases in the areas of grassland, building land, forest land, and wetlands. Arable land changed the most, with 19,391.13 hectares transforming to other land types. Of these, 38.50% was converted to grassland, 31.90% to building land, and 25.42% to woodland. Most water lands were converted to woodland, with the largest proportion transforming to building land at 6,664.30 hectares. Grassland and arable land were transformed the most, at 811.62 hectares and 6186.56 hectares, respectively. In addition, relatively large proportions of grassland and forest land were formed by transformation, at 5739.03 hectares and 5003.38 hectares. The area of the formed wetland was small; the area fluctuated initially and stabilized later. In summary, each land use type changed during this decade, with construction land being the most influenced by economic development. National policies also affected land-use changes; for example, the policy of returning farmland to forests and grasslands has had an obvious effect during this decade.
Between 2010 and 2020, the area of arable land, forest land, grassland, and wetland decreased, whereas the area of construction land and water land increased. Cultivated land was transformed the most, at 27,829.95 hectares; of which, 84.64% was converted to building land. During this decade, construction land was the most formed land, with an increase of 36,434.26 hectares. Grassland, arable land, and forest land were also partially converted to construction land, with 7,957.7 hectares, 23,555.48 hectares, and 4,630.7 hectares, respectively. Changes in other land types were relatively small, with wetlands being transformed at 427.02 hectares, and water lands being formed at 1,350 hectares. Wetlands exhibited the smallest area change among the six land types because of the smallest area of wetlands themselves. Overall, between 2010 and 2020, all land types changed under the influence of urbanization and policies at the catchment scale, with a large amount of arable land and grassland being converted into construction land. Despite strategies and facilities to protect arable land and basic farmland in recent years, a significant reduction in arable land has been observed. Therefore, arable land and wetlands must be protected in the future.
Modelling projections of land-use change in the Dianchi watershed
Selection of driving factors
Because of the interaction and influence of many natural, economic, and social factors, the evaluation of land-use change is challenging. Because of the comprehensiveness and accessibility of data, this paper selected natural, socioeconomic, and transportation location factors as the driving factors affecting land-use change in the Dianchi watershed. The study area is located on the Yunnan–Guizhou Plateau; therefore, topographic conditions (such as elevation, slope, and slope direction) are the basic natural factors influencing the spatial land-use pattern in the region. Transport location factors influenced the expansion of construction and other lands in the study area; therefore, the geographical location of this land should be studied when modeling future land use patterns. In the actual land-use change simulation, human socioeconomic factors, such as human activities, also play a substantial role in the spatial distribution of land use. In this study, 10 driving factors were identified, and data on these factors were spatially processed according to the requirements of the GeoSOS–FLUS model; the raster results of each driver were obtained (Fig. 2).
Model parameter setting and accuracy verification
This paper used the ANN-based adaptive probability calculation GeoSOS–FLUS model; 10 drivers were introduced, and the ratio of the model training sample was set. On the basis of the relevant information, 20 sample parameters, 12 hidden layers, and the land use types in 2010 were selected using the random sampling method. Finally, the predicted value of land use suitability in the Dianchi watershed in 2010 was derived; an RMSE of 0.279 indicated that the model has a satisfactory training effect.
A cost matrix for different land use types was set up on the basis of the actual land use type transfer matrix for the Dianchi watershed from 2010 to 2020 (Table 4). Land types that cannot be converted to other lands and grasslands that cannot be converted to wetlands were identified on the basis of the specific conditions of the study area. The neighborhood weight parameter was used to indicate the expansion capacity of the land use type. The expansion capacity ranged from 0 to 1, with values closer to 1 indicating a stronger expansion capacity for the land type. Adjustment and simulation of the changing characteristics of land use types and local land use policies in Kunming in recent years resulted in the following neighborhood factors: arable land 0.4, forest land 0.4, grassland 0.3, wetland 0.25, water 0.5, and construction land 1.0. On the basis of the land use status in 2000, 2010, and 2020, a Markov chain model was used to forecast the land use status in 2020 and 2030 (Table 5).
The model parameters set above and the 2010 base land use data were imported into the GeoSOS–FLUS model to simulate the land use results in 2020. The 2020 land use simulation results of the Dianchi watershed were verified against the actual land use data, and the Kappa coefficient was calculated to verify the model's effectiveness. Kappa 0.75 indicates the high simulation accuracy of the model (Li, 2018). The Kappa coefficient of this simulation result is 0.79, and the overall accuracy (OA) was 83.44%, indicating that the model has certain feasibility and applicability.
Predicted results
The simulation results of land use in the Dianchi basin in 2020 indicated that the FLUS model can satisfactorily simulate future land-use changes. Factors, such as policies, economic development, and ecological protection, may affect future land-use changes in the Dianchi basin. We assumed that under the natural development model, land-use changes during 2020–2030 would be less influenced by policies and other factors and would maintain the development status in the current year. The Markov model was used to calculate the number of image elements for each land use type in the study area in 2030 (Tables 5). The model parameters were maintained to simulate the land-use results in 2020 and to predict the land-use changes in 2030 (Fig. 3).
The simulation results indicated that the area of built-up land increased the most, and the corresponding area of arable land decreased the most. Most of the reduced arable land was transformed into built-up land, and the areas with relatively large changes were concentrated near the existing built-up areas. In addition to other land types that were reduced and transformed into built-up land, the change areas were associated with the existing built-up areas in patches.
Valuation of ESVs by using the Xie Value Equivalent Method
Valuation of ESVs
To calculate , rice, wheat, and maize were selected as food crops. The collected data and equation (3–8) were used to calculate the economic value of food production per unit area in the Dianchi basin; the values were RMB 790.40, RMB 1667.20, and RMB 1909.95 in 2000, 2010, and 2020, respectively. The economic value of one ecosystem service equivalent factor in the Dianchi watershed was, therefore, RMB 790.40/hm2, RMB 1667.20/hm2, and RMB 1909.95/hm2 for the three years, respectively. The ESV for each year was calculated by combining equations (3–9), (3–10) and Table 2. The results are presented in Tables 6-8.
The results indicated that the total ESV of the Dianchi basin in 2000 was RMB 5,771.1539 million. The major type of ecosystem services in the Dianchi basin in 2000 were regulating services, valued at RMB 4,671.9810 million in the current year and accounting for 80.95% of the total ESV. Hydrological regulation among the regulating services had the highest service value of RMB 324.8790 million . Support services, provisioning services, and cultural services all had modest service values of RMB660,548,300million, RMB 274.1919 million, and RMB 164.4327 million, respectively, and accounted for 11.45%, 4.75%, and 2.85% of the total value, respectively. In terms of ecosystem type, apart from built-up land (which will not be mentioned below because the ESV of built-up land was not considered), watershed had the largest ESV in 2000 at RMB 3,258..0826 million and accounted for approximately 56.45% of the total value. This was followed by woodland, grassland, and arable land with ESVs of RMB 1,349.4687 million, RMB 808.2959 million, and RMB 343.6666 million respectively, and accounting for 23.38%, 14.00%, and 5.95% of the total value, respectively. Wetlands had the least ESV at RMB 11.6401 million, accounting for 0.20% of the total value.
The results indicated that the total ESV of the Dianchi basin in 2010 was RMB 12028.7197 million. The major type of ecosystem services in the Dianchi basin in 2010 were regulating services, valued at RMB 9,599.2676 million in 2010 and accounting for 79.80% of the total ESV. The value of hydrological regulation among the regulating services was the highest, at RMB 6,488.8161 million. Support services, supply services, and cultural services had modest service values of RMB 1,472.3415 million, RMB 596.4383 million, and RMB 360.6723 million, respectively, and accounted for 12.24%, 4.96%, and 3.00% of the total value. In terms of land use type, water land had the largest ESV in 2010, at RMB 640,138,600 and accounting for approximately 53.22% of the total value. This was followed by forest land, grassland, and arable land, with service values of RMB 3,040.2577 million, RMB 1,890.4433 million, and RMB 622.6315 million, respectively, and accounting for 25.27%, 15.71%, and 5.18%, respectively. Wetlands had the smallest ESV of RMB 74.0065 million, representing 0.61% of the total value.
The results indicated that the total ESV of the Dianchi basin in 2020 was RMB 13,273.2436 million. The major type of ecosystem services in the Dianchi basin in 2020 were regulating services, at RMB 10,624.9732 million and accounting for 80.05% of the total ESV. Among regulating services, the service value of hydrological regulation was the highest, at RMB 7,380.6395 million. Support services, provisioning services, and cultural services had fewer service values, at RMB 1,540.8741 million, RMB 723.8420 million, and RMB 383.5544 million, respectively, and accounting for 11.61%, 5.45%, and 2.89% of the total value, respectively. In terms of land use types, water land had the highest ESV in 2020, at RMB 7,553.9356 million and accounting for approximately 56.91% of the total value. This was followed by forest land, grassland, and arable land, with service values of RMB 3,231.1919 million, RMB 1,906.5875 million, and RMB 536.0853 million, respectively, and accounting for 24.34%, 14.36%, and 4.04% of the total value, respectively. Wetlands had the smallest ESV of RMB 45.4433 million, accounting for 0.34% of the total value.
ESV prediction
On the basis of the existing economic value of food production per unit area in 2000, 2010, and 2020, the economic value of one ecosystem service equivalent factor in the study area in 2030 was predicted to be RMB 2469.72/hm2. Subsequently, the ESVs in 2030 were predicted on the basis of the model’s predicted land use in 2030 and by combining equations (3–9) and (3–10); the results are presented in Table 9.
The results indicated that the total ESV of the Dianchi basin in 2030 was RMB 16,592.6958 million. The major type of ecosystem services in the Dianchi basin in 2030 were regulating services, valued at RMB 13,331.3619 million in 2030 and accounting for 80.34% of the total ESV. Among regulating services, the service value of hydrological regulation was the highest, at RMB 9,493.4356 million. Support services, supply services, and cultural services had less service value, accounting for 11.02%, 5.85%, and 2.79% of the total value, respectively. In terms of land use type, the watershed had the largest ESV in 2030, at RMB 9,994.8029 million, and accounted for approximately 60.24% of the total value. This was followed by woodland, grassland, and arable land, accounting for 23.22%, 13.18%, and 3.16% of the total value, respectively. Wetlands had the smallest ESV at RMB 33.8672million, accounting for 0.20% of the total value.
Analysis of changes in the ESVs
This paper used ESVs for each year to calculate the contribution of each ecosystem type (land-use type) to ESVs for each year and the changes; the results are presented in Table 10.
The results indicated that the ESV in the Dianchi basin steadily increased from 2000 to 2020, with a total increase of RMB 7,502.0897 million and a change rate of 129.99%. The greatest change was observed from 2000 to 2010, with an increase in the total value by RMB 6,257.5658 million and a change rate of 108.48%. A modest change was observed from 2010 to 2020, with an increase in the total value by RMB 1,244.5239 million and a change rate of only 10.35%. In terms of ecosystem types, the contribution of watershed was the highest, with a contribution rate of over 53%; moreover, the value volume increased from RMB 3,258.0826 million in 2000 to RMB 7,553.9356 million in 2020, an increase of 131.85%. The value of woodland and grassland also increased, with the value of woodland increasing from RMB 1,349.4687 million in 2000 to RMB 3,040.2577 million in 2010, followed by RMB 1,906.5875 million in 2020, maintaining a contribution rate of approximately 24%. The value of grassland increased from RMB 1,890.4433 million in 2010 to RMB 1,906.5875 million in 2020, thereby maintaining a contribution rate of approximately 15%. The contribution rate of arable land exhibited a steady decrease from 5.95% to 4.04%. However, the value volume first increased from RMB 343.6666 million in 2000 to RMB 622.6315 million in 2010, followed by a decrease of RMB 86.5463 million from 2010 to 2020. The contribution of wetlands and the value volume were less, with the contribution rate remaining below 1%. However, the variation was significant, with the value volume increasing by 535.79% from 2000 to 2010 and decreasing by 38.60% from 2010 to 2020. Overall, the value of all ecosystem types increased from 2000 to 2020. Compared with 2020, the projected ESV results for 2030 exhibited a decrease in the contribution of all ecosystem types except for waters, which increased from 56.91% to 60.24%. The most notable change was the total ESV of arable land, which changed from RMB 536.0853 million in 2020 to RMB 524.0221 million in 2030, a change of –2.25%. The total ESV of wetlands also decreased by RMB 11.5761 million. The total ESV of woodlands, grasslands, and water lands increased to varying degrees; moreover, the total ESV from 2020 to 2030 increased by 3,319.4522 million, a change of 25.01%.