基于Google Earth Engine和作物模型快速评估低温冷害对大豆生产的影响

作为中国商品粮的主要生产基地,东北地区频发的低温冷害给中国粮食安全带来了严重的影响,及时、准确和大范围评估低温冷害灾损是降低损失、快速恢复生产的重要前提。本文以鄂伦春为例,提出了一种快速评估低温冷害对大豆生产影响的新方法。首先诊断出该地区典型冷害事件发生的年份为1989年、1995年、2003年、2009年和2018年;然后基于本地化后CROPGRO-Soybean模型设置512组低温冷害和田间管理组合模拟情景;其次构建了1600组包括3个变量（有效积温距平值（CDD）、模拟的叶面积植被指数（LAI）和产量）的冷害脆弱性模型;最后依托Google Earth Engine（GEE）平台逐像元提取大豆关键生育期早晚窗口内最大的宽动态植被指数（WDRVI）及对应的日期（DOY）,将WDRVI转化为大豆种植格点的实际LAI,结合构建的冷害脆弱性模型逐像元计算出产量和减产率。主要发现如下：① 校准后的CROPGRO-Soybean模型能较为准确地模拟不同冷害情景下的大豆生长发育过程;② 大豆遭受全生育期的降温情景（1~3 ℃）的减产幅度比局部降温情景（4个生育期随机生成连续5日温度为0 ℃）的减产幅度大;③ 历史冷害年1989年、1995年、2003年、2009年平均减产率约为9.6%、29.8%、50.5%和15.7%,与实际6.4%、39.2%、47.7%和13.2%的减产率相比,冷害灾损评估结果具有较好的精度且误差均在一倍方差以内;④ 运用该方法评估2018年冷害田间尺度的产量损失,并利用Sentinel-2A影像进行10 m高精度制图。结果显示,该方法能够快速、准确地评估不同尺度的低温冷害灾损情况,为作物估产以及农作物灾害损失评估的业务化运行提供了新的思路。     

基于GEE的中国湖泊浮游植物生物量时空动态分析

随着全球变暖和社会经济发展,中国湖泊富营养化情况时有发生,迫切需要对中国湖泊的浮游植物生物量进行有效监测.本文选择了中国756个面积超过10km2的湖泊进行研究,基于Google Earth Engine(GEE)云端运算平台,反演2003-2018年间叶绿素a(chl-a)浓度数据,以此来分析研究各个湖泊的营养状态及...     

Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine

Quantitative evaluation and driving mechanism analysis of vegetation dynamics are essential for promoting regional sustainable development. In the past 20 years, the ecological environment in Altay Prefecture has changed significantly due to global warming. Meanwhile, with increasing human activities, the spatiotemporal pattern and driving forces of vegetation variation in the area are uncertain and difficult to accurately assess. Hence, we quantified the vegetation growth by using the Normalized Difference Vegetation Index (NDVI) on the Google Earth Engine (GEE). Then, the spatiotemporal patterns of vegetation from 2000 to 2019 were analyzed at the pixel scale. Finally, significance threshold segmentation was performed using meteorological data based on the correlation analysis results, and the contributions of climate change and human activities to vegetation variation were quantified. The results demonstrated that the vegetation coverage in Altay Prefecture is mainly concentrated in the north. The vegetation areas representing significant restoration and degradation from 2000 to 2019 accounted for 24.08% and 1.24% of Altay Prefecture, respectively. Moreover, spatial correlation analysis showed that the areas with significant correlations between NDVI and temperature, precipitation and sunlight hours accounted for 3.3%, 6.9% and 20.3% of Altay Prefecture, respectively. In the significant restoration area, 18.94% was dominated by multiple factors, while 3.4% was dominated by human activities, and 1.74% was dominated by climate change. Within the significant degradation area, abnormal degradation and climate change controlled 1.07% and 0.17%, respectively. This study revealed the dynamic changes of vegetation and their driving mechanisms in Altay Prefecture, and can provide scientific support for further research on life community mechanism theory and key remediation technology of mountain-water-forest-farmland-lake-grass in Altay Prefecture.     

Analysis of ecological quality in Lhasa Metropolitan Area during 1990–2017 based on remote sensing and Google Earth Engine platform

Based on a total of 519 images,the composite images with the lowest possible cloud cover were generated at pixel level with image synthesis method on Google Earth En-gine(GEE)platform.The Remote Sensing Ecological Index(RSEI)was adopted,and calcu-lated in an efficient way with the assistance of parallel cloud computing of the GEE platform.The RSEI was used in this paper to evaluate and monitor the eco-environmental quality of the Lhasa Metropolitan Area.Results show that:(1)The ecological quality is better in the west than in the east of Lhasa Metropolitan Area,with Lhasa as an approximate dividing point.The ecological quality improved and then deteriorated dramatically before 2000,with the mean RSEI value dropping from 0.51 to 0.46;the trend was followed by a gradual increase up until 2017,with the mean RSEI value increased from 0.46 to 0.55.(2)The RSEI is weakly and positively correlated with socioeconomic indicators.This indicates that the population growth and economic development did not negatively influence the ecological quality,but actually boosted it.(3)The GEE can serve as an efficient computing platform for the assessment and monitoring of eco-environmental quality in vast regions.     

1986—2019年黄河流域地表水体动态变化及其影响因素

黄河流域水资源严重短缺,对地表水面积（SWA）开展动态监测有助于明晰地表水资源时空变化规律及其驱动机制。本文基于Google Earth Engine云平台技术,综合利用混合指数规则集、线性斜率、多元线性回归和偏微分分解等方法,揭示了黄河流域SWA的年际变化及其空间分异规律,厘定了降雨、温度、植被叶面积指数、前一年SWA和水利水保措施与人类用水活动等其他因素对SWA的影响量和相对影响率。结果表明：① 地表水体总体识别精度为97%。1986—2019年全流域永久性SWA年际增长速率49.82 km²/a,其中主河道区贡献83.2%,且2001年为SWA变化由减小到增加的转折点;季节性SWA年际减小速率-79.2 km²/a,其中子流域区贡献61.8%。② 除红碱淖SWA呈显著持续减小外,其他主要天然湖泊SWA均较为稳定;6个主河道大型水库中,小浪底和龙羊峡水库SWA增加趋势最为显著;在86个子流域中,50个子流域SWA呈增加趋势,主要分布于流域中下游。③ 非气象要素对SWA的影响均大于气象要素影响作用。降雨对SWA的增加作用最小,温度上升造成中游地区SWA减小,但却导致源区SWA增加。植被叶面积指数增加导致主河道区和子流域区SWA变化斜率分别增加10.12 km²/a和7.26 km²/a。其他因素对子流域区SWA增加呈负作用,这表明子流域内剧烈用水活动对SWA的减小作用大于水利水保措施对SWA的增加作用,但是分布于主河道中的大型梯级水库调蓄功能可显著提升其对主河道区SWA的增加作用。     

缺测站干旱流域生态输水遥感监测与农业节水效益分析

生态输水与农业节水是实现内陆干旱流域可持续发展的重要手段,连续水文观测资料的缺乏制约了生态输水与农业节水效益评价。为此,以中国甘肃敦煌疏勒河流域下游为例,基于遥感水文站与谷歌地球引擎进行2016—2020年月尺度的生态输水遥感监测,在此基础上结合蒸散发和土地覆盖类型等多源遥感数据评价生态输水与农业节水效益,分析两者之间在水资源方面的平衡关系。结果表明：（1） 遥感水文站与谷歌地球引擎（Google Earth Engine, GEE）能够为生态输水遥感监测与农业节水效益评价提供可靠的数据支撑。（2） 2017—2020年生态输水能够为下游湿地与河道平均每年提供2.50×10⁸ m³生态用水,其中30.06%的水量到达下游湿地,18.47%的水量被下游河道周边的植被所利用,且使下游河道周边植被面积增加112.25 km²。（3） 农业节水在保持耕地面积维持上升趋势的前提下,2017—2020年平均每年降低耕地的蒸散发量0.395×10⁸ m³;耕地蒸散发减少量平均占生态输水量的14.22%,农业节水有效缓解了内陆干旱流域农业用水挤占生态用水的问题。本文将为内陆干旱缺测站流域的生态输水遥感监测与农业节水效益评价提供新的思路,以期为未来的生态输水与农业节水工程的实施提供理论支撑。     

Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM)

In the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty.     

Assessment and determinants of per capita household CO<Subscript>2</Subscript> emissions (PHCEs) based on capital city level in China

Household CO₂ emissions were increasing due to rapid economic growth and different household lifestyle. We assessed per capita household CO₂ emissions (PHCEs) based on different household consuming demands (including clothing, food, residence, transportation and service) by using provincial capital city level survey data in China. The results showed that: (1) there was a declining trend moving from eastward to westward as well as moving from northward to southward in the distribution of PHCEs. (2) PHCEs from residence demand were the largest which accounted for 44% of the total. (3) Correlation analysis and spatial analysis (Spatial Lag Model (SLM) and Spatial Error Model (SEM)) were used to evaluate the complex determinants of PHCEs. Per capita income (PI) and household size (HS) were analyzed as the key influencing factors. We concluded that PHCEs would increase by 0.2951% and decrease by 0.5114% for every 1% increase in PI and HS, respectively. According to the results, policy-makers should consider household consuming demand, income disparity and household size on the variations of PHCEs. The urgency was to improve technology and change household consuming lifestyle to reduce PHCEs.