南水北调中线水源区植被覆盖度遥感监测分析

南水北调中线工程是我国大规模跨流域调水工程的一部分,开展该区域植被覆盖度变化的研究与分析,对于保护该区域的生态环境及水质具有重要意义。该文以2000年和2009年两期遥感图像为本底数据,利用基于NDVI的像元二分模型对南水北调中线水源区的植被覆盖度进行了估算,并分析了该区植被覆盖度的时空变化特征。结果表明:2000年该水源区植被覆盖度的平均值为67.5%,2009年的平均值达到72%,植被覆盖度总体呈增长趋势;植被覆盖度增幅的空间特征表现为水源区中部地区高,东西部地区相对较低;在不同植被类型中,落叶针叶林的覆盖度平均值增幅最大,草地覆盖度增幅最小;位于水源区的大多数县(市)的植被覆盖度在近十年来都有不同程度的增加,其中柞水县的植被覆盖度平均值增长幅度最大,这与国家实施退耕还林、封山育林、基本农田建设等政策有关。     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

Woody vegetation cover,height and biomass at 25-m resolution across Australia derived from multiple site,airborne and satellite observations

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha^-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.     

Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT)

Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue.     

Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic

Abstract

Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region.     

伊犁河谷天然植被覆盖度的变化特征分析

利用2000-2015年植被生长季(4~10月)MODIS/NDVI产品反演生成同时相的植被覆盖度数据,运用趋势性分析方法和皮尔逊相关系数法,进行了不同类型自然植被覆盖度时空变化特征及其与降水量、平均气温的驱动因素分析。结果表明从多年平均状态看,针叶林的植被覆盖度高于其他天然植被,灌丛类的最低,荒漠和灌丛类的植被覆盖度总体呈递增趋势;从年际尺度上看,草原和灌丛类植被对气温和降水量的响应规律大致呈反方向;不同类植被在春季(4月)对气温和降水量反映的差异性最大,与春季为新疆融雪高峰期有一定关系。     

Spatiotemporal analysis of vegetation index after typhoons in the mountainous watershed

An extensive land cover change was triggered by a series of typhoons, especially Typhoon Morakot in 2009 in Taiwan. The normalized difference vegetation index (NDVI) series from multiple satellite images were applied to monitor the change processes of land cover. This study applied spatiotemporal analysis tools, including empirical orthogonal functions (EOF), and multiple variograms in analyzing space–time NDVI data, and detected the effects of large chronological disturbances in the characteristics of land cover changes. Spatiotemporal analysis delineated the temporal patterns and spatial variability of NDVI caused by these large typhoons. Results showed that mean of NDVI decreased but spatial variablity of NDVI increased after typhoons in the study area. The EOF can clarify the major component of NDVI variations and identify the core area of the NDVI changes. Various approaches showed consistent results that Typhoon Morakot significantly lowered the NDVI in land cover change process. Furthermore, the spatiotemporal analysis is an effective monitoring tool, which advocates the use of the index for the quantification of land cover change and resilience.     

1983—1992年中国陆地植被NDVI演变特征的变化矢量分析

以NDVI时序资料为基本数据源，综合应用变化矢量分析和主成分分析方法对1983年至1992年中国陆地植被NDVI的变化强度、变化类型及空间结构变化特征进行了分析。研究结果表明在此期间中国陆地植被NDVI变化有以下特点：（1）十年间NDVI变化东西分异明显，东部变化幅度远大于西部。NDVI变化整体表现为稳中略增，增加区主要分布在台湾、福建、四川、河南等地；减少区主要分布在云南省和新疆北部等地。（2）空间结构信息表现了景观异质性，其变化主要发生在南方，反映了植被的生长和衰老过程及地形（山脉走向）变化。     

中国陆地生态系统脆弱带遥感模型

本研究通过对我国陆地生态系统8个典型样地的植被指数取样实验和图像计算结果发现，这8个样地植被指数随着水、热因子的季节变化，在时间和空间上具有一定的“绿波推移”和“景观更替”规律。在中国东部湿润的季风区（样地1－3），随着纬度的增高，其月平均植被指数与月平均气温有较大的相关。发现降水相对丰沛的地带，热量和光照条件的变化成为植被生长和变化的自然限制因子；而在中国北方森林－森林草原－典型昌原－荒漠草原－荒漠地带上，随着从东部（湿润地区）到西部（干旱地区）干湿条件的更替，月平均植被指数与降水多寡有较大的正相关关系。在8个样地上都呈现出共同的规律，即定向风的分布与植被指数的分布在时间和空间上具有逆相分布的“套合关系”。尤其在时间上有相逆套合关系，这正是中国北方沙尘暴和沙漠化加剧的自然原因。本研究定量地给出了我国陆地不同经纬度带生态系统脆弱季节和累积时间的分布。     

Phenology of trees and urbanization: a comparative study between New York City and Ithaca,New York

The ‘global warming’ effect has been found to influence vegetation phenological processes. Heat island phenomenon associated with urbanized area presents a unique place to investigate its local warming effects. This study compares the date of budburst (DOBB) of street London plane trees (Platanus × acerifolia) between highly urbanized New York City (NYC) and relatively less urbanized Ithaca, New York in 2007 and 2008. It also linked DOBB with land surface temperature and fractional vegetation cover derived from Landsat satellite images. The DOBB in NYC and Ithaca differed significantly as budburst occurred 3 and 4 days earlier in NYC than in Ithaca in 2007 and 2008, respectively. The intensity of the heat island effect and its effect on tree phenology were greater in NYC. Results show that DOBB can be explained by temperature, and findings could be extrapolated to make inferences on the potential impact of global warming on vegetation communities.     

Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated.The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST.     

Tracking desertification on the Mongolian steppe through NDVI and field-survey data

Abstract

Changing environmental and socio-economic conditions make land degradation, a major concern in Central and East Asia. Globally satellite imagery, particularly Normalized Difference Vegetation Index (NDVI) data, has proved an effective tool for monitoring land cover change. This study examines 33 grassland water points using vegetation field studies and remote sensing techniques to track desertification on the Mongolian plateau. Findings established a significant correlation between same-year field observation (line transects) and NDVI data, enabling an historical land cover perspective to be developed from 1998 to 2006. Results show variable land cover patterns in Mongolia with a 16% decrease in plant density over the time period. Decline in cover identified by NDVI suggests degradation; however, continued annual fluctuation indicates desertification – irreversible land cover change – has not occurred. Further, in situ data documenting greater cover near water points implies livestock overgrazing is not causing degradation at water sources. In combination of the two research methods – remote sensing and field surveys – strengthen findings and provide an effective way to track desertification in dryland regions.     

Effects of vegetation cover on summer climate in China during 1982–2001

In this paper, we apply lagged correlation analysis to study the effects of vegetation cover on the summer climate in different zones of China, using NOAA/AVHRR normalized difference vegetation index (NDVI) data during the time period from 1982 to 2001 and climate data of 365 meteorological stations across China (precipitation from 1982 to 2001 and temperature from 1982 to 1998). The results show that there are positive correlations between spring NDVI and summer climate (temperature and precipitation) in most zones of China; these suggest that, when the vegetation cover increases, the summer precipitation will increase, and the lagged correlations show a significant difference between zones. The stronger correlations between NDVI in previous season and summer climate occur in three zones (Mid-temperate zone, Warm-temperate zone and Plateau climate zone), and this implies that vegetation changes have more sensitive feedback effects on climate in the three zones in China.     

基于MODIS植被指数的塔河流域植被时空变化特征研究

基于获取的塔河流域2000~2014年历年4~10月间逐月MODIS植被指数产品,采用时间序列谐波分析法(HANTS)对最大值合成的逐月NDVI时间序列数据进行了重建,用趋势线分析法对塔河流域近15年生长季(4~10月)MODIS NDVI的时间变化进行计算,用一元线性回归趋势法计算得到了塔河流域近15年生长季(4~10月)NDVI变化趋势的空间分布。结合植被类型分布图对计算得到的实验结果进行了研究分析,总结了塔河流域多年植被覆盖的时空分布及其变化规律,成果可为塔河流域综合治理及生态环境评价提供依据。     

Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R² = 0.96), shrub cover (adj. R² = 0.83), grass cover (adj. R² = 0.71), bare soil (adj. R² = 0.88), stone cover (adj. R² = 0.83) and litter cover (adj. R² = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies.     

On urban heat island of Beijing based on landsat TM data

Land surface temperature (LST) of Beijing area was retrieved from Landsat TM thermal band data utilizing a radiative transfer equation and the urban heat island (HUI) effects of Beijing and its relationship with land cover and normalized difference vegetation index (NDVI) were discussed. The result of LST showed that the urban LST was evidently higher than the suburban one. The average urban LST was found to 4. 5°C and 9°C higher than the suburban and outer suburban temperature, respectively, which demonstrated the prominent UHI effects in Beijing. Prominent negative correlation between LST and NDVI was found in the urban area, which suggested the low percent vegetation cover in the urban area was the main cause of the urban heat island.     

基于NDVI序列影像精化结果的植被覆盖变化研究

植被归一化指数(NDVI)是地表植被覆盖特征的重要指标之一。本文以三峡库区2001-2003年MODIS遥感数据反演的NDVI时间序列影像为例,研究NDVI影像序列的精化问题,包括降云及去噪处理的有效方法。在改进的BISE技术降云处理的基础上,采用小波软阈值降噪方法提取有效变化趋势。然后进行库区2001-2003年植被变化的变化矢量分析,采用阈值分割的方法将库区变化强度影像分为未变化、小变化、中等变化与剧烈变化四个类型。研究成果可为三峡库区宏观生态环境变化的掌握提供一定的依据。     

NDVI时间变换一致性的贵州茂兰植被覆盖变化分析

利用近18年贵州茂兰自然保护区的Landsat TM/ETM+/OLI数据,针对云覆盖对影像质量的影响,提出并使用了一种基于NDVI时间变换一致性的方法,构建出较为完整的研究区植被指数时间序列,实现了小区域尺度下长时间序列的植被覆盖变化研究,并采用一元线性回归模型和相关分析法探讨研究区植被覆盖变化趋势及其对气象因子的响应关系。得出结论：NDVI时间变换一致性处理方法可以有效地消除云覆盖的影响;研究区近18年植被覆盖状况良好且正呈缓慢上升趋势,气候因子与植被覆盖变化呈显著正相关关系,其中平均温度的影响在当月最强,而降水量和平均相对湿度的影响则存在滞后性。     

Assessing land cover change resulting from large surface mining development

A remote sensing based land cover change assessment methodology is presented and applied to a case study of the Oil Sands Mining Development in Athabasca, Alta., Canada. The primary impact was assessed using an information extraction method applied to two LANDSAT scenes. The analysis based on derived land cover maps shows a decrease of natural vegetation in the study area (715,094 ha) for 2001 approximately −8.64% relative to 1992. Secondary assessment based on a key resources indicator (KRI), calculated using normalized difference vegetation index (NDVI measurements acquired by NOAA–AVHRR satellites), air temperature and global radiation was performed for a time period from 1990 to 2002. KRI trend analysis indicates a slightly decreasing trend in vegetation greenness in close proximity to the mining development. A good agreement between the time series of inter-annual variations in NDVI and air temperature is observed increasing the confidence of NDVI as an indicator for assessing vegetation productivity and its sensitivity to changes in local conditions.