草业学报 ›› 2023, Vol. 32 ›› Issue (8): 28-39.DOI: 10.11686/cyxb2022385
李芳1(), 王广军1(), 杜海波2, 李萌1, 梁四海3, 彭红明4,5
收稿日期:
2022-09-27
修回日期:
2022-12-09
出版日期:
2023-08-20
发布日期:
2023-06-16
通讯作者:
王广军
作者简介:
E-mail: cugbwgj@163.com基金资助:
Fang LI1(), Guang-jun WANG1(), Hai-bo DU2, Meng LI1, Si-hai LIANG3, Hong-ming PENG4,5
Received:
2022-09-27
Revised:
2022-12-09
Online:
2023-08-20
Published:
2023-06-16
Contact:
Guang-jun WANG
摘要:
归一化植被指数(NDVI)能够较准确表达出植被覆盖和生长状况,对其进行时间序列分析已成为研究全球、国家或区域植被生长的重要方式。针对当前NDVI时序产品空间分辨率不高,难以应用于小尺度的精细研究,以及利用Landsat不同时相NDVI评估生态环境质量受植被季相和年际变化影响较大等问题,首先基于增强型时空自适应反射率融合模型(ESTARFM)融合MOD09Q1和Landsat数据,对植被年内生长季NDVI数据进行预测插补,之后利用Logistic模型重构2001-2020年植被生长季NDVI曲线,通过引入MODIS逐日NDVI数据确定NDVI年内最大值日期,逐像素求解出最优的Landsat NDVI年内最大值,并将其应用于青海湖流域布哈河附近局部典型区域植被生长状况评估。结果表明:1)融合MODIS和Landsat数据的Landsat NDVI年内最大值求解结果在3倍中误差以内的占98.5%,求解结果具有较高的精度;2)利用Landsat NDVI年内最大值进行植被生长状况评估,能弱化Landsat数据因时相差异引起的误差;3)研究区植被NDVI年内最大值呈南北高中间低的空间分布特点,年际变化整体先降低再增加,植被生长状况呈向好趋势;高寒嵩草、杂类草草甸NDVI年内最大值呈减少趋势且波动剧烈,应是青海湖流域监测的重点植被类型。
李芳, 王广军, 杜海波, 李萌, 梁四海, 彭红明. 融合MODIS和Landsat数据的青海湖流域典型区NDVI重构与年内最大值变化分析[J]. 草业学报, 2023, 32(8): 28-39.
Fang LI, Guang-jun WANG, Hai-bo DU, Meng LI, Si-hai LIANG, Hong-ming PENG. Integrating MODIS and Landsat data to reconstruct the Landsat NDVI of a typical region in the Qinghai Lake Basin and changes in the intra-annual NDVI maximum[J]. Acta Prataculturae Sinica, 2023, 32(8): 28-39.
年份 Year | 参与融合的左右影像(MODIS日) Left and right images involved in fusion (MODIS day) | 预测目标Landsat影像日期 Predict the target Landsat image data |
---|---|---|
2001 | 152(06-02), 169(06-18) | 06-10 |
185(07-04), 233(08-21) | 08-02 | |
240(08-28), 281(10-08) | 09-04 | |
2005 | 164(06-13), 180(06-29) | 06-25 |
196(07-15), 235(08-23) | 07-21 | |
251(09-08), 260(09-17) | 09-13 | |
2009 | 175(06-24), 198(07-17) | 06-28 |
207(07-26), 223(08-11) | 08-05 | |
239(08-27), 271(09-28) | 09-22 | |
2011 | 165(06-14), 188(07-07) | 06-30 |
197(07-16), 213(08-01) | 07-24 | |
220(08-08), 236(08-24) | 08-16 | |
252(09-09), 277(10-04) | 09-25 | |
2014 | 157(06-06), 196(07-15) | 07-04 |
205(07-24), 260(09-17) | 07-31 | |
2016 | 186(07-04), 202(07-20) | 07-15 |
211(07-29), 250(09-06) | 08-07 | |
259(09-15), 275(10-01) | 09-20 | |
2019 | 162(06-11), 203(07-22) | 07-05 |
226(08-14), 258(09-15) | 08-30 | |
274(10-01), 290(10-17) | 10-09 | |
2020 | 181(06-29), 222(08-09) | 07-25 |
238(08-25), 254(09-10) | 09-04 | |
261(09-17), 277(10-03) | 09-23 |
表1 参考影像和预测影像信息
Table 1 Reference image and prediction image information
年份 Year | 参与融合的左右影像(MODIS日) Left and right images involved in fusion (MODIS day) | 预测目标Landsat影像日期 Predict the target Landsat image data |
---|---|---|
2001 | 152(06-02), 169(06-18) | 06-10 |
185(07-04), 233(08-21) | 08-02 | |
240(08-28), 281(10-08) | 09-04 | |
2005 | 164(06-13), 180(06-29) | 06-25 |
196(07-15), 235(08-23) | 07-21 | |
251(09-08), 260(09-17) | 09-13 | |
2009 | 175(06-24), 198(07-17) | 06-28 |
207(07-26), 223(08-11) | 08-05 | |
239(08-27), 271(09-28) | 09-22 | |
2011 | 165(06-14), 188(07-07) | 06-30 |
197(07-16), 213(08-01) | 07-24 | |
220(08-08), 236(08-24) | 08-16 | |
252(09-09), 277(10-04) | 09-25 | |
2014 | 157(06-06), 196(07-15) | 07-04 |
205(07-24), 260(09-17) | 07-31 | |
2016 | 186(07-04), 202(07-20) | 07-15 |
211(07-29), 250(09-06) | 08-07 | |
259(09-15), 275(10-01) | 09-20 | |
2019 | 162(06-11), 203(07-22) | 07-05 |
226(08-14), 258(09-15) | 08-30 | |
274(10-01), 290(10-17) | 10-09 | |
2020 | 181(06-29), 222(08-09) | 07-25 |
238(08-25), 254(09-10) | 09-04 | |
261(09-17), 277(10-03) | 09-23 |
图5 研究区数字高程模型(DEM)和2001-2020年Landsat NDVI年内最大值空间分布
Fig.5 DEM and spatial distribution of Landsat intra-annual NDVI maximum in the study area from 2001 to 2020
图7 2001-2020年NDVI年内最大值变化趋势和变异系数空间分布
Fig.7 Spatial distribution of the maximum value change trend and coefficient of variation of NDVI from 2001 to 2020
斜率Slope | 变化趋势等级Grade of variation trend | 占总面积百分比Percentage of total area (%) |
---|---|---|
Slope≤-0.0005 | 显著减少Significant decrease | |
-0.0005<Slope≤-0.0002 | 轻度减少Insignificant decrease | |
-0.0002<Slope≤0.0002 | 基本稳定No change | 26.97 |
0.0002<Slope≤0.0005 | 轻度增加Insignificant increase | 36.67 |
Slope>0.0005 | 显著增加Significant increase | 28.33 |
表2 研究区变化趋势分级及面积比例
Table 2 Grading of change trend and area proportion in the study area
斜率Slope | 变化趋势等级Grade of variation trend | 占总面积百分比Percentage of total area (%) |
---|---|---|
Slope≤-0.0005 | 显著减少Significant decrease | |
-0.0005<Slope≤-0.0002 | 轻度减少Insignificant decrease | |
-0.0002<Slope≤0.0002 | 基本稳定No change | 26.97 |
0.0002<Slope≤0.0005 | 轻度增加Insignificant increase | 36.67 |
Slope>0.0005 | 显著增加Significant increase | 28.33 |
变异系数CV | 变异程度 Degree of variation | 占总面积百分比Percentage of total area (%) |
---|---|---|
0<CV≤0.1 | 非常稳定High stabilization | |
0.1<CV≤0.2 | 稳定Stabilization | 53.42 |
0.2<CV≤0.3 | 波动Fluctuation | 32.70 |
CV>0.3 | 剧烈波动High fluctuation |
表3 研究区变异系数分级及面积占比
Table 3 Coefficient of variation (CV) classification and area proportion in the study area
变异系数CV | 变异程度 Degree of variation | 占总面积百分比Percentage of total area (%) |
---|---|---|
0<CV≤0.1 | 非常稳定High stabilization | |
0.1<CV≤0.2 | 稳定Stabilization | 53.42 |
0.2<CV≤0.3 | 波动Fluctuation | 32.70 |
CV>0.3 | 剧烈波动High fluctuation |
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