本文關(guān)鍵詞：中國東北植被時空動態(tài)變化及其對氣候響應(yīng)研究　出處：《東北師范大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： GIMMS3g NDVI NPP 植被物候 氣候變化 長時間序列 MODIS

【摘要】：全球氣候變化已經(jīng)不僅僅是自然生態(tài)系統(tǒng)問題,而逐漸成為人們關(guān)心的一個重大社會問題。中國東北地區(qū)是全球氣候變化最敏感區(qū)域之一,研究該地區(qū)的植被動態(tài)變化及其對全球氣候變化的響應(yīng)具有重要的指示作用。本研究利用長時間序列GIMMS3g數(shù)據(jù)集,提取能夠反映植被時空動態(tài)變化的植被遙感參數(shù)指標(biāo)(NDVI、NPP和物候期),并結(jié)合東北地區(qū)降水、氣溫等氣象要素,對東北地區(qū)植被遙感參數(shù)指標(biāo)時空動態(tài)變化特征及其對氣候變化的響應(yīng)進(jìn)行了研究。首先對獲取的GIMMS3g數(shù)據(jù)集進(jìn)行高質(zhì)量數(shù)據(jù)重建,剔除噪聲影響,同時對東北地區(qū)氣象站點(diǎn)數(shù)據(jù)(溫度、降水、太陽輻射等)進(jìn)行空間化,獲得與遙感數(shù)據(jù)相同空間分辨率和投影系統(tǒng)的柵格數(shù)據(jù);其次,利用1982?2013年NDVI數(shù)據(jù),分析了不同植被類型季節(jié)性和年際變化特征,并分析了不同植被類型NDVI對溫度和降水變化的響應(yīng)特征;第三,采用CASA模型(光能利用效率模型),計算了東北地區(qū)植被NPP(植被凈初級生產(chǎn)力),并使用MOD17A3數(shù)據(jù)進(jìn)行驗(yàn)證,探討了東北地區(qū)不同植被類型NPP的時空變化特征及對氣候變化的響應(yīng);最后,采用不對稱高斯模型建立擬合函數(shù),對長時間序列GIMMS NDVI數(shù)據(jù)進(jìn)行擬合,獲取東北地區(qū)植被物候最佳參數(shù),提取植被生長季開始時間和生長季結(jié)束時間,分析不同類型植被物候時空變化、及其對溫度和降水響應(yīng)特征。本研究使用響應(yīng)氣候變化的三個植被遙感參數(shù)指標(biāo),從不同的角度分析東北地區(qū)不同植被類型時空變化及其氣候變化的響應(yīng),為預(yù)測東北植被變化趨勢提供了科學(xué)依據(jù)。本文主要得出的結(jié)論如下:(1)東北地區(qū)植被在過去32年里NDVI趨勢主要以負(fù)向變化為主;在不同季節(jié)中植被NDVI變化特征存在著差異,冬季、春季和夏季主要以負(fù)的變化趨勢,而秋季是正的變化趨勢。東北地區(qū)不同植被類型NDVI的變化趨勢不同,草叢在過去32年里呈現(xiàn)顯著的增加趨勢(0.0013單位/年,P0.001);草甸、高山植被、闊葉林、沼澤植被等植被NDVI值呈顯著降低的變化趨勢,其變化速率分別為-0.0004 單位/年(P0.001),-0.0009 單位/年(P0.001),-0.0004 單位/年(P0.05),-0.0007單位/年(P0.001);草原、灌叢、栽培植被、針闊混交林、針葉林等植被NDVI值沒有顯著變化;在1982?1999年時間段內(nèi),所有植被類型變化均為增加的趨勢,而在2000?2013年時間段內(nèi),除了草甸和草原有顯著增加趨勢以外,其他植被類型沒有顯著變化趨勢,因此在近14年里,東北地區(qū)植被NDVI呈現(xiàn)下降的趨勢。(2)東北地區(qū)植被森林覆蓋區(qū)域春季NDVI與年均溫度之間主要表現(xiàn)出了正相關(guān)的趨勢,而與降水相關(guān)性不顯著;夏季NDVI與年均溫度和降水都不顯著,秋季NDVI與溫度相關(guān)性也不顯著,而與降水呈現(xiàn)出相關(guān)性,且相關(guān)性主要分布在山區(qū);冬季溫度對東北地區(qū)植被NDVI的影響主要表現(xiàn)在顯著正相關(guān)性,而與降雨呈現(xiàn)負(fù)相關(guān)關(guān)系。(3)東北地區(qū)植被平均NPP 為 499.75 gC/m~2/yr,總 NPP 為 601.85× 1012 gC/yr。東北地區(qū)NPP空間分布的基本特點(diǎn)是西部、西南部低,東部、北部高,從東北向西南呈逐級遞減趨勢。不同植被類型年均NPP、總NPP差異明顯。東北地區(qū)不同植被類型年均NPP排序?yàn)獒橀熑~混交林闊葉林沼澤針葉林灌叢高山植被草甸栽培植被草叢草原。(4)東北地區(qū)過去32年里草叢、草甸、草原、灌叢、闊葉林、栽培植被、沼澤、針葉林年均NPP均表現(xiàn)為增大的趨勢,其中:草叢NPP的增速趨勢為6.63gC/m~2/yr2(P＜0.01),草甸 NPP 的增速趨勢為 3.26gC/m~2/yr2(P0.05),草原NPP 的增速趨勢為 3.9gC/m~2/yr2(P0.05),灌叢 NPP 的增速為 2.91gC/m~2/yr(P0.01),闊葉林NPP的增速趨勢為2.73gC/m~2/yr2(P0.05),栽培植被NPP的增速趨勢為3.32gC/m~2/yr2(P0.05),沼澤NPP的增速趨勢為4.91gC/m~2/yr2(P0.01),針葉林NPP的增速趨勢為4.32gC/m~2/yr2(P＜0.05)。但是,NPP的增長并不是一個直線向上的過程,在某些氣候異常的年份,不同的植被類型對不同的氣候因子反應(yīng)的敏感程度差異很大。(5)不同植被類型NPP與溫度和降水的相關(guān)性不同。春季10種植被NPP與溫度變化全部呈正相關(guān)。夏季除草原外,其他植被NPP與溫度變化均呈正相關(guān)。秋季10種植被與溫度變化全部呈正相關(guān),草甸、灌叢、闊葉林、栽培植被呈正相關(guān)顯著,其他植被類型呈正相關(guān)不顯著。冬季10種植被與溫度變化全部呈負(fù)相關(guān),草甸、沼澤呈負(fù)相關(guān)顯著,其他植被類型均呈負(fù)相關(guān)不顯著。全年來看,草叢、草甸、草原NPP與溫度呈正相關(guān),其他植被類型呈負(fù)相關(guān),正負(fù)相關(guān)性均不顯著。(6)在空間上來看,研究區(qū)SOS(植物生長開始期)南到北逐漸推遲,EOS(植物生長結(jié)束期)逐漸提前,平原地區(qū)植被SOS明顯晚于山地地區(qū)。在32年中不同植被的SOS、EOS變化趨勢特征是不同的,草叢、草甸、草原、灌叢、沼澤、針葉林的SOS出現(xiàn)提前的趨勢,高山植被、闊葉林、栽培植被、針闊混交林出現(xiàn)了推遲的趨勢。所有的植被類型EOS均出現(xiàn)了推遲的趨勢,其中趨勢最為明顯的是針闊混交林和針葉林,草叢、草甸、草原、灌叢、沼澤、高山植被、闊葉林、栽培植被等變化的趨勢相對較緩。(7)從時間上來看,春季植被SOS與溫度呈負(fù)相關(guān),溫度升高可以導(dǎo)致植物SOS提前。夏季和秋季植被EOS與溫度呈正相關(guān),溫度升高可以導(dǎo)致植物EOS推遲。在森林區(qū)域溫度升高促使SOS提前的現(xiàn)象更為明顯,而在草原區(qū)沒有顯著相關(guān)性。SOS對降水具有一定的滯后響應(yīng),而對同期的溫度響應(yīng)更為強(qiáng)烈。溫度對EOS具有一定滯后性影響,但起決定性作用的時間為9-11月份。
[Abstract]:Global climate change is not only a problem of natural ecosystems, but has gradually become a major social problem that people are concerned about. Northeast China is one of the most sensitive regions of global climate change. It is important to study the dynamic change of vegetation and its response to global climate change. This study uses GIMMS3g data long time sequence set, vegetation parameters can reflect the dynamic extraction of vegetation change (NDVI, NPP and phenology), and combined with the meteorological factors such as precipitation and temperature in Northeast China, the northeast region vegetation parameters temporal change characteristics and its response to climate change were studied. The obtained GIMMS3g data set with high quality data reconstruction, eliminate noise, and the site of Northeast China Meteorological Data (temperature, precipitation and solar radiation, etc.) of space, grid data and remote sensing data to obtain the same spatial resolution and projection system; secondly, the use of 1982? NDVI data in 2013, analysis of the different vegetation the type of seasonal and interannual variations, and the analysis of the response characteristics of different vegetation types in NDVI on the variation of temperature and precipitation; third, using the CASA model (light use efficiency model), vegetation NPP in Northeast China were calculated (net primary productivity), and verified using MOD17A3 data, discusses the temporal and spatial variations of different vegetation types NPP in Northeast China and its response to climate change; finally, a fitting function based on the asymmetric Gauss model to fit the long time sequence of GIMMS by NDVI data Take the best parameters of vegetation phenology in Northeast China, extract the beginning time of vegetation growing season and the end time of growing season, analyze the spatio-temporal change of phenology and its response to temperature and precipitation of different vegetation types. In this study, three vegetation remote sensing parameter indicators that respond to climate change were used to analyze the temporal and spatial variation of different vegetation types and their responses to climate change in Northeast China from different angles, which provided a scientific basis for predicting the trend of vegetation change in Northeast China. In this paper, the main conclusions are as follows: (1) the vegetation in Northeast China in the past 32 years NDVI trend mainly negative changes; in different seasons in different NDVI characteristics, winter, spring and summer is mainly negative trend, and the autumn is the trend of positive. The different change trend of different types of vegetation NDVI in Northeast China, the grass showed a significant increasing trend in the past 32 years (0.0013 units / year, P0.001); meadow, alpine vegetation, broad-leaved forest, swamp vegetation and other vegetation NDVI value showed a trend of decrease, the rate of change was -0.0004 per year (P0.001), -0.0009 per year (P0.001), -0.0004 per year (P0.05), -0.0007 per year (P0.001); grassland, shrub and cultivated vegetation, coniferous forest, coniferous forest vegetation NDVI value had no significant change; in 1982? 1999 period, all vegetation types were all increased the trend, while in the 2000? 2013 period, in addition to meadow and steppe had a significant increase trend, other vegetation types had no significant change trend, so in the past 14 years, vegetation NDVI in Northeast China showed a downward trend. (2) the northeast forest vegetation area between spring NDVI and annual average temperature mainly showed a trend of positive correlation, but not significant correlation with precipitation in summer; NDVI and mean annual temperature and precipitation are not significant, the fall of NDVI also has no significant correlation with temperature and precipitation, and showing a correlation, and the correlation is mainly distributed in the mountainous area; influence of winter temperature on vegetation NDVI in Northeast China is mainly reflected in the significant positive correlation, and negatively correlated with rainfall. (3) the average NPP of the vegetation in Northeast China is 499.75 gC/m~2/yr, and the total NPP is 601.85 x 1012 gC/yr. The basic characteristics of the spatial distribution of NPP in Northeast China are the west, the southwest, the East and the north, and the trend of decreasing from the northeast to the southwest. The annual average NPP and total NPP of different vegetation types were distinct. The annual average NPP of different vegetation types in the northeast region is the coniferous broadleaf broadleaf forest swamp coniferous forest shrubs and alpine vegetation meadow vegetation grasslands. (4) in Northeast China in the past 32 years, the grass meadow, grassland, shrub and broadleaf forest, coniferous forest and swamp vegetation cultivation, annual NPP showed increasing trend, the growth trend of NPP is: grass 6.63gC/m~2/yr2 (P < 0.01), the growth trend is 3.26gC/m NPP meadow (P0.05), the growth rate of ~2/yr2 the trend of grassland is 3.9gC/m~2/yr2 NPP NPP (P0.05), shrub growth rate of 2.91gC/m~2/yr (P0.01), the growth rate of NPP for the 2.73gC/m~2/yr2 trend of broad-leaved forest (P0.05), the growth rate trend of cultivated vegetation is 3.32gC/m~2/yr2 NPP (P0.05), the growth trend for 4.91gC/m~2/yr2 swamp NPP (P0.01), the growth trend of coniferous forest was 4.32gC/m~2/yr2 (NPP P < 0.05). However, the growth of NPP is not a straight upward process. In some climate abnormal years, the sensitivity of different vegetation types to different climate factors is quite different. (5) the correlation between NPP of different vegetation types and temperature and precipitation is different. The 10 planting in spring was positively correlated with the change of NPP and temperature. In summer, the other vegetation NPP was positively correlated with the change of temperature. In autumn, 10 planting was positively correlated with temperature change. There was a positive correlation between meadow, shrub, broad-leaved forest and cultivated vegetation. In winter, 10 plants were negatively correlated with temperature changes, and meadow and swamps were negatively correlated, and other vegetation types were negatively correlated. Throughout the year, the grass, meadow and grassland NPP were positively correlated with the temperature, and the other vegetation types were negatively correlated, and the positive and negative correlations were not significant. (6) in terms of space, SOS (plant growth start stage) gradually postponed from south to north, and EOS (plant growth end stage) progressively advanced. SOS in plain area was obviously later than that in mountainous area.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：Q948.1

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