A Simulation Analysis of Land Use Changes in the Yarlung Zangbo River and Its Two Tributaries of Tibet Using the Markov–PLUS Model
Abstract
:1. Introduction
- On the basis of land use and cover change, propose the regional development goals of the Yarlung Zangbo River and its two tributaries of Tibet.
- Analyze the driving mechanism of land use expansion in the Yarlung Zangbo River and its two tributaries of Tibet, and simulate the land use situation in future landscapes.
- Provide different decision-making perspectives and a basis for the future spatial pattern of land use.
2. Study Area and Data
2.1. Study Area
2.2. Data and Preprocessing
3. Methods
3.1. Landscape Presets
- (1)
- Periodic land use change landscape. In total, 17 driving factors were selected based on the past land use changes and regional land expansion factors (GDP, population, night lights, etc.) and natural factors (precipitation, dryness, elevation, rivers, etc.). In the actual development of the two rivers region, elevation, temperature, and ecological function were selected as the restrictive factors of regional development, and the Markov model was used to predict the scale of various types of land in the future without considering the restrictions of major policies.
- (2)
- Agricultural land conservation landscape. In the Markov model, the transfer matrix is modified to strictly control the conversion of agricultural land to residential areas, while reducing the conversion probability by 60% to strictly conserve agricultural land.
- (3)
- Ecological conservation landscape. Ecological restoration and ecological conservation have become an important part of regional and watershed developments. The ecological conservation landscape adds ecological function zoning; severely restricts the transformation of ecological conservation red line areas; and reduces the probability of converting waters (natural and artificial waters) to 30%, grassland and forest land to 50%, and agricultural land to residential areas to 30%. The reduced agricultural land is further converted into forest land, and making provisions for water systems and 100 m buffer zones also reduces the probability of land use changes.
3.2. Land Use Multi-Landscape Simulation Parameter Setting
- (1)
- Forecast of Land use change
- (2)
- Spatial Neighborhood Weight Calculation
- (3)
- Numerical calculation of adaptive inertial coefficient
- (4)
- Restricted area setting and cost transfer matrix
4. Results
- (1)
- Quantity accuracy check
- (2)
- Spatial matching accuracy
4.1. The Overall Change of the Landscape Pattern in the Study Area
4.2. Analysis of the Driving Mechanism of Land Expansion
4.3. Analysis of Multi-Landscape Simulation Results
- (1)
- Periodic land use change landscape
- (2)
- Agricultural land conservation landscape
- (3)
- Ecological conservation landscape
4.4. Dynamics of Land Landscape Pattern in Multi-Landscape Simulation
- (1)
- The periodic land use changes landscape, due to the lack of regional policies and national development guidelines, each type of landscape pattern presents agricultural land, ecological land landscape fragmentation, a complex shape, and low connectivity compared to the other two landscapes. The aggregation index of grassland in the periodic land use change landscape is only 90.46%, which is less than that of the agricultural land conservation and ecological conservation landscapes. The aggregation index of forest land is only 75.75% in the periodic land use changes landscape, much lower than the agricultural land conservation landscape and the ecological conservation landscape. The aggregation index values of agricultural land and residential area in the periodic land use change landscape are 80.12% and 74.47%, respectively, which are also lower than those in the agricultural land conservation landscape, and the result for the ecological conservation landscape is 0.053, which is lower than the agricultural land conservation landscape and the ecological conservation landscape (Figure 6). It is also explained that under the development situation at present, there is a risk of hindering the improvement of agricultural productivity due to the fragmentation of agricultural land landscapes, and there is also the fragmentation and complication of the ecological landscape, which will lead to the migration of organisms, plant production efficiency and other activities. The problem of a negative impact reflects the urgent need for scientific management and the configuration of future land use activities.
- (2)
- In agricultural land conservation, the connectivity index and the average patch size of the agricultural land were significantly higher than those of the other two landscapes, and their indices were 0.096 and 524.812, respectively (Figure 6). The shape index and patch density of the landscape are less than those of the other two landscapes, with their indices being 51.139 and 0.0075, respectively (Figure 6). In this landscape, the agricultural landscape tends to be concentrated, the degree of fragmentation is reduced, and the landscape shape is regular and orderly. The trend shows that agricultural land has been better conserved in agricultural land conservation practices. However, in the arable land conservation priority development landscape, the landscape connectivity and average patch density of forest land, water area, and grassland are lower than the ecological conservation landscape, and the ecological-type landscape shows a fragmentation trend. Contradictions are still prominent.
- (3)
- Under the ecological conservation landscape, due to the establishment of a regional sustainable development path for the coordinated development of regional ecological environment conservation and economic development, compared with the periodic land use change and ecological conservation landscapes, the landscape connectivity and fragmentation of the ecological land are higher. Both the degree and connectivity have been considerably improved. The aggregation index values of woodland and water are 88.21% and 80.00%, respectively, the landscape shape index values are 74.06 and 52.99, and the average patch size values are 1088.14 and 155.76 (Figure 6). However, there is also the risk of the fragmentation of agricultural land and reduced landscape connectivity, with the patch size and landscape connectivity index of agricultural land being 283.89 and 0.078, respectively (Figure 6), thereby hindering the risk of agricultural productivity improvement. The landscape connectivity index is only 0.056, which hinders the development of large- and medium-sized cities in the region. Therefore, how to balance regional ecological environment conservation and the sustainable and healthy development of the regional economy has become a challenge for regional development practices.
5. Discussion
- (1)
- The use and change of land resources is a complex and dynamic process of change. Although 17 driving factors, restrictive factors, and regional policy restrictions were selected by combining human and natural factors in the study, the climate change occurring in Tibet is drastic. The influence of climate change is not considered separately, and the influence factors of the model will be further optimized in the follow-up research in the future [63,64].
- (2)
- The study determined that the simulation accuracy of various types of land use practices was highly dependent on the pixel size. The smaller the pixel, the higher the simulation accuracy, indicating that the research accuracy can be improved further with the refinement of the study area.
- (3)
- There is a certain degree of subjectivity in the setting of model parameters. For example, the spatial neighborhood weight basically refers to the research results of previous studies, and is determined according to the human influence of different land types in the Yarlung Zangbo River and its two tributaries of Tibet. Although the simulation is achieved after continuous debugging, in the future, more objective model parameters need to be consistently determined in the research.
- (4)
- Grassland expansion is mainly affected by altitude, population density, precipitation, topography, and distance from the roads (Figure 4). The expansion of agricultural land is mainly affected by altitude, GDP, population density, and road distance (Figure 4). The expansion of forest land is mainly affected by altitude, population density, and precipitation (Figure 4). The water expansion is mainly affected by altitude, distance from rivers, slopes, and road distances (Figure 4). The expansion of residential areas is mainly affected by lighting conditions at night, altitude, slope, and the distance from the city (Figure 4).
6. Conclusions
- (1)
- The landscape pattern of the watershed drastically changes, and the landscape pattern of different land types is different. Due to the rapid expansion of residential areas, the intensity of agricultural and animal husbandry production activities increased. In the past 30 years, each patch type of the watershed landscape presented a balanced distribution in the landscape, the dominant components in the landscape structure weakened the influence of the overall landscape pattern, and the overall trend was fragmented and balanced. In all kinds of land use landscapes, in addition to the trend towards regularization of agricultural land, affected by the development trends of fragmentation of residential areas, ecological land also showed a trend of fragmentation and a complex shape, and the conflict between urban development and ecological conservation in the river basin was intensified (Figure 4).
- (2)
- The Markov–PLUS model has a high simulation accuracy for different land types in the study area, and can sufficiently simulate the changes in the demand for different land types in the Yarlung Zangbo River and its two tributaries of Tibet (Table 4). The cross-validation of the various types of periodic land use changes in the Yarlung Zangbo River and its two tributaries of Tibet in 2018 simulated by the Markov–PLUS model and the actual land use data of various types in the region in 2018 shows that the kappa value is 0.93. This model can be used to predict the changes in land use and land types in the districts and counties of the river basin, and even the plateau basin.
- (3)
- The three development landscapes basically reflect different regional development models. From the perspective of the spatial pattern of land use expansion, the three development landscapes show the most significant changes in agricultural land, forest land, water area, and grassland. The landscape pattern of the periodic land use changes presents the characteristics of disordered development, and the landscape tends to be fragmented and complicated. In the agricultural land conservation landscape, as a result of the management and control of agricultural land, the agricultural land landscape tends to develop in a regular and orderly manner, but attention should be paid to the conflict occurring between agricultural land and ecological conservation practices. In the ecological conservation landscape, the ecological space and residential area are conserved and controlled, meeting the requirements of ecological conservation (Figure 3).
- (4)
- With the rapid social and economic development of Tibet and the steady improvement of the level of urbanization, it is determined that the expansion of the residential area is irreversible in the future, but a disorderly expansion will threaten the food and ecological security in the region. While ensuring social and economic developments in the basin, it is necessary to consider the efficient use of residential areas in the basin in the future, and ecological land, such as forest land, grassland, and water area, and production land, such as agricultural land, should not be encroached.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Sources | Technical Information |
---|---|---|
Land use and land cover (1990 to 2020) | Resource and Environment Science and Data Center of China (https://www.resdc.cn/, accessed on 28 March 2022). | Raster, 30 m × 30 m |
Digital elevation model (DEM) | Geospatial Data Cloud (China) (https://www.giscloud.cn/, accessed on 28 March 2022). | Raster, 30 m × 30 m |
Night-time lights | National Tibetan Plateau Data Center of China (https://www.tpdc.ac.cn, accessed on 28 March 2022) | Raster, 1 km ×1 km |
Aridity, precipitation, temperature | Tibet Meteorological Bureau (http://xz.cma.gov.cn/, accessed on 28 March 2022) | Vector |
Gross domestic product (GDP) | Resource and Environment Science and Data Center of China (https://www.resdc.cn/, accessed on 28 March 2022). | Raster, 1 km ×1 km |
Population density | World Pop Country Datasets (https://www.worldpop.org/, accessed on 28 March 2022). | Raster, 1 km ×1 km |
Livestock | World Food and Agriculture Organization (FAO) (https://data.apps.fao.org/, accessed on 28 March 2022) | Raster, 1 km ×1 km |
Soil denudation | National Tibetan Plateau Data Center of China (https://www.tpdc.ac.cn, accessed on 28 March 2022) | Raster, 1 km ×1 km |
Main roads, town, and water | Open Street Map (http://www.openstreetmap.org/, accessed on 28 March 2022) | Vector |
Ecological function area | Tibet natural resources bureau | Vector |
Type of Land Use | Woodland | Grassland | Agricultural Land | Waters | Residential Area | Unused Land |
---|---|---|---|---|---|---|
Neighborhood factor parameters | 0.01 | 0.3 | 0.2 | 0.4 | 1 | 0.5 |
Landscape Settings | Periodic Land Use Change Landscape | |||||
Woodland | Grassland | Agricultural Land | Waters | Residential Area | Unused Land | |
Woodland | 1 | 1 | 1 | 1 | 1 | 1 |
Grassland | 1 | 1 | 1 | 1 | 1 | 1 |
Agricultural land | 1 | 1 | 1 | 1 | 1 | 1 |
Waters | 1 | 1 | 1 | 1 | 1 | 1 |
Residential area | 0 | 0 | 0 | 0 | 1 | 0 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
Landscape Settings | Agricultural Land Conservation Landscape | |||||
Woodland | Grassland | Agricultural Land | Waters | Residential Area | Unused Land | |
Woodland | 1 | 1 | 1 | 0 | 1 | 1 |
Grassland | 1 | 1 | 1 | 1 | 1 | 1 |
Agricultural land | 0 | 0 | 1 | 0 | 0 | 0 |
Waters | 0 | 1 | 1 | 1 | 1 | 1 |
Residential area | 0 | 0 | 0 | 0 | 1 | 0 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
Landscape Settings | Ecological Conservation Landscape | |||||
Woodland | Grassland | Agricultural Land | Waters | Residential Area | Unused Land | |
Woodland | 1 | 0 | 0 | 0 | 0 | 0 |
Grassland | 1 | 1 | 0 | 1 | 0 | 0 |
Agricultural land | 1 | 1 | 1 | 1 | 1 | 1 |
Waters | 0 | 0 | 0 | 1 | 0 | 0 |
Residential area | 0 | 0 | 0 | 0 | 1 | 0 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
Type of Land Use | Actual in 2018 | Forecast in 2018 | Accuracy Rating |
---|---|---|---|
Agricultural land | 2,478,882 | 2,459,652 | 99.224% |
Woodland | 11,458,724 | 11,414,959 | 99.618% |
Grassland | 35,106,464 | 35,830,173 | 97.980% |
Waters | 1,939,357 | 2,241,711 | 86.512% |
Residential area | 192,692 | 195,781 | 98.422% |
Unused land | 14,743,646 | 13,777,489 | 93.447% |
Landscape Settings | Woodland | Grassland | Agricultural Land | Waters | Residential Area | Unused Land |
---|---|---|---|---|---|---|
Status area in 2018/hm2 | 1,031,285.16 | 3,159,581.76 | 223,099.38 | 174,542.13 | 17,342.28 | 1,326,928.14 |
Periodic land use change landscape for 2038/hm2 | 725,488.20 | 3,222,781.92 | 223,391.88 | 169,398.90 | 22,967.91 | 1,568,699.28 |
Agricultural land conservation landscape for 2038/hm2 | 749,944.98 | 3,216,079.62 | 230,049.81 | 162,986.49 | 22,470.39 | 1,551,198.69 |
Ecological conservation landscape for 2038/hm2 | 1,076,375.16 | 3,232,238.67 | 197,585.10 | 190,270.53 | 21,880.53 | 1,214,428.05 |
Rate of change in Periodic land use change landscape in 2038 (compared to 2018/%) | −0.2965 | 0.0200 | 0.0013 | −0.0295 | 0.3244 | 0.1822 |
Rate of change in agricultural land conservation landscape in 2038 (compared to 2018/%) | −0.2728 | 0.0179 | 0.0312 | −0.0662 | 0.2957 | 0.1690 |
Rate of change in ecological conservation landscape in 2038 (compared to 2018/%) | 0.0437 | 0.0230 | −0.1144 | 0.0901 | 0.2617 | −0.0848 |
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Hao, W.; Cao, Z.; Ou, S.; Qin, Y.; Wang, Z.; Yang, S.; Tiando, D.S.; Fan, X. A Simulation Analysis of Land Use Changes in the Yarlung Zangbo River and Its Two Tributaries of Tibet Using the Markov–PLUS Model. Sustainability 2023, 15, 1376. https://doi.org/10.3390/su15021376
Hao W, Cao Z, Ou S, Qin Y, Wang Z, Yang S, Tiando DS, Fan X. A Simulation Analysis of Land Use Changes in the Yarlung Zangbo River and Its Two Tributaries of Tibet Using the Markov–PLUS Model. Sustainability. 2023; 15(2):1376. https://doi.org/10.3390/su15021376
Chicago/Turabian StyleHao, Wenyuan, Zhenzhu Cao, Shengya Ou, Yi Qin, Zhongbin Wang, Shuang Yang, Damien Sinonmatohou Tiando, and Xin Fan. 2023. "A Simulation Analysis of Land Use Changes in the Yarlung Zangbo River and Its Two Tributaries of Tibet Using the Markov–PLUS Model" Sustainability 15, no. 2: 1376. https://doi.org/10.3390/su15021376
APA StyleHao, W., Cao, Z., Ou, S., Qin, Y., Wang, Z., Yang, S., Tiando, D. S., & Fan, X. (2023). A Simulation Analysis of Land Use Changes in the Yarlung Zangbo River and Its Two Tributaries of Tibet Using the Markov–PLUS Model. Sustainability, 15(2), 1376. https://doi.org/10.3390/su15021376