大兴安岭森林火烧恢复年限对土壤磷及其有效性的影响

林火是大兴安岭地区森林生态系统的重要影响因子,对地下土壤养分,特别是磷及其有效性变化长时间序列研究,将有助于林火后植被恢复管理、模型模拟及科学评价。选择呼中、南瓮河、双河、图强、塔河、加格达奇、满归等地区火后恢复2-50年样地48块,并以火烧区域周边相似地形地貌、未经火烧区域为配对对照样地,野外调查记录地形地貌和地理位置数据,室内分析土壤全磷和速效磷。配对t检验、冗余排序分析、趋势分析、离散性分析等处理相结合,试图找出火烧后土壤磷及其有效性变化特征。研究结果表明：（1）整体数据平均值及离散程度表明,火后全磷及速效磷均值均稍低于对照,火后二者的离散程度为37%-49%,低于对照样地6-22个百分点;区分不同恢复时间,配对t检验未发现火烧样地土壤磷与对照间的显著差异。（2）以火烧对照比值及差值进行趋势分析发现,相对于对照,火烧后全磷呈现先增加后降低的变化趋势（R²=0.89-0.95,P=0.07-0.15）,火后20-30年间开始低于对照样地。速效磷也呈类似变化趋势,但是相关性较低（R²=0.44-0.67,P=0.44-0.66）;速效磷占比年代变化趋势不明显（R²=0.08-0.12,P > 0.95）。（3）RDA排序分析表明,火烧导致恢复年限成为影响磷变化的最主要因素（15.3%）,其次是经度（10.7%）;而在对照样地中,纬度是土壤磷变化最关键影响因素,能够解释其变化总量的27.6%。研究结果为森林火后土壤养分管理以及森林演替生态评价提供重要参数。     

太岳山油松林火烧迹地恢复初期植物群落结构特征

为研究山西太岳山油松（Pinus tabuliformis）林过火后恢复初期林下草本植物群落结构,以2019年3月太岳山油松林火烧迹地为研究对象,采用群落相似性指数、多样性指数、稳定性系数、TWINSPAN数量分类和DCA排序等方法对群落组成、多样性、类型及稳定性等进行了研究。结果表明：（1）火烧迹地恢复初期以低矮灌木和多年生草本植物为主,其中又以地面芽植物占比最大。优势植物主要为大披针薹草（Carex lanceolata）和多花胡枝子（Lespedeza floribunda）;（2）火烧迹地恢复初期各样地植物群落相似性系数和多样性指数相对较小,均匀度指数较大,但各样地恢复效果不尽相同,样地6和样地7的植物群落各项指标相对大于其他样地;（3）TWINSPAN数量分类将火烧迹地植物群落划分成：I.大披针薹草+南牡蒿（Artemisia eriopoda）+地榆（Sanguisorba officinalis）群丛、II.多花胡枝子+大披针薹草+二色棘豆（Oxytropis bicolor）群丛、III.多花胡枝子+大披针薹草+米口袋（Gueldenstaedtia verna）群丛、IV.多花胡枝子+大披针薹草+荠苨（Adenophora trachelioides）群丛、V.大披针薹草+白莲蒿（Artemisia stechmanniana）+狗娃花（Aster hispidus）群丛、VI.白莲蒿群丛、VII.大披针薹草+狗娃花+野艾蒿（Artemisia lavandulifolia）群丛;（4）DCA排序结果表明,火烧迹地恢复初期植物群落结构单一,沿样地和坡位变化较小,仅区分出样地7和其他样地两大类植物群落。（5）火烧迹地恢复初期植物群落处于不稳定的状态,但各样地间群落稳定性存在一定差别,其中样地1最大,样地5最小。总之,研究区植物群落处于演替初期,各样地间植物群落稳定性较差,多样性和整体相似性较小,但优势种群相似性较高,植物群落结构仍处于动态变化之中。研究揭示了太岳山火烧迹地恢复初期植物群落结构特征,积累了该区域火烧迹地植物群落生态学数据,同时可为该区域生态恢复提供科学依据。     

大兴安岭呼中林区火烧迹地粗木质残体特征

对大兴安岭呼中林区不同年份火烧迹地的粗木质残体特征进行了研究.结果表明：呼中林区火烧迹地粗木质残体贮量在24.87～180.98 m³·hm^-2,其中倒木和枯立木分别为6.03～93.91 m³·hm^-2和15.29～138.37 m³·hm^-2,且不同年份火烧迹地之间差异显著;倒木、枯立木所占比例分别为24.26％～86.00％和14.01％～75.4％,且不同年份火烧迹地之间差异显著;倒木和枯立木的优势径级分别为2.50～20 cm和1.50～15 m, 优势长度分别为2.50～15 cm和5～20 m;随着火烧迹地的恢复,粗木质残体贮量的动态变化不明显.粗木质残体特征与火前林分条件和火烧强度密切相关.     

林草交错区植被动态变化及其影响因子——以中国东北大兴安岭为例

东北大兴安岭林草交错区对气候变化和人类活动高度敏感是我国重要的生态脆弱区,研究表征生态环境变化的植被指数时空变化及驱动因子是制定政策、改善生态环境的理论基础。基于此,利用遥感区域分析和地面实证分析对大兴安岭林草交错区的植被变化趋势及影响因子进行分析,并通过重要性指标定量阐述环境因子和人类活动因子影响的相对重要性。结果表明：1982-2015年的植被呈退化趋势（-0.02/10a）,最低温度、平均温度和最高温度均呈增温趋势分别为0.13℃/10a、0.16℃/10a和0.20℃/10a,而年平均降水量呈下降趋势（-16.3 mm/10a）。植被NDVI随云量的增加而降低（R=-0.21）,并且显著负相关占总面积的24.98%。NDVI随最低温度、平均温度和最高温度的增加而增加（R_TMN=0.01、R_TMN=0.02和R_TMN=0.04）。潜在蒸散对NDVI的影响存在差异,降水量在200-400 mm NDVI与潜在蒸散显著负相关（占总面积18.60%）,400 mm以上显著正相关且占总面积16.01%。降水量与NDVI正相关（R=0.03）,其中显著正相关占总面积的19.55%,显著负相关仅占总面积的5.31%。降水量是陈巴尔虎旗、新巴尔虎左旗以及鄂温克族自治区西部植被（草地）的主导影响因子,云分量是东部林地的主导影响因子。此外,实证分析结果表明人类活动因子（家畜密度和开垦面积）对NDVI的解释率高于温度和降水,并且人类活动的平均重要性指标（VIP_{人类活动因子}=2.48）高于气候因子（VIP_气候因子=0.80）,气候因子中的降水解释率和重要性均高于温度。因此,气候变暖背景下,东北林草交错区气候呈变暖变干旱趋势,而人类活动因子对植被的影响作用不容忽视,合理调控农牧业是改善林草交错区植被生态系统稳定可持续的重要途径。     

深圳市建成区入侵植物对草本植物种类及功能多样性的影响

入侵植物对城市生态系统形成潜在威胁,有待引起足够的关注。为探究城市入侵植物对草本植物种类及功能多样性的影响,以深圳市建成区入侵植物鬼针草（Bidens pilosa）和南美蟛蜞菊（Sphagneticola trilobata）为例,分析了不同绿地类型中不同程度的单独入侵和共同入侵对草本植物群落物种多样性和功能多样性的影响规律。结果显示：①Margalef物种丰富度指数、Shannon-Wiener多样性指数、Simpson优势度指数和Pielou均匀度指数均与入侵植物盖度呈显著负相关（P<0.05,0.5865 < R² < 0.9356）。②功能丰富度指数（FRic）、功能均匀度指数（FEve）和Rao二次熵指数（FD_Q）与入侵植物盖度有一定的相关关系（0.0000 < R² < 0.2211）。③群落的特征加权平均株高（CWM_H）与入侵植物盖度有一定的正相关关系（0.0716 < R² < 0.2262）。④与未入侵的样方相比,鬼针草轻度入侵显著提高了物种多样性（P<0.05）,鬼针草和南美蟛蜞菊单独重度入侵均显著降低物种多样性（P<0.05）,二者各种程度的分别单独入侵及共同入侵都显著提高了群落加权平均株高（CWM_H）（P<0.05）。⑤对不同的绿地类型分开计算发现,鬼针草单独入侵和二者共同入侵都显著提高了各种绿地类型的物种多样性（P<0.05）,南美蟛蜞菊单独入侵只对部分绿地类型的群落物种多样性影响显著。⑥鬼针草和南美蟛蜞菊之间的生态效应可能为拮抗作用。研究结果为进一步揭示植物入侵对城市草本植物群落的影响规律提供参考,为有效防治城市外来植物入侵提供一定的依据。     

近20年来西南地区植被净初级生产力时空变化与影响因素及其对生态工程响应

植被净初级生产力（NPP）是研究陆地生态系统中物质和能量转换的重要指标，NPP的空间分布与区域气候、植被生长以及人类活动等因素息息相关，其变化能反映植被群落的生产能力，是生态系统功能和结构变化的重要表征。近20年来，中国西南地区植被NPP呈现增长趋势。然而，目前对NPP时空变化格局及潜在原因尚不清楚。因此，利用2001-2018年间MODIS-NPP、岩性、气候、土地利用、造林面积和石漠化治理情况等数据，对西南地区植被NPP的时空变化趋势及其成因进行了分析。结果发现：（1）2001-2018年间，中国西南地区植被NPP总体呈增长趋势，突变分析结果显示，2012-2018年间NPP的增长速度（5.13 gC m^-2a^-1）比2001-2011年更快（1.78 gC m^-2a^-1），在两个时段，岩溶区NPP增长速度都高于非岩溶区；（2）对西南地区植被NPP变化与气候因子的相关分析结果显示，2001-2011年与2012-2018年两个时间段内NPP与温度的平均相关性（R=0.19，0.26）要高于NPP与降水的平均相关性（R=0.07，0.05），表明西南地区植被NPP更容易受到温度的影响；（3）对两个时期土地利用变化下NPP总量的变化情况的研究结果显示，2001-2011年期间城市用地面积增加使得NPP总量下降，而2012-2018年未利用地面积增长造成了NPP总量下降；（4）2001-2018年西南地区累计造林面积与NPP存在显著正相关性（R=0.7，P<0.05），说明"退耕还林"工程实施促进了西南地区NPP增长。对石漠化面积统计结果表明，2011年后石漠化面积显著减少，这与NPP的突变点一致，表明石漠化治理对西南地区NPP增长有重要促进作用。     

张广才岭南部东北虎猎物种群冬季营养容纳量研究

猎物匮乏是影响东北虎（Panthera tigris altaica）种群恢复的关键因素之一。容纳量研究是开展东北虎猎物恢复工作的必要前提。通过Maxent模型、聚类分析和训练随机树分类等方法,结合调查数据,预测了吉林省张广才岭南部黄泥河林业局东北虎主要猎物的适宜栖息地空间分布,解译了植被类型,在此基础上基于不同植被类型动物可采食部分代谢能、不同生境等级食物可利用率、马鹿（Cervus elaphus）和狍（Capreolus capreolus）生境等级重叠情况以及动物冬季能量需求,分析了东北虎猎物的冬季营养容纳量。结果表明：黄泥河林业局狍、野猪（Sus scrofa）和马鹿的适宜栖息地分别占研究区域总面积的52.8%、40.7%和25.4%;从猎物获取能量来看,以山杨（Populus davidiana）、桦树（Betula）、核桃楸（Juglans mandshurica）为主的植被类型是马鹿、狍可获得能量较多的生境,以蒙古栎（Quercus mongolica）、核桃楸为主的植被类型是野猪可获得能量较多的生境。东北虎猎物种群的综合冬季营养容纳量为574只马鹿（0.29只/km²）,7016只狍（3.54只/km²）,4785只野猪（2.38只/km²）。     

塔河林业局林火对植被的影响

针对大兴安岭地区塔河林业局不同林型下,不同火烧强度的火烧迹地的森林植被更新及恢复情况进行了调查研究。结果表明：（1）针叶林过火迹地上,落叶松幼苗较少,阔叶树萌条更新强度同火烧程度成正比;火烧前生长有白桦、赤杨的林地上,火烧后阔叶树成为主导树种,林相完全发生变化。（2）重度火烧下有利于天然更新,促使白桦、山杨萌生。中度火烧最初形成以杨桦为优势树种的阔叶林,后逐渐演变为针阔混交林。轻度火烧有利于针叶林的更新。（3）火烧后不同林型下灌木草本植被种类变化不同。从盖度方面来看,杜鹃落叶松林型中林下灌木草本更新最为良好。     

武夷山国家公园不同林地土壤呼吸动态变化及其影响因素

探明亚热带山岳型国家公园不同林地利用方式下土壤呼吸（R_s）的动态变化规律以及影响因素,对准确评价和预测该区域以国家公园为主体的自然保护地体系的碳收支具有重要的现实意义。以武夷山国家公园为研究对象,利用Li-8100开路式土壤碳通量测定系统对茶园、锥栗（Castanea henryi（Skam） Rehd.et Wils.）林、马尾松（Pinus massoniana Lamb.）林和裸地的土壤呼吸及近地面气温、土壤温度、土壤湿度、土壤养分和土壤微生物碳（MBC）、氮（MBN）进行测定。结果显示：（1）与近地面气温、土壤温度和土壤湿度相同,不同林地的R_s均呈现夏 > 春 > 秋 > 冬的季节动态,R_s的季节均值按大小排序为茶园（3.10 μmol m^-2 s^-1） > 马尾松（2.96 μmol m^-2 s^-1） > 锥栗（2.32 μmol m^-2 s^-1） > 裸地（1.43 μmol m^-2 s^-1）,锥栗和裸地之间、锥栗与马尾松之间均差异显著（P<0.01）。除马尾松林外,其他林地水热因子（近地面气温、土壤温度和土壤湿度）的单因子二次多项式模型对R_s的拟合度最高。水热因子共同建立的复合模型中,土壤温度、湿度的幂-指数模型对茶园R_s的拟合度较高,土壤温度和土壤湿度能够解释R_s变化的80%,马尾松林的R_s较适用于土壤温度、湿度建立的对数函数模型,而三因子线性模型（进入回归法）对锥栗林和裸地的R_s的拟合度最优,R²分别为0.565和0.281。（2）茶园和锥栗林的碳、氮、磷含量均高于马尾松林和裸地,MBN含量茶园 > 马尾松 > 锥栗 > 裸地。茶园的R_s与全磷（TP）、有效磷（AP）、全钾（TK）、速效钾（AK）含量呈极显著（P<0.01）正相关,马尾松林的R_s受TP、TK、AK含量的影响极显著（P<0.01）,锥栗林的R_s与TK、AK、MBN含量呈现显著（P<0.05）正相关,裸地的R_s受MBN含量影响较为显著（P<0.05）,4种林地土壤呼吸与养分的多元逐步回归方程R²均接近1。综上,茶园和马尾松林土壤呼吸速率较高,且所有林地的土壤呼吸均呈现夏 > 春 > 秋 > 冬的季节动态。温度和湿度与土壤呼吸的相关性强,是水热条件丰富的亚热带山岳地区土壤呼吸季节变化的主导因素,其中武夷山茶园土壤呼吸对水热因子的响应在4种林地中最为敏感。除温度和湿度外,各林地土壤呼吸受P、K元素的影响较大,其中茶园主要受P元素影响,马尾松林地受K元素影响较多。     

联合GF-6和Sentinel-2红边波段的森林地上生物量反演

光谱反射率能反映地物差异,是森林地上生物量（Aboveground Biomass,AGB）遥感反演的理论基础。红边波段处于近红外与红光波段交界处快速变化的区域,能对植被冠层结构和叶绿素含量的微小变化做出快速反应,对植被生长状况较敏感。研究以GF-6和Sentinel-2多光谱影像作为数据源,结合野外调查AGB数据,构建落叶松和樟子松AGB线性和非线性估测模型,通过比较模型精度选择最优模型进行森林AGB反演和空间分布制图。结果表明：GF-6和Sentinel-2影像红边波段反射率与落叶松、樟子松AGB均呈显著相关（P<0.05）,红边波段对AGB估测较敏感。多变量估测模型整体估测效果优于单变量模型,所有模型中多元线性回归模型取得了最优的决定系数（落叶松R²=0.66,樟子松R²=0.65）和最低的均方根误差（落叶松RMSE=31.45 t/hm²,樟子松RMSE=54.77 t/hm²）。相比单个数据源,联合GF-6和Sentinel-2影像构建的多元线性回归模型估测效果得到了显著提升,模型RMSE对于落叶松和樟子松AGB估测分别最大降低了22.9%和11.2%。增加红边波段进行AGB估测能显著提高模型估测精度,三组数据源分别加入红边波段信息后进行建模,模型RMSE得到了显著降低。GF-6拥有800 km观测幅宽和高效的重访周期,可以快速地提供大尺度时间序列数据,在森林地上生物量反演和动态监测方面有着很大潜力。     

Remote sensing approach to detect post-fire vegetation regrowth in Siberian boreal larch forest

Remote sensing with time series data offers considerable potential in the trajectory of post forest fire dynamics beyond the current monitoring of structural attributes that are displayed in the post-fire area. Many studies have addressed this topic by using time series remote sensing indices; however, this approach has sometimes been demonstrated as an unrealistic and biased representation of the post-fire forest patterns due to the saturation issues of vegetation indices. These saturation issues then lead to an underestimation of the forest successional stages and an overestimation of the forest recovery rate. This paper aims to develop a framework for trajectory of the post-fire forest patterns in the Siberian boreal larch forest (Larix sibirica) with the synergistic use of different remote sensing based vegetation-cover indicators derived from the Landsat time series and the WorldView-2 images. A time-series of the forest recovery index (FRI) and fractional vegetation cover (FVC) has been analyzed to estimate the rates of forest regeneration and vegetation recovery across different burn severity levels in the Siberian larch forest. The results showed that the FRI method can be used to observe the regrowth of the larch forest from the tenth year after the fire overlapping with the period of significant increase in the sapling stem volume. The post-fire larch forest canopy can fully recover to the pre-fire condition with respect to the magnitude of the FRI values after 30–47 years where the highest regeneration rate was observed in the moderate burn severity areas followed by the low and high burn severity. On the other hand, the FVC method was positively correlated with burn severity and more sensitive for evaluating the early stages of the forest succession in which the FVC dramatically increases after 5–6 years after the fire. The significant growth of FVC was accentuated by the maximum emergence of the sapling density as well as the rapid growth of herbaceous plants, grasses, shrubs, and shade-intolerant trees immediately after the fire, which could not be evaluated using the FRI. Both time series of the FRI and the FVC are valuable tools for determining the dominant stages of the post-fire larch forest succession in order to understand the relationships between fire disturbance and natural cycles of the boreal larch forest.     

基于NDVI假彩色合成法的土地覆被变化监测

土地覆被变化监测对区域生态系统保护、环境变化研究具有重要的作用,研究旨在提供一种基于归一化植被指数(NDVI)的假彩色合成法的土地覆被变化监测方法。该研究以黄河三角洲为研究区,以3期 Landsat TM影像(成像时间分别为1987年5月7日,1998年5月5日,2009年5月3日)为数据源,在进行相对辐射校正的基础上,生成3期NDVI图像,然后分别以三期的NDVI图像作为红、绿和蓝波段生成假彩色合成图像。基于彩色合成原理,对黄河三角洲的1987-2009年间的土地覆被变化进行了分析。结果表明:(1) 假彩色合成图像上的灰白色区域表示其土地覆被状态稳定,三个时期的NDVI值均较大,黑色区域的土地覆被状态也较稳定,但是三个时期的NDVI值均较小,而青色、绿色、红色则反映相应地区的NDVI处在不稳定状态;(2)不同的颜色反映了不同的土地覆被变化方式,较为直观地反映了土地覆被的变化特点,尤其是自然植被与农田之间的转换;(3)限于NDVI的瞬时性,该方法需要与基于遥感影像分类的方法相结合,才能更好地监测土地覆被变化。     

Satellite microwave detection of boreal forest recovery from the extreme 2004 wildfires in Alaska and Canada

The rate of vegetation recovery from boreal wildfire influences terrestrial carbon cycle processes and climate feedbacks by affecting the surface energy budget and land‐atmosphere carbon exchange. Previous forest recovery assessments using satellite optical‐infrared normalized difference vegetation index (NDVI) and tower CO₂ eddy covariance techniques indicate rapid vegetation recovery within 5–10 years, but these techniques are not directly sensitive to changes in vegetation biomass. Alternatively, the vegetation optical depth (VOD) parameter from satellite passive microwave remote sensing can detect changes in canopy biomass structure and may provide a useful metric of post‐fire vegetation response to inform regional recovery assessments. We analyzed a multi‐year (2003–2010) satellite VOD record from the NASA AMSR‐E (Advanced Microwave Scanning Radiometer for EOS) sensor to estimate forest recovery trajectories for 14 large boreal fires from 2004 in Alaska and Canada. The VOD record indicated initial post‐fire canopy biomass recovery within 3–7 years, lagging NDVI recovery by 1–5 years. The VOD lag was attributed to slower non‐photosynthetic (woody) and photosynthetic (foliar) canopy biomass recovery, relative to the faster canopy greenness response indicated from the NDVI. The duration of VOD recovery to pre‐burn conditions was also directly proportional (P < 0.01) to satellite (moderate resolution imaging spectroradiometer) estimated tree cover loss used as a metric of fire severity. Our results indicate that vegetation biomass recovery from boreal fire disturbance is generally slower than reported from previous assessments based solely on satellite optical‐infrared remote sensing, while the VOD parameter enables more comprehensive assessments of boreal forest recovery.     

Phenology-based,remote sensing of post-burn disturbance windows in rangelands

Wildland fire activity has increased in many parts of the world in recent decades. Ecological disturbance by fire can accelerate ecosystem degradation processes such as erosion due to combustion of vegetation that otherwise provides protective cover to the soil surface. This study employed a novel ecological indicator based on remote sensing of vegetation greenness dynamics (phenology) to estimate variability in the window of time between fire and the reemergence of green vegetation. The indicator was applied as a proxy for short-term, post-fire disturbance windows in rangelands; where a disturbance window is defined as the time required for an ecological or geomorphic process that is altered to return to pre-disturbance levels. We examined variability in the indicator determined for time series of MODIS and AVHRR NDVI remote sensing data for a database of ∼100 historical wildland fires, with associated post-fire reseeding treatments, that burned 1990–2003 in cold desert shrub steppe of the Great Basin and Columbia Plateau of the western USA. The indicator-based estimates of disturbance window length were examined relative to the day of the year that fires burned and seeding treatments to consider effects of contemporary variability in fire regime and management activities in this environment. A key finding was that contemporary changes of increased length of the annual fire season could have indirect effects on ecosystem degradation, as early season fires appeared to result in longer time that soils remained relatively bare of the protective cover of vegetation after fires. Also important was that reemergence of vegetation did not occur more quickly after fire in sites treated with post-fire seeding, which is a strategy commonly employed to accelerate post-fire vegetation recovery and stabilize soil. Future work with the indicator could examine other ecological factors that are dynamic in space and time following disturbance – such as nutrient cycling, carbon storage, microbial community composition, or soil hydrology – as a function of disturbance windows, possibly using simulation modeling and historical wildfire information.     

Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

Measuring and monitoring restored ecosystems: can remote sensing be applied to the ecological recovery wheel to inform restoration success?

The commencement of the United Nations Decade on Ecosystem Restoration has highlighted the urgent need to improve restoration science and fast-track ecological outcomes. The application of remote sensing for monitoring purposes has increased over the past two decades providing a variety of image datasets and derived products suitable to map and measure ecosystem properties (e.g. vegetation species, community composition, and structural dimensions such as height and cover). However, the operational use of remote sensing data and derived products for ecosystem restoration monitoring in research, industry, and government has been relatively limited and underutilized. In this paper, we use the Society for Ecological Restoration (SER) ecological recovery wheel (ERW) to assess the current capacity of drone-airborne-satellite remote sensing datasets to measure each of the SER's recommended attributes and sub-attributes for terrestrial restoration projects. Based on our combined expertise in the areas of ecological monitoring and remote sensing, a total of 11 out of 18 sub-attributes received the highest feasibility score and show strong potential for remote sensing assessments; while sub-attributes such as gene flows, all trophic levels and chemical and physical substrates have a reduced capacity for monitoring. We argue that in the coming decade, ecologists can combine remote sensing with the ERW to monitor restoration recovery and reference ecosystems for improved restoration outcomes at the local, regional, and landscape scales. The ERW approach can be adapted as a monitoring framework for projects to utilize the benefits of remote sensing and inform management through scalable, operational, and meaningful outcomes.     

基于TM遥感影像的陕北黄土区结构化植被因子指数提取

根据结构化植被因子指数的概念,以TM影像为信息源,探讨了利用遥感技术提取陕北黄土区结构化植被因子指数（C_s）的途径与方法.结果表明：在陕北黄土区,C_s能更好地描述植被群落的水土保持效益,其与绿度植被指数（归一化植被指数NDVI、修正土壤调整植被指数MSAVI）和黄度植被指（归一化差异衰败指数NDSVI、归一化耕作指数NDTI）等单一的遥感植被指数虽然均存在良好的相关关系,但用绿度与黄度植被指数相结合可综合反映植被的水土保持功能,能较好地克服单一指数在描述植被控制水土流失中的不足;MSAVI、NDTI分别是基于遥感影像提取C_s较为理想的绿度和黄度植被指数;根据群落结构化植被因子指数与遥感植被指数的关系推算区域尺度上的结构化植被因子指数是可行的,但由于不同地区植物物候期的差异,要使该方法在其他地区适用,仍需开展相应的率定和验证工作.     

新区开发建设及其生态效应——以雄安新区为例

雄安新区自2017年设立为国家级新区以来，已进行了大量的开发建设，但迄今尚未见对开发建设所产生的生态效应的研究报道。因此，选取2017年和2020年的Landsat 8影像，分别代表雄安新区未开始建设和建设3年后的两个时期来对此进行对比。利用遥感空间信息技术反演出2017—2020年雄安新区开发建设以来主要地表覆盖类型的变化，并采用遥感生态指数(RSEI)对这些地表覆盖类型变化产生的生态效应进行评估。结果表明：(1)雄安新区2017—2020年间的开发建设已使建筑用地面积增加了60.01 km²,水体面积增加了8.95 km²,植被面积减少了69.29 km²。雄安新区现阶段的开发建设主要集中在区内的容城县和雄县，而安新县的开发强度较小。(2)雄安新区2017—2020年间开发建设的生态效应体现在生态改善面积大于退化面积，生态等级良好以上的面积占比有所提升。因此，新区的生态质量总体略有上升，RSEI均值从0.668上升到0.677。但3个县的表现不一，雄县和安新县的生态有所提升，而容城县则略有下降。(3)雄安新区虽经开发...     

基于中高分辨率遥感的植被覆盖度时相变换方法

植被覆盖度是衡量地表植被状况、指示生态环境变化的一个重要指标,也是许多学科的重要参数。传统的测量方法难以获取时间连续的面状数据,且耗时、耗力,很难大范围推广。遥感估算方法虽然可以弥补传统方法的不足,但由于云覆盖等天气条件的影响,获得同一时相覆盖整个研究区的遥感影像非常困难,时相的差异必然导致研究结果产生误差。针对植被覆盖度这一重要生态参数,结合低分辨率遥感数据的时间优势和中高分辨率遥感数据的空间优势,提出一种时相变换方法,将源于中高分辨率影像的植被覆盖度变换到研究需要的时相上。首先,利用像元二分模型计算MODIS尺度的时间序列植被覆盖度,并利用已经获得的SPOT影像计算其获取时相上的植被覆盖度;其次,利用土地利用图划分植被覆盖类型,并利用MODIS数据和土地利用数据之间的空间对应关系制作MODIS像元内各类植被覆盖的面积百分比数据;再次,利用面积百分比数据提取各类植被覆盖的纯像元,结合MODIS植被覆盖度时间序列,从而提取各类植被覆盖纯像元的植被覆盖度时间序列曲线;最后利用像元分解的方法提取MODIS像元内各类植被覆盖组分的植被覆盖度的变化规律,将其应用到该组分对应位置上SPOT像元的植被覆盖度上,从而将其变换到所需要的时相上。在密云水库上游进行试验,将覆盖研究区的10景SPOT5多光谱影像计算的植被覆盖度统一变换到7月上旬,结果显示:视觉效果上明显好转,且空间上连续一致;变换前后植被覆盖度的统计量对比结果也符合植被生长规律;利用外业样点数据与对应位置的植被覆盖度变换结果进行回归分析,结果发现各植被覆盖类型的R2均在0.8左右,表明变换结果与实测值非常接近,时相变换的效果较好,从而可以很好地促进相关研究精度的提高。