甘肃省黄土丘陵地带森林植被对水资源的影响

通过对甘肃省黄土丘陵地带森林植被区与裸露的黄土丘陵区若干流域水文资料的分析,说明该区植被有利于增加降水量.对暴雨洪水及年径流也有明显的调节作用.同时,对改善区域小气候、保持水土、涵养水源效果显著.     

中国半干旱-湿润区末次间冰期以来黄土有机碳同位素特征与植被变化

对比研究中国半干旱-湿润气候区的黄土有机碳同位素(δ13CTOC)变化的控制因素及其所揭示的植被演替过程,能够为认识这些地区植被变化规律提供新的依据.迄今,秦岭以南亚热带地区土壤δ13CTOC与植被和气候之间的关系及其与黄土高原地区的联系还不清楚.本研究选取末次间冰期以来黄土高原的镇北台、西峰、洛川,以及秦岭地区的洛宁和长江下游的镇江等5个代表性黄土剖面,对其δ13CTOC进行对比分析.结果表明,湿润区黄土δ13CTOC在间冰期偏正,在冰期偏负,可以反映季风强度的变化.末次间冰期以来湿润气候条件下黄土沉积区的植被是以C4草本为主的C4/C3混合的草地.温度满足一定条件后,季风降水变化可能是控制半湿润-湿润区黄土沉积上覆C3/C4植物丰度变化的主要因素.     

MIS3阶段以来沙漠/黄土过渡区植被演替及其对气候变化的响应

选择沙漠/黄土过渡带姬塬、和林格尔两个地点的黄土-古土壤剖面开展较高分辨率花粉记录研究,重建MIS 3以来植被演替及其对气候变化的响应.黄土高原北部MIS 3阶段以来植被经历了4个主要阶段:MIS 3阶段早-中期气候较温湿,植被为疏林草原类型;MIS 3晚期-末次盛冰期为荒漠草原植被类型;末次冰消期为干草原植被类型;全...     

气候变化对黄土碳库效应影响的敏感性研究

中国黄土分布广、厚度大,是古气候变化的良好记录载体,然而黄土在大气CO2循环中到底是源还是汇一直是困扰人们的一个问题.本文利用陆相生态系统中的生物地球化学模型,通过敏感性试验,模拟了土壤有机碳对气候变化的响应.结果表明:1)在各种稳定的气候条件下黄土的土壤有机质都是持续增加的,因此可以认为自然条件下黄土是大气CO2的一个汇;2)温度和降水对黄土中土壤有机质含量的影响正好相反,表明湿度是影响黄土地区生态环境的主要因素,温度和降水都是通过对湿度的影响来影响植被生态的;3)地表植被和土壤有机质是黄土碳库与大气CO2之间的重要媒介.黄土表层的生物地球化学过程是影响黄土碳汇效应的主要过程;4)黄土碳库的主要存在形式以次生碳酸盐为主,其次是土壤有机质,气态CO2只占很小比例.     

内蒙古扎鲁特旗地区更新统沉积特征及气候环境演变

内蒙古扎鲁特旗处于东亚季风区的东北缘,对季风强弱以及全球气候变化影响敏感.本文以扎鲁特旗地区的更新统赤峰黄土与乌尔吉组作为研究对象,采用实测剖面中沉积物常量元素、微量元素、粒度、碳酸盐、磁化率、孢粉、光释光等指标分析研究了该地区气候环境变化.研究表明,研究区光释光测年结果为（130.8±6.1）～（38.7±1.7） ka,赤峰黄土的气候环境以温暖湿润草原植被景观为主,底部和顶部出现较短暂的寒冷干燥的疏林草原植被景观;乌尔吉组气候环境变化总体表现为凉爽干燥荒漠草原－草原植被景观.     

末次盛冰期以来陕北黄土高原的植被和气候变化

本文对黄土高原靖边和富县末次盛冰期以来的黄土沉积做了系统的孢粉分析.结果显示,末次盛冰期以来两地一直发育以蒿为主的草原植被,但植被组成在时间和空间上都有明显的差异.全新世早-中期植物种类比末次盛冰期丰富,沙漠植被减少,落叶阔叶树增加,表明气候变得温暖湿润.末次盛冰期靖边地区是以蒿、藜和其他菊科(非蒿属)为主的荒漠植被,...     

新疆伊犁地区黄土滑坡成因及影响因素分析

新疆伊犁地区是我国西北黄土滑坡广泛分布的地区之一,伊犁地区地处欧亚大陆腹地,独特的地形地貌条件和特殊的地理位置为黄土滑坡的形成创造了条件。黄土滑坡不仅有其形成的内在物质条件、力学条件,还有许多外在的影响因素,包括降水与融雪、地震、植被、人类工程活动,对伊犁地区黄土滑坡成因及影响因素的分析,对伊犁地区黄土滑坡的研究具有一定的价值。     

黄土-古土壤序列的典型微结构与1万年来的环境演化——以关中地区的全新世黄土剖面为例

通过对土壤微结构系统的研究,发现黄土高原南部全新世黄土-古土壤序列的微结构可分为基底式胶结结构、胶斑状结构、斑状-胶斑结构、粒状结构、似粒状结构和粒状-斑状结构.其中前3种总是出现在古土壤中,其形成在温暖湿润、植被茂盛的环境下;后3种总是出现在黄土或风化黄土中,形成在以干旱为特征的环境下.微结构沿黄土剖面垂直方向上的变化,实质上揭示了1万年来的环境变化.     

温度影响东北地区更新世植被变化的黄土记录

温度、降水(湿度)和大气CO2含量被认为是影响C3植物生长的主要因素.大量的现代植物和土壤有机质碳稳定同位素(δ13C)研究表明,温度升高可使C3植物的碳同位素变重(正),降水增多(湿度增大)和大气CO2含量升高可使C3植物的碳同位素偏轻(负);同时,C4植物可明显地影响土壤的有机碳同位素组成.基于这些认识,以前对黄土高原的黄土-古土壤序列有机质碳同位素和植被组成变化进行过不少研究.但是,相关的研究在我国东北地区的黄土中还未开展.本文对我国东北地区厚度36m的喀喇沁旗牛样子沟黄土-古土壤剖面进行了间隔10cm的采样和总有机碳含量(TOC)、有机质稳定碳同位素的测试分析.结果表明,在间冰期发育的古土壤有机质含量高、δ13C值偏正;反之,在冰期堆积的黄土有机质含量低、δ13C值偏负.通过分析表明,研究区的植被类型是以C3植物占主导地位,C4植物对土壤有机质δ13C变化的贡献有限,并且气候变化具有冰期-间冰期季风气候变化的特点,据此推断温度是决定东北地区植被碳同位素组成变化的主要因素,超过了降水(湿度)和大气CO2含量对植被(植物碳同位素组成)变化的反向影响.这一发现揭示了温度对我国东北地区长时间尺度植被变化的控制作用.这些认识对于在未来全球变暖背景下,东北地区的林木和小麦、大豆、水稻等C3作物的种植有借鉴意义.     

中国黄土高原洛川剖面S5以来的孢粉学记录

中国黄土-古土壤序列记录了距今约2.6 Ma以来的环境变迁。孢粉作为恢复植被演替的敏感指标,被广泛用于古植被重建,但是针对高原中部地区长时间尺度植被演替历史的孢粉学研究还较缺乏。通过对黄土高原洛川剖面S5以来的黄土-古土壤序列开展较高分辨率的孢粉学工作,揭示出该区约0.6 Ma以来的植被演化历史。孢粉谱分析表明,洛川地区0.6 Ma以来以温带草原植被为主,不支持塬区历史时期存在大范围落叶阔叶林的观点。现代黄土高原缺少森林植被主要是由自然原因引起,因此塬区的现代生态恢复应以退耕还草为主。     

Forecasting areas vulnerable to forest conversion using artificial neural network and GIS (case study: northern Ilam forests,Ilam province,Iran)

Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the Zagros region. Yet, areas vulnerable to forest conversion are unknown. This study aims to predict the spatial distribution of deforestation in western Iran. Landsat images dated 1988, 2001, and 2007 are classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. Meanwhile, in order to examine deforestation factors’ investigation, deforestation maps with physiographic and human spatial variables are entered into the model. Areas vulnerable to forest changes in the Zagros forest region are predicted by a multilayer perceptron neural network (MLPNN) with a Markov chain model. The results show that about 19,294 ha forest areas are deforested in the last 19 years. The predictive performance of the model appears successful, which is validated using the actual land cover map of the same year from Landsat data. The validated map is found to be 94 % accurate. The validation is also tested using the relative operating characteristic approach which yielded a value of 0.96. The model is then further extended to predict forest cover losses for 2020. The MLPNN approach was found to have a great potential to predict land use/land cover changes because it permits developing complex, nonlinear models.     

A Vegetation History of the Far North of New Zealand during the Late Otira (Last) Glaciation

During the latter part of the last (Otira) glaciation the forest cover of New Zealand was much reduced. It has frequently been postulated, however, that diverse mixed forest communities survived in the far north of North Island. Pollen diagrams and radiocarbon dates from two last glacial and postglacial (Aranuian) sits on the Aupouri Peninsula in the far north of New Zealand are compared with other published palynological and plant macrofossil evidence from the region. Mixed kauri/podocarp/angiosperm forest was present at times during the late Otiran (and Aranuian) and no evidence was found for substantial loss of forest. However, radiocarbon samples from one site, at least, seem to have been contaminated with young carbon; this introduces uncertainty into the chronology established at that site. Possibly nondeposition or erosion has obscured part or all of the late Otiran record at all the sites studied so that very much reduced forest cover at that time cannot be ruled out.     

Modelling forest resilience in Hindu Kush Himalaya using geoinformation

Resilience is the capacity of an ecosystem to absorb disturbance and undergo change while maintaining its essential structure, functions, identity and feedbacks. The forests of the Hindu Kush Himalayan (HKH) region are vulnerable to both natural and anthropogenic changes, and the forest land conversion and degradation. Using satellite-derived tree canopy cover percent data and precipitation as the explaining variable, we studied the forest cover resilience in a geospatial framework employing the logistic regression and polynomial equation fitting. Out of the \(4.3\,\hbox {million km}^{2}\) geographical areas, \(873{,}650\,\hbox {km}^{2}\) (20.20%) was under the forest in 2000 and experienced loss of \(11{,}250\,\hbox {km}^{2}\) during 2000–2010. We could model the forest cover and treeless areas fairly than the scrub and grassland owing to the variation in precipitation pattern. The majority of the forest cover (59.3%) has been estimated to have less resilience owing to the receipt of <1650 mm of total annual precipitation, whereas only \(375\,\hbox {km}^{2}\) forest area could change to scrub that shows the least resilience. About 94.4% of treeless areas were estimated to be stable, while only 1% \((25{,}200\,\hbox {km}^{2})\) area could accommodate the grassland. The resilient forest areas estimated and observed, owing to the mapping and modelling protocols used in this study, shall be useful in conservation planning in the HKH region.     

Predictive modelling of the spatial pattern of past and future forest cover changes in India

This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km², which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880–2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multi-layer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km² of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.     

喀斯特地区不同植被下小生境土壤矿物组成及有机碳含量空间异质性初步研究

通过对喀斯特地区乔木林、灌木林和草丛不同植被类型覆盖下的土面、石面、石缝、石沟、石洞、石槽和石坑7类小生境土壤样品分析测定,探讨了小生境土壤矿物组成及有机碳的空间异质性特征。结果表明:(1)不同植被类型下土壤矿物组成存在明显差异,即从草丛→灌木林→乔木林,小生境土壤中的石英、高岭石基本保持不变,但草丛土壤中的白云石矿物全部消失,灌木林、乔木林土壤中逐渐出现绿泥石;(2)随着植被正向演替,土壤有机碳含量显著增加,小生境土壤有机碳含量在不同植被类型条件下存在明显的异质性,表现为草丛>乔木林>灌木林。与此同时,以小生境土壤有机碳含量及变异系数为评价因子,分别对乔木林、灌木林及草丛中小生境有机碳进行聚类分析,结果显示,小生境微地貌特征是土壤有机碳含量及空间变异性的重要影响因子。对于土壤有机碳保护,草丛及灌木林区应以提高植被覆盖率为主;乔木林区则主要是保持现有良好的植被状况,减少人为干扰。      

新疆天山西部巩乃斯河谷积雪与森林/草地 覆盖条件下季节冻土特征分析

不同的覆盖条件下,季节冻土的特征会存在差异。为了分析积雪与森林/草地覆盖条件下季节冻土的特征,在新疆天山西部巩乃斯河上游的中国科学院天山积雪雪崩研究站的实验场地监测了森林-积雪,草地-积雪,以及草地覆盖条件下季节冻土的冻结深度,并对有无积雪覆盖条件下季节冻土发育过程中的土壤温度和土壤含水量进行了跟踪测量。结果表明：森林-积雪覆盖条件下季节冻土的冻结深度最浅,草地-积雪覆盖条件下次之,草地覆盖条件下最深。积雪的存在可以改变季节冻土的冻结深度,还会影响土壤温度和土壤含水量变化。在季节冻土的发育阶段,积雪的隔热作用使得有积雪覆盖条件下土壤温度和土壤含水量较高;在积雪消融阶段,由于积雪融水的补给,土壤含水量也相应地增加,积雪消失后由于蒸发的存在导致土壤含水量减少。     

基于MODIS数据的东北地区积雪覆盖率估算

东北地区是我国三大积雪区之一,森林覆盖面积占总面积的40%左右。受森林冠层的影响,当前的MODIS雪盖产品（V6）提供的积雪覆盖率标准模型对东北地区积雪覆盖率估算结果存在明显的低估现象。基于此,采用分区建模的方式：在森林地区,计算归一化差值林地积雪指数（NDFSI）,建立像元积雪覆盖率（FSC）与NDFSI及NDVI之间的线性关系;在非森林地区,采用MOD10A1 V6提供的归一化差值积雪指数（NDSI）,建立像元积雪覆盖率（FSC）与NDSI及NDVI之间的线性关系。采用Landsat 8 OLI数据提取的积雪覆盖率（FSC）对分区建模的估算结果与标准模型的估算结果进行对比,发现进行估算的过程中均方根误差和平均绝对误差这两项指标的数值相对于标准模型有了大幅下降,这一结果在林区有更显著的表现。计算得到的决定系数R²,在本文模型也有提高。以T1林区影像为例,本文模型的均方根误差和平均绝对误差分别为0.246、0.055,而标准模型的两项指标则分别为0.420、0.348。本文模型和标准模型的决定系数分别为0.675、0.641。     

东亚中高纬Heinrich 1事件的表现特征:呼伦湖孢粉记录

Heinrich 1事件是发生于末次冰消期的极端气候突变事件之一,对全球大气环流和陆地生态格局产生了深刻影响.基于对东亚夏季风边缘区最北端呼伦湖 HL08 孔5. 75 m 以上沉积岩芯的 AMS 14C定年技术和 415~275 cm段140个样品的孢粉分析,重建了东亚中高纬地区呼伦湖21500~13000 cal. a B. P.高分辨率植被变化历史,在此基础上揭示了Heinrich 1事件期间呼伦湖区植被响应过程,明确了Heinrich 1事件在东亚中高纬地区的表现特征.结果显示:呼伦湖区Heinrich 1事件发生于16500~15400 cal. a B. P.,以剧烈降温和显著干旱化为表现特征;事件发生期间湖区周围山地发育亚高山草甸,森林植被稀疏;湖盆区域以藜科为主的荒漠草原显著扩张,区域植被盖度降低、生态环境明显恶化;同时,不同植被类型对Heinrich 1事件的响应存在明显差别,亚高山草甸和蒿属为主的典型草原较藜科为主的荒漠草原和桦属为主的落叶阔叶林响应更为快速、敏感.     

近20余年来西北地区植被变化特征分析

利用1982—2003年8 km分辨率的NDVI数据集,选中国西北地区森林、草原、灌溉农业、雨养农业区不同类型植被为研究区,分析了植被年、年际变化特征,并对植被覆盖空间变化进行动态研究.结果表明:森林、草原、灌溉农业区和以春小麦为主的雨养农业区NDVI年变化为单峰型曲线,以冬小麦为主的雨养农业区NDVI曲线呈双峰型;同一类型的植被NDVI受纬度或海拔高度的影响,绿峰出现时间存在1个月的位相差.22 a来森林植被NDVI多呈下降趋势,草原植被区为上升趋势;雨养农业区变化不大,灌溉植被区呈显著的上升趋势.西北东部雨养农业区植被波动频率和幅度最大,是受降水影响最敏感的地区;森林植被次之;有灌溉条件的绿洲植被,年际间波动最小.22 a间西北地区植被以增加趋势为主,增加面积约为20.5%,主要分布在新疆和河西走廊绿洲、黄河沿岸灌区以及青海草区,水分条件充足的绿洲是NDVI增加最显著的区域;NDVI减少地区面积为4.77%,主要分布在西北东部.     

Assessing the transition from deforestation to forest regrowth with an agent-based model of land cover change for south-central Indiana (USA)

The Midwest and Northeast United States has experienced a net increase in forest cover since the beginning of the 20th century. However, we lack a detailed understanding of the transition to net forest cover increase because this forest regrowth occurred prior to the use of tools like household-level surveys focused on land use decision-making and availability of remote sensing data. Agent-based modeling presents an appealing means to explore what social and biophysical dynamics may have contributed to this forest regrowth process. In this paper we employ an agent-based model to analyze the process of forest regrowth in south-central Indiana from 1939 to 1993. Simulation results are presented for assumptions regarding pre-existing forest age and the stand age at which landowners harvest timber with a goal of understanding the influence of these factors on future trajectories of land cover change. The results demonstrate both the value and the burden of having many time points to evaluate the success of the model. Is the best simulation the one with the smallest net residuals across all time points? Or is the best simulation the one that best reproduces the correct trajectory of land cover change for each time interval? Model simulations that assume a harvesting frequency of 30 years all produced landscapes within 2% of the actual forest cover in 1993, the end point of the study period. However, the trajectories of these simulations did not all mimic the gradual forest regrowth found in our observed data. The model simulation with the smallest residual values across all time points assumed a initial forest age of 5 years in 1939 and a harvesting frequency of 30 years. Ultimately, we cannot definitively conclude that one model simulation is a better representation of reality than another because we have no way of comprehensively validating the model assumptions. However, we can more plausibly conclude that the age of forest regrowth in 1939 and the frequency of timber harvesting by landowners both had an influence on the successive trajectory of land cover change in the study area.