天山北坡植被覆盖近30年时空变化及预测分析

doi:10.13292/j.1000-4890.202212.019

生态学杂志 ›› 2022, Vol. 41 ›› Issue (12): 2414-2423.doi: 10.13292/j.1000-4890.202212.019

天山北坡植被覆盖近30年时空变化及预测分析

李红梅¹,巴贺贾依娜尔·铁木尔别克²,常顺利^1*,张毓涛^3,4

（¹新疆大学生态与环境学院，乌鲁木齐 830017； ²新疆大学地理与遥感科学学院，乌鲁木齐 830017； ³新疆林业科学院森林生态研究所，乌鲁木齐 830063； ⁴新疆天山森林生态系统国家定位观测研究站，乌鲁木齐 830063）

出版日期:2022-12-10 发布日期:2022-12-20

Spatial-temporal variations in the past 30 years and prediction analysis of vegetation coverage in the northern slope of Tianshan Mountain.

LI Hong-mei¹, BAHEJIAYINAER Tiemuerbieke², CHANG Shun-li^1*, ZHANG Yu-tao^3,4#br#

#br#

(¹College of Ecology and Environment, Xinjiang University, Urumqi 830017, China; ²College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China; ³Institute of Forest Ecology, Xinjiang Academy of Forestry, Urumqi 830063, China; ⁴Xinjiang Tianshan Forest Ecosystem National Positioning Observation Research Station, Urumqi 830063, China).

Online:2022-12-10 Published:2022-12-20

摘要/Abstract

摘要： 天山北坡位于丝绸之路经济带上，人类活动较为频繁，并且存在较多的生态脆弱区，通过探究该区域的植被覆盖度时空变化，对于区域绿色发展具有重要的意义。基于Google Earth Engine(GEE)云平台，利用1990、2000、2010和2020年7—8月份的Landsat影像数据，计算归一化植被指数（NDVI）和植被覆盖度，运用Moran I对植被覆盖度的空间集聚状况进行分析，最后利用灰色预测GM(1,1)模型对高植被覆盖度变化趋势进行预测。结果表明：（1）1990—2020年天山北坡植被覆盖度整体以低覆盖度为主，2020年植被覆盖度较1990年总体呈增加趋势；（2）空间自相关分析结果显示，天山北坡植被覆盖度整体表现为显著的正空间自相关（P<0.01），即研究区植被覆盖度呈聚集状态；不同年份局部空间自相关分析结果略有差异，主要体现在研究区中部高高聚集现象增加，南缘低低聚集现象增加；（3）灰色预测GM(1,1)模型结果发现：2020—2040年，研究区年均气温升高、年降水量下降，高植被覆盖度面积呈增加趋势；（4）GEE云平台在处理大范围、长时序影像数据时展现出高效率，可以作为植被覆盖度常态化监测的有效工具。

关键词: Landsat, GEE, 灰色预测模型, 空间自相关, 天山

Abstract: The north slope of Tianshan Mountain is located on the Silk Road Economic Belt, with frequent human activities and many ecologically fragile areas. Exploring the temporal and spatial variations of vegetation coverage in this area is of a great significance for regional green development. Based on Google Earth Engine (GEE) cloud platform, the normalized difference vegetation index (NDVI) and vegetation coverage were calculated by using Landsat Image data from July to August of 1990, 2000, 2010 and 2020. The spatial agglomeration of vegetation coverage was analyzed by Moran’s I index, and the changes of high vegetation coverage were predicted by Grey Prediction GM (1,1) model. The results showed that: (1) Vegetation coverage on the north slope of Tianshan Mountain was generally low from 1990 to 2020, and that in 2020 showed an increasing trend compared with 1990. (2) According to the results of spatial autocorrelation analysis, the overall vegetation coverage on the north slope of Tianshan Mountain showed significant positive spatial autocorrelation (P<0.01), with an aggregation state. The results of local spatial autocorrelation analysis in different years were slightly different, which was mainly reflected in the increase of high-high aggregation in the middle part of the study area and low-low aggregation in the south edge of the study area. (3) The results of Grey Prediction GM (1,1) model showed that the average annual temperature in the study area would gradually increase from 2020 to 2040, the annual precipitation would decrease, and the area with high vegetation coverage showed an increasing trend. (4) The high efficiency of GEE cloud platform in processinglarge-scale and long-time series image data makes it an effective tool for vegetation coverage normalization monitoring.

Key words: Landsat, Google Earth Engine (GEE), grey prediction model, spatial autocorrelation, Tianshan Mountain.

李红梅, 巴贺贾依娜尔·铁木尔别克, 常顺利, 张毓涛. 天山北坡植被覆盖近30年时空变化及预测分析[J]. 生态学杂志, 2022, 41(12): 2414-2423.

LI Hong-mei, BAHEJIAYINAER Tiemuerbieke, CHANG Shun-li, ZHANG Yu-tao. Spatial-temporal variations in the past 30 years and prediction analysis of vegetation coverage in the northern slope of Tianshan Mountain.[J]. Chinese Journal of Ecology, 2022, 41(12): 2414-2423.

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天山北坡植被覆盖近30年时空变化及预测分析

Spatial-temporal variations in the past 30 years and prediction analysis of vegetation coverage in the northern slope of Tianshan Mountain.

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