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1.
Expansion and heterogeneous clustering of commercial horticulture within the central highlands of Kenya after the mid-1990s impact watersheds and the sustainable resource management. This is distressing since climate conditions for world horticultural regions are projected to change, making such farming extremely difficult and costly to the environment. To understand the scope of impact on vegetation, the study evaluated (1) interannual variability in averaged normalized difference vegetation index (NDVI); (2) trends in average annual NDVI before and after 1990 – the presumed onset of rapid horticulture; and (3) relationship between the average annual NDVI and large-scale commercial farms, population density, and mean annual rainfall in subwatersheds. Time-series analysis of long-term Global Inventory Modeling and Mapping Studies NDVI data were analyzed as indicator of vegetation condition. NDVI trends before 1990s (1982–1989) and after 1990s (1990–2006) were evaluated to determine the slope (sign), and the Spearman’s correlation coefficient (strength). Overall, results show considerable variations in vegetation condition due largely to mixed factors including intensive farming activities, drought, and rainfall variation. Statistical analysis shows significant differences in slopes before 1990 and after 1990 (p < 0.05 and p < 0.1 respectively). Negative (decline) trends were common after 1990, linked to increased commercial horticulture and related anthropogenic disturbances on land cover. There was decline in vegetation over densely populated subwatersheds, though low NDVI values in 1984 and 2000 were the effect of severe droughts. Understanding the linkage between vegetation responses to the effects of human-induced pressure at the subwatershed scale can help natural resource managers approach conservation measures more effectively. 相似文献
2.
This study explores the possible linkages of El Nino/Southern Oscillation (ENSO) with vegetation and rainfall patterns, vegetation activity and food grain yields, in arid and semi-arid regions of western India. A sequence of 20-year (1981–2000) monthly maximum Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) and monthly rainfall from 160 stations were examined to study the seasonal patterns and their relation to ENSO activity. In addition, a direct (ENSO-crop yield) linkage and an intermediate (ENSO-NDVI) linkage of agricultural responses to ENSO were also investigated. The results indicate below-normal seasonal NDVI and rainfall associated with El Nino (warm) events, except during 1997, while positive anomalies occur during La Nina (cold) events. Sea surface temperature (SST) anomalies from NINO 3 region (5°N–5°S; 150°W–90°W), as an indicator of ENSO were significantly correlated with NDVI anomalies, rainfall anomalies and yield anomalies but the Southern Oscillation Index (SOI) was significantly related to NDVI anomalies only. NDVI anomaly patterns correspond to rainfall variability including that associated with ENSO activity. The observed strong intermediate linkage between yield anomalies and NDVI anomaly signal (r = 0.609) indicates that NDVI is an ideal index for understanding and analysing agricultural response to ENSO climate teleconnections. 相似文献
3.
中国陆地生态系统脆弱带遥感模型 总被引：4，自引：0，他引：4
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。 相似文献
4.
Bingwen Qiu Zhuangzhuang Wang Zhenghong Tang Zhe Liu Difei Lu Chongcheng Chen 《地理信息系统科学与遥感》2016,53(5):596-613
Given the complexity of vegetation dynamic patterns under global climate change, multi-scale spatiotemporal explicit models are necessary in order to account for environmental heterogeneity. However, there is no efficient time-series tool to extract, reconstruct and analyze the multi-scale vegetation dynamic patterns under global climate change. To fill this gap, a Multi-Scale Spatio-Temporal Modeling (MSSTM) framework which can incorporate the pixel, scale, and time-specific heterogeneity was proposed. The MSSTM method was defined on proper time-series models for multi-temporal components through wavelet transforms. The proposed MSSTM approach was applied to a subtropical mountainous and hilly agro-forestry ecosystem in southeast China using the moderate resolution imaging spectroradiometer enhanced vegetation index (EVI) time-series data sets from 2001 to 2011. The MSSTM approach was proved to be efficient in characterizing and forecasting the complex vegetation dynamic patterns. It provided good estimates of the peaks and valleys of the observed EVI and its average percentages of relative absolute errors of reconstruction was low (6.65). The complexity of the relationship between vegetation dynamics and meteorological parameters was also revealed through the MSSTM method: (1) at seasonal level, vegetation dynamic patterns are strongly associated with climatic variables, primarily the temperature and then precipitation, with correlations slight decreasing (EVI–temperature)/increasing (EVI–precipitation) with altitudinal gradients. (2) At inter-annual scale, obvious positive correlations were primarily observed between EVI and temperature. (3) Despite very low-correlation coefficients observed at intra-seasonal scales, considerable proportions of EVI anomalies are associated with climatic variables, principally the precipitation and sunshine durations. 相似文献
5.
A novel approach to study vegetation dynamics is introduced, using the Empirical Mode Decomposition (EMD) to analyze NDVI time series. The NDVI time series which is nonlinear and nonstationary can be decomposed by EMD into components called intrinsic mode functions (IMFs), based on inherent temporal scales. The highest frequency component which has been found to represent noise is subtracted from the original NDVI series; thus smoothing the noisy signal. The different key features describing vegetation phenology have been extracted by analyzing the noise free signal. The lowest frequency component (last IMF) is the trend in the NDVI series. The trend in the series has been identified finding the Sen’s slope of last IMF, and the non-parametric seasonal Mann–Kendall test has been used to confirm the significance of the observed trend. The method has been applied on per–pixel basis to the SPOT Vegetation NDVI product covering Northeast India and surrounding regions for the time span of 1998–2009. Results show that the method has performed well in identifying the pixel clusters with significant trends. Hotspot regions with severe vegetation degeneration have been identified, and the relationship of the observed trends with the expected causative variables such as land use and land cover, topographic relief, and anthropogenic causes has been explored. The spatial locations of these critical regions closely matches with the findings of the previous studies carried out locally in the region, mainly indicating the shifting cultivation practice to be the main cause for land cover change. 相似文献
6.
Baozhang Chen Guang Xu Nicholas C. Coops Philippe Ciais Ranga B. Myneni 《International Journal of Digital Earth》2016,9(11):1055-1076
The Asia-Pacific (AP) region has experienced faster warming than the global average in recent decades and has experienced more climate extremes, however little is known about the response of vegetation growth to these changes. The updated Global Inventory Modeling and Mapping Studies third-generation global satellite Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index dataset and gridded reanalysis climate data were used to investigate the spatiotemporal changes in both trends of vegetation dynamic indicators and climatic variables. We then further analyzed their relations associated with land cover across the AP region. The main findings are threefold: (1) at continental scales the AP region overall experienced a gradual and significant increasing trend in vegetation growth during the last three decades, and this NDVI trend corresponded with an insignificant increasing trend in temperature; (2) vegetation growth was negatively and significantly correlated with the Pacific Decadal Oscillation index and the El Niño/Southern Oscillation (ENSO) in AP; and (3) at pixel scales, except for Australia, both vegetation growth and air temperature significantly increased in the majority of study regions and vegetation growth spatially correlated with temperature; In Australia and other water-limited regions vegetation growth positively correlated with precipitation. 相似文献
7.
1983—1992年中国陆地植被NDVI演变特征的变化矢量分析 总被引：32，自引：2，他引：32
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。 相似文献
8.
The authors derived the normalized difference vegetation index (NDVI) from the NOAA/AVHRR Land dataset, at a spatial resolution of 8km and 15-day intervals, to investigate the vegetation variations in China during the period from 1982 to 2001. Then, GIS is used to examine the relationship between precipitation and the Normalized Difference Vegetation Index (NDVI) in China, and the value of NDVI is taken as a tool for drought monitoring. The results showed that in the study period, China’s vegetation cover had tended to increase, compared to the early 1980s; mean annual NDVI increased 3.8%. The agricultural regions (Henan, Hebei, Anhui and Shandong) and the west of China are marked by an increase, while the eastern coastal regions are marked by a decrease. The correlation between monthly NDVI and monthly precipitation/temperature in the period 1982 to 2001 is significantly positive (R2=0.80, R2=0.84); indicating the close coupling between climate conditions (precipitation and temperature) and land surface response patterns over China. Examination of NDVI time series reveals two periods: (1) 1982–1989, marked by low values below average NDVI and persistence of drought with a signature large-scale drought during the 1982 and 1989; and (2) 1990–2001, marked by a wetter trend with region-wide high values above average NDVI and a maximum level occurring in 1994 and 1998. 相似文献
9.
MODIS NDVI和AVHRR NDVI 对草原植被变化监测差异 总被引：5，自引：0，他引：5
以草地作为研究载体,对比分析草原植被AVHRR NDVI和MODIS NDVI两种NDVI序列的年内、年际变化特征,讨论两种NDVI序列对降水量、平均气温和水汽压3种气候因子的响应差异,为合理选择NDVI序列对植被进行监测研究提供参考。结果表明：(1)两种NDVI序列所反映的草原植被年内变化趋势相似,但MODIS NDVI对各类草原的区分度优于AVHRR NDVI;(2)两种NDVI序列所反映的2000年—2003年草原植被年际变化差异明显。较之于MODIS NDVI,AVHRR NDVI变化趋势分类图表现出更强的植被改善趋势,植被改善面积在AVHRR NDVI变化趋势分类图中占94.25%,在MODIS NDVI中为83.33%;两种NDVI变化趋势分类图反映的植被变化趋势吻合度为52.88%。(3)两种NDVI序列与水汽压、降水量相关性差异显著。MODIS NDVI与各站点平均气温的相关系数均大于GIMMS NDVI;而MODIS NDVI与水汽压的相关系数83%(10个站点)小于GIMMS NDVI,与降水量的相关系数67%(8个站点)小于GIMMS NDVI。 相似文献
10.
基于MODIS-NDVI数据分析澜沧江流域生长季植被NDVI时空特征和变化趋势,结合地形数据、气象站点数据和植被类型数据,利用趋势分析和相关性分析法研究植被NDVI变化对气候因子的响应。结果表明:1)2000-2017年澜沧江流域生长季植被NDVI均值为0.592,整体呈现出由西北向东南波动增加趋势,增长速率为0.09%/10年;2) 2000-2017年澜沧江流域气温呈上升趋势,降水呈下降趋势,植被NDVII总体与平均气温的相关性高于累积降水量;3)澜沧江流域生长季植被NDVI驱动因子分析表明,气候驱动中以气温降水联合驱动为主,流域植被NDVI变化整体为非气候驱动。 相似文献
11.
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。 相似文献
12.
李春娥 《测绘与空间地理信息》2022,45(1):95-99,107
针对植被与气候因子的分析多从研究区总体出发、缺少地域差异化研究的问题,本文提出了在不同地貌、坡度和植被类型区域进行植被覆盖度的时空分布特征及其影响因子分析研究的方法.该方法实现了植被在不同地理环境、不同植被类型中对气候因子响应机制的差异化研究.结果表明:1)植被覆盖度在平原和丘陵、坡度小于2°和大于15°区域递增趋势较... 相似文献
13.
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. 相似文献
14.
地表温度和近地表大气温度是地球系统、大气系统以及地—气相互作用物理过程的重要参量。在陆地—大气的相互作用过程中,水汽含量、NDVI指数、下垫面变化等因素会对地—气热量传输造成一定的影响。本文首先利用地表温度产品（MYD11A1）以及气温站点数据（GSOD）获得全国尺度下地表温度年最大值、近地面气温年最大值。在此基础上,使用趋势分析法分析2003年—2018年地、气温度年最大值时空分布特征及变化趋势,以及地—气温差气候倾向率变化趋势。最后,结合大气总水汽含量产品（MYD05）、NDVI指数（MYD13A3）、二氧化碳平均浓度增长率分析导致地表温度年最大值与近地面气温年最大值趋势发生变化的原因。研究结果表明：（1）在全国尺度下,2003年—2018年地表温度年最大值呈现北高南低的空间分布特征。近地面气温年最大值的空间分布与地表温度年最大值相反。大气总水汽含量年最大值在热带、亚热带季风气候区内总体较高。水汽含量既影响近地面气温的大小,同时也受到近地面气温的影响,因此,水汽含量年最大值与近地面气温年最大值表现出一定的空间分布一致性特征。（2）在2003年—2018年期间,地表温度年最大值的气候倾向率在空间上表现出北高南低的分布特征。近地面气温年最大值的气候倾向率在空间上也表现为北高南低,与地表温度年最大值的气候倾向率变化基本一致。但地表温度年最大值的变化幅度要大于近地面气温年最大值,并且在个别区域表现不一致。主要分布在天山地区、三江平原以及秦岭南侧地区,地—气年最大值变化趋势相反即地—气差减小。（3）大气总水汽含量年最大值的增加可造成近地面气温年最大值的增加,而植被覆盖度的上升可造成地表温度年最大值下降。但在天山地区大气总水汽含量与地—气差的响应不明显,但天山地区的近地面气温年最大值与CO2平均浓度增长率的关系较为明显。（4）遥感数据反演的地表温度年最大值和站点观测的近地面气温年最大值空间分布表现出差异,但时间变化趋势基本一致。 相似文献
15.
《International Journal of Digital Earth》2013,6(11):1098-1117
ABSTRACTDetecting changes in vegetation, distinguishing the persistence of changes, and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics. Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (NDVI) version NDVI3g dataset in the Tibetan Plateau, the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982. Dynamic trends in vegetation experienced an obvious increase at regional scale, but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended. At pixel scale, areas with significant browning significantly expanded over 14 periods for all seasons, but for significant greening significantly increased only in autumn. The changes of vegetation activity in spring were the most drastic among three seasons. Increased increments of NDVI in summer, spring, and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods. Vegetation activity was mainly regulated by a thermal factor, and the dominant climatic drivers of vegetation growth varied across different seasons and regions. We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn. 相似文献
16.
Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 1-km vegetation index products, daily temperature, photosynthetically active radiation (PAR), and precipitation from 2001 to 2004 were utilized to analyze the temporal variations of the MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), as well as their correlations with climate over the evergreen forested sites in Zhejiang-a humid subtropical region in the southeast of China. The results showed that both NDVI and EVI could discern the seasonal variation of the evergreen forests. Attributed to the sufficient precipitation in the study area, the growth of vegetation is mainly controlled by energy; as a result, NDVI, and especially EVI, is more correlated with temperature and PAR than precipitation. Compared with NDVI, EVI is more sensitive to climate condition and is a better indicator to study vegetation variations in the study region 相似文献
17.
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 m... 相似文献
18.
ABSTRACT Inner Mongolia is an important ecological zone of northern China and 67% of its land area is grassland. This ecologically fragile region has experienced significant vegetation degradation during the last decades. Although the spatial extents and rates of vegetation change have previously been characterized through various remote sensing and GIS studies, the underlying driving factors of vegetation changes are still not well understood. In this study, we first used time-series MODIS NDVI data from 2000 to 2016 to characterize the temporal trend of vegetation changes. These vegetation change trends were compared with climate and socioeconomic variables to determine the potential drivers. We used a set of statistical methods, including multiple linear regression (MLR), spatial correlation analysis, and partial least squares (PLS) regression analyzes, to quantify the spatial distribution of the driving forces and their relative importance to vegetation changes. Results show that the main driving factors and their impact magnitude (weight) are in the order of human activities (r = -0.785, p < 0.01, VIP = 1.37), precipitation (r = 0.541, p < 0.05, VIP = 0.89), temperature (r = -0.319, p > 0.05 VIP = 0.59). The area affected by human activities was 10.57%. Specific human activities, such as coal mining and grazing were negatively associated with vegetation cover, while eco-engineering projects had positive impacts. This study provided thorough quantification of driving forces of vegetation change and enhanced our understanding of their interactions. Our integrated geospatial-statistical approach is particularly important for sustainable development of ecosystem balance in Chen Barag Banner and other areas facing similar challenges. 相似文献
19.
《地理信息系统科学与遥感》2013,50(3):371-393
This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact. 相似文献
20.
R D Doi 《Journal of the Indian Society of Remote Sensing》2001,29(4):213-224
The purpose of this paper is to demonstrate the suitability of NOAA-AVHRR imagery for monitoring vegetal cover. Of course, climate is recognized as the most important control on natural vegetation. However, spatial and seasonal changes in land cover are dependent on several factors including the crop species, soil hydrology, grazing conditions, harvest timing and above all on the frequency and amount of rainfall. The analysis is based on six maximum value composite (MVC) NDVI images with a spatial resolution of approximately 5.5 km. An attempt has also been made to examine the serial relationship between the NDVI and the rainfall occurrences at different stations. ‘Thar Desert’ is clearly visible in the images of summer months. A transitional zone is situated between the arid and semi-arid areas along the western fringe of the Aravalli Range. The elimination of time lag in vegetational response of rainfall indicates expected positive relationship between vegetal cover and rainfall. 相似文献