草业学报 ›› 2022, Vol. 31 ›› Issue (10): 1-17.DOI: 10.11686/cyxb2021386
• 研究论文 •
收稿日期:
2021-10-28
修回日期:
2022-01-10
出版日期:
2022-10-20
发布日期:
2022-09-14
通讯作者:
冯琦胜
作者简介:
E-mail: fengqsh@lzu.edu.cn基金资助:
Zhe-ren JIN(), Qi-sheng FENG(), Rui-jing WANG, Tian-gang LIANG
Received:
2021-10-28
Revised:
2022-01-10
Online:
2022-10-20
Published:
2022-09-14
Contact:
Qi-sheng FENG
摘要:
青藏高原位于我国西部,又被称为“世界第三极”,对我国和世界的生态以及气候变化影响显著。为了评估2000-2020年青藏高原草地地上生物量(aboveground biomass,AGB)的变化情况,本研究采用多种机器学习方法结合MCD43A4产品数据模拟了草地地上生物量,并对该区域草地地上生物量的时空特征进行分析。结果表明:1)构建的机器学习模型中,Rborist模型精度最高,基于筛选后变量的R2 达到0.6484。“prec_05”、“prec_06”、“tp_12”、“NDPI”、“prec_04”、“tmax_01”、“prec_08”、“prec_12”这8个变量与生物量相关;2)青藏高原东南部的生物量要高于西北部,呈现由东南向西北递减趋势;3)2000-2020年间青藏高原草地生物量稳步增长,整体向好发展。青藏高原61.38%的草地变化趋势不具有可持续性,4.67%的草地持续性轻微恶化,持续性明显恶化的区域占比1.19%,呈稳定或恢复趋势的区域占比32.76%。
金哲人, 冯琦胜, 王瑞泾, 梁天刚. 基于MODIS数据与机器学习的青藏高原草地地上生物量研究[J]. 草业学报, 2022, 31(10): 1-17.
Zhe-ren JIN, Qi-sheng FENG, Rui-jing WANG, Tian-gang LIANG. A study of grassland aboveground biomass on the Tibetan Plateau using MODIS data and machine learning[J]. Acta Prataculturae Sinica, 2022, 31(10): 1-17.
序号Number | 植被指数Vegetation index | 名称 Full name | 公式 Equation | 引用 Reference |
---|---|---|---|---|
1 | NDVI | 归一化植被指数Normalized difference vegetation index | [ | |
2 | EVI | 增强型植被指数Enhanced vegetation index | [ | |
3 | EVI2 | 增强型植被指数2 Enhanced vegetation index two | [ | |
4 | DVI | 差值植被指数Difference vegetation index | [ | |
5 | RVI | 比值植被指数Ratio vegetation index | [ | |
6 | SAVI | 土壤调整植被指数Soil adjusted vegetation index | [ | |
7 | MSAVI | 修改型土壤调节植被指数Modified soil adjusted vegetation index | [ | |
8 | OSAVI | 优化土壤调整植被指数Optimized soil adjusted vegetation index | [ | |
9 | SATVI | 土壤调整总植被指数Soil adjusted total vegetation index | [ | |
10 | NDPI | 归一化物候指数Normalized difference phenology index | [ |
表1 本研究中使用的植被指数
Table 1 The vegetation index used in this study
序号Number | 植被指数Vegetation index | 名称 Full name | 公式 Equation | 引用 Reference |
---|---|---|---|---|
1 | NDVI | 归一化植被指数Normalized difference vegetation index | [ | |
2 | EVI | 增强型植被指数Enhanced vegetation index | [ | |
3 | EVI2 | 增强型植被指数2 Enhanced vegetation index two | [ | |
4 | DVI | 差值植被指数Difference vegetation index | [ | |
5 | RVI | 比值植被指数Ratio vegetation index | [ | |
6 | SAVI | 土壤调整植被指数Soil adjusted vegetation index | [ | |
7 | MSAVI | 修改型土壤调节植被指数Modified soil adjusted vegetation index | [ | |
8 | OSAVI | 优化土壤调整植被指数Optimized soil adjusted vegetation index | [ | |
9 | SATVI | 土壤调整总植被指数Soil adjusted total vegetation index | [ | |
10 | NDPI | 归一化物候指数Normalized difference phenology index | [ |
草地类型Grassland type | 风干重Dry weight (kg·hm-2) | 盖度Cover degree (%) | 高度Height (cm) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
最小值Minimum value | 最大值Maximum value | 平均值Mean | 标准偏差Standard deviation | 最小值Minimum value | 最大值Maximum value | 平均值Mean | 标准偏差Standard deviation | 最小值Minimum value | 最大值Maximum value | 平均值Mean | 标准偏差Standard deviation | |
A | 104.00 | 2420.00 | 808.42 | 604.83 | 15.00 | 99.00 | 55.10 | 31.58 | 4.26 | 52.00 | 26.12 | 14.66 |
B | 106.00 | 2384.00 | 604.35 | 486.25 | 9.00 | 100.00 | 52.70 | 31.08 | 2.00 | 16.20 | 4.74 | 3.35 |
C | 57.00 | 5858.00 | 1533.51 | 922.10 | 1.00 | 100.00 | 83.15 | 16.27 | 0.86 | 92.00 | 11.62 | 8.85 |
D | 50.00 | 4154.00 | 588.59 | 519.72 | 5.00 | 100.00 | 55.60 | 23.31 | 1.00 | 53.00 | 8.74 | 5.89 |
E | 11.00 | 1585.00 | 406.76 | 280.79 | 2.00 | 94.00 | 29.16 | 19.51 | 1.50 | 17.00 | 4.92 | 3.14 |
F | 140.00 | 500.00 | 304.06 | 89.62 | 15.00 | 99.00 | 53.09 | 17.18 | 1.00 | 85.00 | 12.98 | 13.91 |
G | 179.00 | 2927.00 | 1276.86 | 812.90 | 10.00 | 95.00 | 52.00 | 33.77 | 4.70 | 34.20 | 13.80 | 7.80 |
H | 790.00 | 1633.00 | 1153.33 | 433.37 | 61.00 | 71.00 | 66.33 | 5.03 | 10.10 | 11.40 | 10.93 | 0.72 |
I | 281.00 | 4816.00 | 2001.48 | 928.60 | 15.00 | 100.00 | 87.13 | 13.37 | 1.60 | 96.70 | 18.61 | 11.88 |
J | 143.00 | 969.00 | 456.15 | 242.65 | 15.00 | 60.00 | 35.62 | 13.90 | 3.00 | 28.00 | 10.00 | 7.56 |
K | 113.00 | 1771.00 | 672.83 | 306.59 | 4.00 | 80.00 | 50.23 | 19.24 | 3.00 | 33.20 | 11.87 | 5.89 |
L | 210.00 | 3009.00 | 831.73 | 462.44 | 7.00 | 97.00 | 61.01 | 19.83 | 1.00 | 81.00 | 15.66 | 9.82 |
M | 8.00 | 1096.00 | 393.85 | 281.47 | 1.00 | 83.00 | 25.24 | 20.01 | 0.95 | 48.00 | 14.85 | 9.21 |
N | 883.00 | 3684.00 | 1812.50 | 966.50 | 48.00 | 99.00 | 75.50 | 21.92 | 0.95 | 25.13 | 8.25 | 8.27 |
总计Total | 8.00 | 5858.00 | 1359.28 | 950.42 | 1.00 | 100.00 | 76.16 | 22.53 | 0.86 | 96.70 | 12.18 | 9.44 |
表2 不同草地类型的调查样本数据基本情况
Table 2 Survey sample data and basic statistics of different grassland type
草地类型Grassland type | 风干重Dry weight (kg·hm-2) | 盖度Cover degree (%) | 高度Height (cm) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
最小值Minimum value | 最大值Maximum value | 平均值Mean | 标准偏差Standard deviation | 最小值Minimum value | 最大值Maximum value | 平均值Mean | 标准偏差Standard deviation | 最小值Minimum value | 最大值Maximum value | 平均值Mean | 标准偏差Standard deviation | |
A | 104.00 | 2420.00 | 808.42 | 604.83 | 15.00 | 99.00 | 55.10 | 31.58 | 4.26 | 52.00 | 26.12 | 14.66 |
B | 106.00 | 2384.00 | 604.35 | 486.25 | 9.00 | 100.00 | 52.70 | 31.08 | 2.00 | 16.20 | 4.74 | 3.35 |
C | 57.00 | 5858.00 | 1533.51 | 922.10 | 1.00 | 100.00 | 83.15 | 16.27 | 0.86 | 92.00 | 11.62 | 8.85 |
D | 50.00 | 4154.00 | 588.59 | 519.72 | 5.00 | 100.00 | 55.60 | 23.31 | 1.00 | 53.00 | 8.74 | 5.89 |
E | 11.00 | 1585.00 | 406.76 | 280.79 | 2.00 | 94.00 | 29.16 | 19.51 | 1.50 | 17.00 | 4.92 | 3.14 |
F | 140.00 | 500.00 | 304.06 | 89.62 | 15.00 | 99.00 | 53.09 | 17.18 | 1.00 | 85.00 | 12.98 | 13.91 |
G | 179.00 | 2927.00 | 1276.86 | 812.90 | 10.00 | 95.00 | 52.00 | 33.77 | 4.70 | 34.20 | 13.80 | 7.80 |
H | 790.00 | 1633.00 | 1153.33 | 433.37 | 61.00 | 71.00 | 66.33 | 5.03 | 10.10 | 11.40 | 10.93 | 0.72 |
I | 281.00 | 4816.00 | 2001.48 | 928.60 | 15.00 | 100.00 | 87.13 | 13.37 | 1.60 | 96.70 | 18.61 | 11.88 |
J | 143.00 | 969.00 | 456.15 | 242.65 | 15.00 | 60.00 | 35.62 | 13.90 | 3.00 | 28.00 | 10.00 | 7.56 |
K | 113.00 | 1771.00 | 672.83 | 306.59 | 4.00 | 80.00 | 50.23 | 19.24 | 3.00 | 33.20 | 11.87 | 5.89 |
L | 210.00 | 3009.00 | 831.73 | 462.44 | 7.00 | 97.00 | 61.01 | 19.83 | 1.00 | 81.00 | 15.66 | 9.82 |
M | 8.00 | 1096.00 | 393.85 | 281.47 | 1.00 | 83.00 | 25.24 | 20.01 | 0.95 | 48.00 | 14.85 | 9.21 |
N | 883.00 | 3684.00 | 1812.50 | 966.50 | 48.00 | 99.00 | 75.50 | 21.92 | 0.95 | 25.13 | 8.25 | 8.27 |
总计Total | 8.00 | 5858.00 | 1359.28 | 950.42 | 1.00 | 100.00 | 76.16 | 22.53 | 0.86 | 96.70 | 12.18 | 9.44 |
图3 各变量之间的相关系数情况***表示极显著相关(P<0.001),下同。 *** indicate extremely significant correlation(P<0.001), the same below. Biomass: 草地生物量 Aboveground biomass, AGB; NDPI: 归一化物候指数Normalized difference phenology index; prec_04: 4月降水量 Precipitation in April; prec_05: 5月降水量Precipitation in May; prec_06: 6月降水量 Precipitation in June; prec_08: 8月降水量 Precipitation in August; prec_12: 12月降水量 Precipitation in December; tmax_01: 1月最高温度 The highest temperature in January; tp_12: 12月平均温度 Average temperature in December.
Fig.3 Correlation coefficient among the variables
图4 筛选前后变量的模型运行情况bam:基于样条函数的广义加性模型 Generalized additive model using splines;brnn:贝叶斯规整化神经网络 Bayesian regularized neural networks;bstTree:提升树 Boosted tree;enet:弹性网络 Elasticnet;gaussprRadial:基于径向基函数核的高斯过程 Gaussian process with radial basis function kernel;glmnet:广义线性模型网 Generalized linear model net;kknn:k-临近算法 k-nearest neighbors;leapForward:基于正向选择的线性回归 Linear regression with forward selection;monmlp:单调多层感知机神经网络 Monotone multi-layer perceptron neural network;nnls:非负最小二乘 Non-negative least squares;ridge:岭回归 Ridge regression;svmRadialSigma:基于径向基函数核的支持向量机 Support vector machines with radial basis function kernel;spls:稀疏偏最小二乘 Sparse partial least squares;cforest:条件推断随机森林 Conditional inference random forest;parRF:平行随机森林 Parallel random forest;qrf:分位数随机森林 Quantile random forest;ranger:基于ranger的随机森林 Random forest;Rborist:基于Rborist的随机森林 Random forest;rf:基于randomForest的随机森林 Random forest;RRF:基于randomForest和RRF的正则化随机森林 Regularized random forest;RRFglobal:基于RRF的正则化随机森林 Regularized random forest;xgbDART:极端的梯度提升1 Extreme gradient boosting 1;xgbTree:极端的梯度提升2 Extreme gradient boosting 2. RMSE:均方根差Root mean square error, R2:决定系数Determination coefficient, MAE:平均绝对误差Mean absolute error.
Fig.4 Filter variables before and after the model runs
方法Method | 筛选变量前的参数Parameters of variables before filtering | 筛选变量后的参数Parameters of variables after filtering |
---|---|---|
Rborist | predFixed=34 and minNode=3 | predFixed=2 and minNode=3 |
RRF | mtry=34, coefReg=0.505 and coefImp=0.5 | mtry=2, coefReg=1 and coefImp=0.5 |
ranger | mtry=34, splitrule=variance and min.node.size=5 | mtry=2, splitrule=variance and min.node.size=5 |
RRFglobal | mtry=67 and coefReg=0.505 | mtry=2 and coefReg=0.01 |
parRF | mtry=34 | mtry=2 |
rf | mtry=34 | mtry=2 |
qrf | mtry=34 | mtry=2 |
kknn | kmax=9, distance=2 and kernel=optimal | kmax=9, distance=2 and kernel=optimal |
cforest | mtry=67 | mtry=8 |
xgbTree | nrounds=150, max_depth=3, eta=0.3, gamma=0, colsample_bytree=0.6, min_child_weight=1 and subsample=1 | nrounds=150, max_depth=3, eta=0.4, gamma=0, colsample_bytree=0.8, min_child_weight=1 and subsample=1 |
xgbDART | nrounds=150, max_depth=3, eta=0.3, gamma=0, subsample=1, colsample_bytree= 0.8, rate_drop=0.5, skip_drop=0.95 and min_child_weight=1 | nrounds=150, max_depth=3, eta=0.3, gamma=0, subsample=1, colsample_bytree= 0.8, rate_drop=0.5, skip_drop=0.95 and min_child_weight=1 |
bstTree | mstop=150, maxdepth=3 and nu=0.1 | mstop=150, maxdepth=3 and nu=0.1 |
svmRadialSigma | sigma=0.03387348 and C=1 | sigma=0.2790061 and C=1 |
gaussprRadial | - | - |
monmlp | hidden1=5 and n.ensemble=1 | hidden1=5 and n.ensemble=1 |
brnn | neurons=3 | neurons=3 |
bam | select=TRUE and method=GCV.Cp | select=FALSE and method=GCV.Cp |
spls | K=24, eta=0.5 and kappa=0.5 | K=7, eta=0.9 and kappa=0.5 |
glmnet | alpha=0.1 and lambda=1.112425 | alpha=1 and lambda=1.112425 |
enet | fraction=0.525 and lambda=0.0001 | fraction=1 and lambda=0.0001 |
ridge | lambda=0.0001 | lambda=0.0001 |
leapForward | nvmax=4 | nvmax=4 |
nnls | - | - |
表3 各机器学习方法参数取值情况
Table 3 Parameters of different machine learning methods
方法Method | 筛选变量前的参数Parameters of variables before filtering | 筛选变量后的参数Parameters of variables after filtering |
---|---|---|
Rborist | predFixed=34 and minNode=3 | predFixed=2 and minNode=3 |
RRF | mtry=34, coefReg=0.505 and coefImp=0.5 | mtry=2, coefReg=1 and coefImp=0.5 |
ranger | mtry=34, splitrule=variance and min.node.size=5 | mtry=2, splitrule=variance and min.node.size=5 |
RRFglobal | mtry=67 and coefReg=0.505 | mtry=2 and coefReg=0.01 |
parRF | mtry=34 | mtry=2 |
rf | mtry=34 | mtry=2 |
qrf | mtry=34 | mtry=2 |
kknn | kmax=9, distance=2 and kernel=optimal | kmax=9, distance=2 and kernel=optimal |
cforest | mtry=67 | mtry=8 |
xgbTree | nrounds=150, max_depth=3, eta=0.3, gamma=0, colsample_bytree=0.6, min_child_weight=1 and subsample=1 | nrounds=150, max_depth=3, eta=0.4, gamma=0, colsample_bytree=0.8, min_child_weight=1 and subsample=1 |
xgbDART | nrounds=150, max_depth=3, eta=0.3, gamma=0, subsample=1, colsample_bytree= 0.8, rate_drop=0.5, skip_drop=0.95 and min_child_weight=1 | nrounds=150, max_depth=3, eta=0.3, gamma=0, subsample=1, colsample_bytree= 0.8, rate_drop=0.5, skip_drop=0.95 and min_child_weight=1 |
bstTree | mstop=150, maxdepth=3 and nu=0.1 | mstop=150, maxdepth=3 and nu=0.1 |
svmRadialSigma | sigma=0.03387348 and C=1 | sigma=0.2790061 and C=1 |
gaussprRadial | - | - |
monmlp | hidden1=5 and n.ensemble=1 | hidden1=5 and n.ensemble=1 |
brnn | neurons=3 | neurons=3 |
bam | select=TRUE and method=GCV.Cp | select=FALSE and method=GCV.Cp |
spls | K=24, eta=0.5 and kappa=0.5 | K=7, eta=0.9 and kappa=0.5 |
glmnet | alpha=0.1 and lambda=1.112425 | alpha=1 and lambda=1.112425 |
enet | fraction=0.525 and lambda=0.0001 | fraction=1 and lambda=0.0001 |
ridge | lambda=0.0001 | lambda=0.0001 |
leapForward | nvmax=4 | nvmax=4 |
nnls | - | - |
图7 2000-2020年青藏高原草地地上生物量持续性变化空间分布
Fig.7 Spatial distribution of grassland above-ground biomass on the Tibetan Plateau from 2000-2020 based on trend and Hurst index
行政区划 Administrative divisions | 草地类型 Grassland type | 变化趋势不稳定Trend instability | 持续性轻微降低 Sustained insignificant decrease | 持续性明显降低 Sustained significant decrease | 持续性稳定不变 Continuous stability | 持续性轻微增加 Sustained insignificant increase | 持续性明显增加Sustained significant increase |
---|---|---|---|---|---|---|---|
甘肃省 Gansu Province | A | 68.10 | 0.46 | 0.04 | 10.30 | 2.49 | 18.61 |
C | 51.06 | 3.61 | 0.97 | 7.99 | 25.00 | 11.38 | |
D | 22.07 | 0.00 | 0.00 | 22.90 | 18.67 | 36.36 | |
F | 31.64 | 0.00 | 0.00 | 21.55 | 25.03 | 21.78 | |
K | 53.02 | 2.22 | 0.42 | 4.42 | 21.12 | 18.79 | |
M | 40.54 | 0.00 | 0.00 | 21.06 | 11.41 | 26.98 | |
O | 30.68 | 0.00 | 0.00 | 13.57 | 12.62 | 43.13 | |
P | 43.37 | 0.00 | 0.00 | 22.06 | 6.57 | 27.99 | |
Q | 61.59 | 6.54 | 0.37 | 7.40 | 19.11 | 4.99 | |
小计Total | 43.70 | 1.99 | 0.48 | 11.92 | 20.71 | 21.20 | |
青海省 Qinghai Province | A | 65.06 | 1.04 | 0.88 | 14.37 | 6.96 | 11.68 |
B | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | |
C | 53.26 | 6.01 | 1.45 | 13.94 | 18.42 | 6.92 | |
D | 40.10 | 2.72 | 1.55 | 22.69 | 18.60 | 14.34 | |
E | 26.20 | 0.00 | 0.00 | 19.38 | 30.78 | 23.64 | |
F | 49.28 | 0.01 | 0.00 | 21.65 | 19.30 | 9.76 | |
G | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | |
K | 55.03 | 7.53 | 1.67 | 7.44 | 18.08 | 10.26 | |
M | 73.30 | 0.00 | 0.00 | 3.28 | 3.28 | 20.13 | |
N | 50.82 | 0.46 | 0.28 | 8.67 | 13.04 | 26.73 | |
P | 55.90 | 0.44 | 0.37 | 25.97 | 6.72 | 10.60 | |
Q | 24.96 | 1.05 | 1.59 | 13.42 | 23.16 | 35.83 | |
小计Total | 50.72 | 4.41 | 1.31 | 16.36 | 17.11 | 10.09 | |
四川省 Sichuan Province | A | 41.74 | 2.57 | 0.98 | 2.08 | 42.35 | 10.28 |
C | 56.24 | 10.71 | 1.32 | 11.26 | 16.26 | 4.21 | |
G | 68.78 | 2.16 | 0.00 | 2.94 | 8.81 | 17.31 | |
I | 75.56 | 3.58 | 0.25 | 2.47 | 7.65 | 10.49 | |
J | 62.72 | 0.73 | 0.00 | 1.88 | 13.35 | 21.32 | |
K | 59.16 | 7.39 | 1.23 | 7.67 | 16.53 | 8.02 | |
N | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Q | 58.08 | 8.00 | 0.70 | 6.67 | 17.89 | 8.65 | |
小计Total | 57.14 | 9.66 | 1.27 | 10.10 | 16.37 | 5.45 | |
西藏自治区 Tibet Autonomous Region | A | 71.53 | 3.69 | 1.41 | 7.92 | 7.97 | 7.48 |
D | 66.97 | 2.14 | 0.84 | 25.61 | 2.10 | 2.34 | |
E | 55.60 | 0.16 | 0.05 | 38.09 | 2.02 | 4.08 | |
F | 52.75 | 0.16 | 0.09 | 39.32 | 2.06 | 5.61 | |
G | 71.46 | 10.42 | 3.97 | 5.46 | 7.94 | 0.74 | |
H | 81.37 | 7.12 | 1.71 | 6.69 | 2.57 | 0.54 | |
I | 76.61 | 4.44 | 0.00 | 4.03 | 6.85 | 8.06 | |
J | 94.64 | 2.82 | 0.47 | 1.69 | 0.38 | 0.00 | |
K | 59.33 | 15.57 | 3.79 | 14.00 | 6.50 | 0.80 | |
L | 52.50 | 23.98 | 9.44 | 12.45 | 1.46 | 0.16 | |
M | 79.63 | 0.19 | 0.00 | 10.42 | 4.39 | 5.36 | |
N | 70.30 | 10.60 | 5.02 | 8.84 | 3.58 | 1.65 | |
O | 76.71 | 0.11 | 0.00 | 7.84 | 10.58 | 4.76 | |
P | 61.43 | 0.70 | 0.34 | 26.64 | 2.96 | 7.93 | |
Q | 55.84 | 8.04 | 0.79 | 27.76 | 3.47 | 4.10 | |
小计Total | 63.61 | 2.30 | 0.89 | 27.98 | 2.30 | 2.93 | |
新疆维吾尔自治区 Xinjiang Uygur Autonomous Region | A | 57.58 | 0.18 | 0.68 | 19.66 | 2.95 | 18.95 |
C | 59.28 | 4.55 | 1.69 | 23.21 | 5.79 | 5.49 | |
D | 58.78 | 2.56 | 0.70 | 28.41 | 4.12 | 5.42 | |
E | 69.46 | 3.34 | 1.40 | 19.38 | 2.73 | 3.70 | |
F | 65.48 | 1.36 | 0.63 | 27.55 | 2.04 | 2.94 | |
K | 61.40 | 3.49 | 0.62 | 21.36 | 8.42 | 4.72 | |
M | 55.24 | 0.08 | 0.07 | 21.74 | 8.97 | 13.90 | |
N | 73.08 | 0.33 | 0.09 | 8.66 | 6.70 | 11.14 | |
O | 67.54 | 0.39 | 0.12 | 14.21 | 7.42 | 10.32 | |
P | 75.68 | 0.09 | 0.03 | 9.14 | 4.76 | 10.31 | |
小计Total | 66.31 | 1.93 | 0.72 | 20.08 | 4.32 | 6.65 | |
云南省 Yunnan Province | A | 91.82 | 6.36 | 0.00 | 0.91 | 0.91 | 0.00 |
C | 91.80 | 1.27 | 0.03 | 3.65 | 2.90 | 0.36 | |
G | 96.45 | 0.16 | 0.00 | 0.81 | 1.62 | 0.97 | |
H | 92.66 | 1.74 | 0.13 | 2.94 | 2.54 | 0.00 | |
I | 89.41 | 1.91 | 0.00 | 1.04 | 4.77 | 2.86 | |
N | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
小计Total | 91.84 | 1.31 | 0.03 | 3.38 | 2.94 | 0.50 |
表4 2000-2020年青藏高原各类草地变化特征分析
Table 4 The change characteristic analysis of different grassland from 2000-2020 on the Tibetan Plateau (%)
行政区划 Administrative divisions | 草地类型 Grassland type | 变化趋势不稳定Trend instability | 持续性轻微降低 Sustained insignificant decrease | 持续性明显降低 Sustained significant decrease | 持续性稳定不变 Continuous stability | 持续性轻微增加 Sustained insignificant increase | 持续性明显增加Sustained significant increase |
---|---|---|---|---|---|---|---|
甘肃省 Gansu Province | A | 68.10 | 0.46 | 0.04 | 10.30 | 2.49 | 18.61 |
C | 51.06 | 3.61 | 0.97 | 7.99 | 25.00 | 11.38 | |
D | 22.07 | 0.00 | 0.00 | 22.90 | 18.67 | 36.36 | |
F | 31.64 | 0.00 | 0.00 | 21.55 | 25.03 | 21.78 | |
K | 53.02 | 2.22 | 0.42 | 4.42 | 21.12 | 18.79 | |
M | 40.54 | 0.00 | 0.00 | 21.06 | 11.41 | 26.98 | |
O | 30.68 | 0.00 | 0.00 | 13.57 | 12.62 | 43.13 | |
P | 43.37 | 0.00 | 0.00 | 22.06 | 6.57 | 27.99 | |
Q | 61.59 | 6.54 | 0.37 | 7.40 | 19.11 | 4.99 | |
小计Total | 43.70 | 1.99 | 0.48 | 11.92 | 20.71 | 21.20 | |
青海省 Qinghai Province | A | 65.06 | 1.04 | 0.88 | 14.37 | 6.96 | 11.68 |
B | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | |
C | 53.26 | 6.01 | 1.45 | 13.94 | 18.42 | 6.92 | |
D | 40.10 | 2.72 | 1.55 | 22.69 | 18.60 | 14.34 | |
E | 26.20 | 0.00 | 0.00 | 19.38 | 30.78 | 23.64 | |
F | 49.28 | 0.01 | 0.00 | 21.65 | 19.30 | 9.76 | |
G | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | |
K | 55.03 | 7.53 | 1.67 | 7.44 | 18.08 | 10.26 | |
M | 73.30 | 0.00 | 0.00 | 3.28 | 3.28 | 20.13 | |
N | 50.82 | 0.46 | 0.28 | 8.67 | 13.04 | 26.73 | |
P | 55.90 | 0.44 | 0.37 | 25.97 | 6.72 | 10.60 | |
Q | 24.96 | 1.05 | 1.59 | 13.42 | 23.16 | 35.83 | |
小计Total | 50.72 | 4.41 | 1.31 | 16.36 | 17.11 | 10.09 | |
四川省 Sichuan Province | A | 41.74 | 2.57 | 0.98 | 2.08 | 42.35 | 10.28 |
C | 56.24 | 10.71 | 1.32 | 11.26 | 16.26 | 4.21 | |
G | 68.78 | 2.16 | 0.00 | 2.94 | 8.81 | 17.31 | |
I | 75.56 | 3.58 | 0.25 | 2.47 | 7.65 | 10.49 | |
J | 62.72 | 0.73 | 0.00 | 1.88 | 13.35 | 21.32 | |
K | 59.16 | 7.39 | 1.23 | 7.67 | 16.53 | 8.02 | |
N | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Q | 58.08 | 8.00 | 0.70 | 6.67 | 17.89 | 8.65 | |
小计Total | 57.14 | 9.66 | 1.27 | 10.10 | 16.37 | 5.45 | |
西藏自治区 Tibet Autonomous Region | A | 71.53 | 3.69 | 1.41 | 7.92 | 7.97 | 7.48 |
D | 66.97 | 2.14 | 0.84 | 25.61 | 2.10 | 2.34 | |
E | 55.60 | 0.16 | 0.05 | 38.09 | 2.02 | 4.08 | |
F | 52.75 | 0.16 | 0.09 | 39.32 | 2.06 | 5.61 | |
G | 71.46 | 10.42 | 3.97 | 5.46 | 7.94 | 0.74 | |
H | 81.37 | 7.12 | 1.71 | 6.69 | 2.57 | 0.54 | |
I | 76.61 | 4.44 | 0.00 | 4.03 | 6.85 | 8.06 | |
J | 94.64 | 2.82 | 0.47 | 1.69 | 0.38 | 0.00 | |
K | 59.33 | 15.57 | 3.79 | 14.00 | 6.50 | 0.80 | |
L | 52.50 | 23.98 | 9.44 | 12.45 | 1.46 | 0.16 | |
M | 79.63 | 0.19 | 0.00 | 10.42 | 4.39 | 5.36 | |
N | 70.30 | 10.60 | 5.02 | 8.84 | 3.58 | 1.65 | |
O | 76.71 | 0.11 | 0.00 | 7.84 | 10.58 | 4.76 | |
P | 61.43 | 0.70 | 0.34 | 26.64 | 2.96 | 7.93 | |
Q | 55.84 | 8.04 | 0.79 | 27.76 | 3.47 | 4.10 | |
小计Total | 63.61 | 2.30 | 0.89 | 27.98 | 2.30 | 2.93 | |
新疆维吾尔自治区 Xinjiang Uygur Autonomous Region | A | 57.58 | 0.18 | 0.68 | 19.66 | 2.95 | 18.95 |
C | 59.28 | 4.55 | 1.69 | 23.21 | 5.79 | 5.49 | |
D | 58.78 | 2.56 | 0.70 | 28.41 | 4.12 | 5.42 | |
E | 69.46 | 3.34 | 1.40 | 19.38 | 2.73 | 3.70 | |
F | 65.48 | 1.36 | 0.63 | 27.55 | 2.04 | 2.94 | |
K | 61.40 | 3.49 | 0.62 | 21.36 | 8.42 | 4.72 | |
M | 55.24 | 0.08 | 0.07 | 21.74 | 8.97 | 13.90 | |
N | 73.08 | 0.33 | 0.09 | 8.66 | 6.70 | 11.14 | |
O | 67.54 | 0.39 | 0.12 | 14.21 | 7.42 | 10.32 | |
P | 75.68 | 0.09 | 0.03 | 9.14 | 4.76 | 10.31 | |
小计Total | 66.31 | 1.93 | 0.72 | 20.08 | 4.32 | 6.65 | |
云南省 Yunnan Province | A | 91.82 | 6.36 | 0.00 | 0.91 | 0.91 | 0.00 |
C | 91.80 | 1.27 | 0.03 | 3.65 | 2.90 | 0.36 | |
G | 96.45 | 0.16 | 0.00 | 0.81 | 1.62 | 0.97 | |
H | 92.66 | 1.74 | 0.13 | 2.94 | 2.54 | 0.00 | |
I | 89.41 | 1.91 | 0.00 | 1.04 | 4.77 | 2.86 | |
N | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
小计Total | 91.84 | 1.31 | 0.03 | 3.38 | 2.94 | 0.50 |
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