废弃矿区环境恢复状况遥感监测研究

废弃矿区环境恢复监测对合理开采矿区具有重要的指导意义.以双鸭山废弃矿区为例,基于该区域2000年和2009年的MODIS数据,利用NDVI对两期的植被覆盖度变化进行了统计分析,总结了这两期的植被覆盖在时间和空间上的变化特征.采用分类方法,对比分析废弃矿区土地利用在时间序列上的变化情况.结果表明,双鸭山市废弃矿区植被覆盖度在时间特征上有了一定的提高.为废弃矿区的环境调查与生态恢复模式的研究提供了技术支撑.     

城市异质植被的覆盖度估算模型比较研究

植被覆盖度是城市生态环境评价的一个重要指标。针对亚热带城市异质植被覆盖特征,选择像元尺度的植被指数(NDVI)转换模型、亚像元尺度的植被—土壤两端元模型(V-S Model)和植被—高—低反射率三端元模型(V-H-L Model)在TM影像上估算植被覆盖度,并结合野外实地调查对比验证3种模型的估算精度及其适用性。结果表明模型尺度和背景亮度对植被覆盖度估算有着不同程度的影响。NDVI转换模型整体高估覆盖度为27%,V-S模型和V-H-L模型整体低估覆盖度分别为23%和5%;验证结果证明:NDVI转换模型对高密度(60%)植被的估算结果最好,低估4%;V-H-L模型对中密度(40%~60%)和低密度(40%)植被的估算结果最优,仅低估2%,并受背景亮度的影响最小。因此,NDVI转换模型适用于高密度植被覆盖度的估算,亚像元尺度下的V-S模型和V-H-L模型适用于低、中密度植被覆盖度的估算,并以V-H-L模型估算较为准确。     

Analysis of monotonic greening and browning trends from global NDVI time-series

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.     

基于CASA-WOFOST耦合模型的大豆单产遥感估算研究

中国是一个农业大国,在田块甚至是亚田块尺度上进行快速、准确的作物产量估算,不仅可以对农民田间管理进行指导,对于农田生态系统对全球变化的响应评价、制定科学合理的粮食政策、对外粮食贸易和国家粮食安全都具有重要意义。目前主流的估产模型主要有经验统计模型、光能利用率模型、作物生长模型等,每一类模型在各自研究领域相对完整,但是都形成了固定的局限性,为了研究利用遥感技术在小区域范围内田块尺度的作物估产,选取黑龙江省双山农场为研究区,以大豆为研究对象,基于CASA-WOFOST耦合估产模式,利用覆盖作物生长季的时间序列HJ-1A/B遥感影像数据构建高时间分辨率归一化植被指数(Normalized Difference Vegetation Index, NDVI),实现逐日连续监测,分别利用CASA模型和CASA-WOFOST耦合模型对作物进行单产模拟,结果表明：耦合得到的新模型能够具有光能利用率模型较高的运行速度,同时还能发挥作物生长模型模型的机理优势,克服CASA模型在小区域田块尺度上应用的局限性。大豆单产模拟线性回归判定系数(R²)由0.668 53上升到0.844 72,均方根误差(RMSE) 由51.41 kg/hm²下降到29.52 kg/hm²,说明耦合后的模型可以综合考虑光能利用与作物生长生态生理全过程,从而提高作物估产的精度、可靠性和稳定性,为区域田块尺度作物估产提供理论支持,更好地服务于精准农业发展。     

基于地表温度和植被指数的黑龙江省旱情遥感动态监测

干旱灾害是一种影响范围广、时间跨度大的自然灾害,对社会经济发展尤其是给农业生产造成重大损失。遥感技术具有观测空间尺度大、实时性强的特点,有利于大范围、实时、动态的旱情监测。利用MODIS的LST和NDVI遥感数据,依托站点实测土壤含水率数据,分析土壤含水率与地表温度和植被指数之间的关系,考虑土壤类型,构建温度植被多项式模型,进行表层土壤水分反演,基于此开展2018年3～7月黑龙江省旱情遥感监测应用研究。监测结果显示,监测期内黑龙江省经历了两次规模较大的干旱过程,干旱的核心区域为黑龙江省西南部区域,与实际情况相吻合。研究表明,考虑土壤类型的温度植被多项式模型对旱情遥感监测具有良好的应用效果。     

Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series

The characteristics of Normalized Difference Vegetation Index (NDVI) time series can be disaggregated into a set of quantitative metrics that may be used to derive information about vegetation phenology and land cover. In this paper, we examine the patterns observed in metrics calculated for a time series of 8 years over the southwest of Western Australia—an important crop and animal production area of Australia. Four analytical approaches were used; calculation of temporal mean and standard deviation layers for selected metrics showing significant spatial variability; classification based on temporal and spatial patterns of key NDVI metrics; metrics were analyzed for eight areas typical of climatic and production systems across the agricultural zone; and relationships between total production and productivity measured by dry sheep equivalents were developed with time integrated NDVI (TINDVI). Two metrics showed clear spatial patterns; the season duration based on the smooth curve produced seven zones based on increasing length of growing season; and TINDVI provided a set of classes characterized by differences in overall magnitude of response, and differences in response in particular years. Frequency histograms of TINDVI could be grouped on the basis of a simple shape classification: tall and narrow with high, medium or low mean indicating most land is responsive agricultural cover with uniform seasonal conditions; broad and short indicating that land is of mixed cover type or seasonal conditions are not spatially uniform. TINDVI showed a relationship to agricultural productivity that is dependent on the extent to which crop or total agricultural production was directly reduced by rainfall deficiency. TINDVI proved most sensitive to crop productivity for Statistical Local Areas (SLAs) having rainfall less than 600 mm, and in years when rainfall and crop production were highly correlated. It is concluded that metrics from standardized NDVI time series could be routinely and transparently used for retrospective assessment of seasonal conditions and changes in vegetation responses and cover.     

Object-oriented Natural and Artificial Oasis Distinguishing in Landsat Imagery: Taking Minqin Oasis as an Example

Taking Minqin Oasis in the downstream area of the Shiyang River Basin which is located in the east of Hexi Corridor as an example, the Landsat 8 OLI image was chosen as the data source. Under the consideration of the basic concept of the artificial oasis and natural oasis in this paper, combining with the information of the spectrum, texture, shape and context basing on the image data preprocessing and multi-scale segmentation, we introduce a series of indexes such as NDVI、maximum difference, compactness, shape index, the space adjacency relation and so on to construct a rule set for distinguish between natural oasis and artificial oasis. The obtained results were further compared with the results based on the maximum likelihood method. As a result, the total accuracy of using the object-oriented image analysis method to distinguishing between natural oasis and artificial oasis is 91.75%, and the Kappa coefficient is 0.65 by using the rule set established in this paper. Compared with the results based on the maximum likelihood method, the overall accuracy is improved by 10.40% and the Kappa coefficient is 0.13. The Kappa coefficient of the artificial oasis is increased by 0.19, and the Kappa coefficient of the natural oasis condition is increased by 0.30. The results showed that the object-oriented image analysis method can overcome the limitations of the classification method that only using spectral feature to a certain extent, avoid the confusion caused by the phenomenon of “same object with different spectrums” and “same spectrum with different objects”, and increase the accuracy of distinguishing between the artificial oasis and natural oasis.     

生态工程背景下西双版纳NDVI时空变化分析

利用中分辨率成像光谱仪(MODIS)所合成的MOD13Q1数据,从时间变化、空间分异、重心迁移和地形因子4个方面分析了西双版纳生态工程实施前(2004～2010年)和实施后(2011～2017年)NDVI(植被归一化指数)值的变化特征,并探讨夏季NDVI值和气象因子的关系。结果表明:①2004～2010年西双版纳NDVI以-0.176%/a的速率下降,而2011～2017年NDVI以0.221%/a的速率上升。②西双版纳NDVI频度呈"单峰"结构,生态工程实施后NDVI阈值0.9所占面积比例增加最为显著(增加了17.2%),NDVI重心由西南方向(迁移距离7.58 km)转向东南方向(迁移距离4.75 km)。③与2004～2010年相比,2011～2017年NDVI在海拔720～870 m、坡度13°～16°以及东北方向上恢复成效最为显著,增长率分别为2.7%,1.4%和1.2%。④夏季气温过高、降水过多以及日照时数少是导致夏季NDVI值最低的主要因素之一。⑤生态工程的实施对2011～2017年西双版纳NDVI的上升具有重要影响,但西双版纳NDVI下降区域的面积比重仍然较大,部分地区的生态环境建设需要继续加强。     

青藏高原1982~2015年FPAR时空变化分析

植被吸收利用太阳光合有效辐射比率反映了植被固碳释氧能力,根据青藏高原GIMMS NDVI3g（1982~2015年）和MODIS NDVI（2001~2015年）数据,采用非线性半理论半经验模型进行FPAR反演及时空变化分析。结果表明：①2001~2015年GIMMS NDVI3g和MODIS NDVI反演FPAR在空间分布上具有较高的一致性,相关系数为0.82（P<0.01）,年际变化趋势一致至少6年的区域占80%;②青藏高原FPAR受坡度和海拔影响较大,其中15~35坡度FPAR变化最快,700~2 100 m海拔区间FPAR值最大;不同坡向对应的FPAR除南坡方向偏低外其他方向差异不大。③1982~2015年青藏高原四季FPAR时空变化研究中,冬季FPAR年际变化最明显,约78.5%的区域表现为增长趋势;秋季FPAR下降区域最多,但超过71.5%区域变化不显著;④基于MODIS NDVI和GIMMS NDVI两数据反演的所有植被类型的FPAR都在2012年间出现小幅度下降趋势,且不同植被类型FPAR的年际变化趋势各不相同。     

滹沱河流域植被覆盖时空演变及其与 SPEI 的相关关系

选择2001-2015年MODIS NDVI(250m)及14个气象站点气温、降水数据,通过计算标准化降水蒸散指数(SPEI),探究滹沱河流域近15年植被覆盖时空演变特征,分别从月、季节、年等尺度探究植被覆盖与SPEI的相关关系。结果显示:(1)近15年滹沱河流域归一化植被指数(NDVI)呈显著增加趋势,增速为0.024/(10a)。(2)植被恢复以明显改善为主,覆盖范围占整个流域面积的51.58%,主要分布于流域中、上游。(3)滹沱河流域SPEI总体上呈波动上升趋势,干旱程度有所降低,流域中游变湿趋势最显著。(4)6月、7月是干旱条件对植被覆盖影响最显著的月份,其中,6月气温对NDVI的影响大于降水,而7月降水对NDVI的影响大于气温;夏季干旱程度对植被覆盖度的影响最为明显;NDVI年际变化与SPEI具有显著正相关关系,干旱对植被生长状态有较大影响。