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1.
森林生物量遥感估算与应用分析 总被引：18，自引：2，他引：16
遥感图像光谱信息具有良好的综合性和现势性,利用遥感信息和GIS技术进行森林生物量估算及碳过程的研究已经成为一种全新的手段。本文对森林生物量遥感估算方法及其应用进行了深入分析,总结了利用遥感信息进行森林生物量估算的四种主要方法:遥感信息参数与生物量拟合关系的方法、遥感数据与过程模型融合的方法、基准样地法(KNN方法)以及人工神经网络模型方法,并在此基础上分析了当前该领域研究的不足,以及今后利用遥感方法进行森林生物量估算的主要发展方向。 相似文献
2.
草原是干旱区生态系统中重要的可再生资源。本文基于草本植被的结构特征,利用ASAR和TM数据,结合MIMICS模型,提出了一种估算干旱区草原地上植被生物量的方法。该方法将光学遥感数据容易反演的叶面积指数（LAI）作为反演生物量模型的参数之一,并利用LAI成功估算了单位面积内的草本植被密度。将地上生物量作为输入变量代入改进的MIMICS模型,利用查找表方法,计算出地上植被生物量。然后,将该方法应用于乌图美仁草原的地上植被生物量的反演。结果表明,该方法能够成功地反演干旱区草原草本植被地上生物量,精度达到R2=0.8562,RMSD=0.6263。最后,分析了该方法估算植被生物量的误差来源。 相似文献
3.
采用机载LiDAR数据估算森林结构参数是当前林业遥感中的研究热点。本文以福建省长汀县朱溪河流域为示范区,探讨了随机森林算法（RF）在机载LiDAR数据林分平均树高估测中的适用性。首先,通过渐进三角网（TIN）算法进行点云滤波并获取相应林分样地的植被点云子集和高程归一化的植被点云;然后,从归一化后的植被点云提取出高度分位数变量以及点云统计特征值等24个变量参数;最后,根据提取的变量参数和野外实测林分均高数据建立研究区林分平均高随机森林回归估测模型。研究结果表明,模型估测的样地平均树高与实测值具有明显线性相关关系,线性回归系数为0.938,相关系数达到0.968。对样地的估测精度都在86%以上,总体平均精度达到了93.17%。研究认为,基于植被点云变量参数的随机森林模型估测林分平均树高具有较高的可靠性。 相似文献
4.
森林碳蓄积量估算方法及其应用分析 总被引：7，自引：0，他引：7
近些年来,森林锐减、土地退化、环境污染、生物多样性丧失,特别是人类活动产生的C02浓度急剧上升和由此导致的温室效应等是目前人类面临最严峻的全球环境变化问题,所以全球碳循环问题日益成为全球变化与地球科学研究领域的前沿与热点问题,其中陆地生态系统碳循环又是全球碳循环中最复杂、受人类活动影响最大的部分。而森林生物量占整个陆地生态系统生物量的90%,因此,为了正确评估森林在全球碳平衡中的作用,了解森林生态系统在碳循环中的作用,森林的碳动态研究正日益成为人们关注的重点。本文总结了估算森林固碳量的几种方法--样地清查法、模型模拟法和遥感估算法,分析了它们的特点及应用等有关问题。 相似文献
5.
依托中分辨率成像光谱仪完整的数据序列和丰富的光谱信息,遥感特征指数在湿地生态系统发展变化的状态、趋向和规律研究方面发挥着不可替代的优势。传统类间距离判别的遥感特征指数选取中常存在过分依赖数据统计特征、入选指数与目标地类间生态学意义不明确、分类模型普适性差等局限性。基于此,本研究以河北省白洋淀湿地自然保护区为例,提出类可分离性距离判别（Class Separation Discrimination,CSD）与类间距离判别（Class Distance Discrimination,CDD）相结合的方法构建最优遥感特征指数集,并采用QUEST算法和马氏距离判别法构建分类决策树模型用于白洋淀湿地信息的提取研究,尝试克服传统类间距离指数选取中的不足。结果表明：运用CSD和CDD相结合的方法所选取的遥感特征指数在研究区湿地信息提取过程中的总体分类精度达到了91.32%,Kappa系数0.88,较传统的分类与回归树（Classification and Regression Tree,CART）方法,分类精度提高了1.67%;其次选取的最优指数与待提取的湿地类型均具有明确的生态学意义,如挺水植物在立地干湿交替条件下的潴育化过程决定了氧化铁比率IO可成功的将混分的耕地和挺水植物进一步分离;进一步将基于研究区2017年OLI影像构建的CSD和CDD相结合方法与CART方法的模型分别应用于研究区2019年OLI影像进行分类,基于CSD和CDD相结合方法构建的模型分类总体精度和Kappa系数分别为：86.97%、0.83,基于CART方法构建的模型无法满足分类需求,研究结果较好地证明了基于CSD和CDD相结合方法构建的模型在年际之间具有良好的适用性和稳定性。总之,CSD和CDD相结合的方法在不降低湿地信息提取精度的基础上,有效避免了传统遥感特征指数选择方法的局限性,提高了分类模型的普适性,是遥感特征指数选择算法和决策树相结合在湿地信息提取方面的有益尝试。 相似文献
6.
王艳杰 王卷乐 魏海硕 Altansukh Ochir Davaadorj Davaasuren Sonomdagva Chonokhuu 《地球信息科学学报》2020,22(9):1814-1822
产草量是衡量草原生产力和诊断草原健康状况的指标,是草地资源管理的重要依据。近年来,遥感数据结合地面实测数据建模已成为产草量估算的重要手段。充足的实测样点信息是产草量遥感建模估算的基础。受境外采样多重因素的制约,蒙古国产草量估算研究中无法获取足够且分布均匀的实测样点,估产模型的精度受到影响,这一问题目前尚未发现有好的解决方法。本研究选取中蒙铁路沿线（蒙古段）两侧200 km缓冲区作为研究区,针对产草量遥感估算中野外样点稀少且分布不均的问题,引入P-BSHADE方法,基于多年NDVI数据和获取的少量地面实测样点数据,考虑草地分布的非均匀性以及样点之间的相关性,对均匀分布的模拟样点处的产草量数据进行插值实验。结果显示,P-BSHADE法的插值效果优于Kriging法,可得到均匀分布于研究区的样点。基于以上实测样点和插值样点,结合NDVI、EVI、PsnNet 3种植被指数进行遥感建模,最优模型精度达到80%,精度优于已有相关研究。选取其中最优的基于NDVI的指数模型对研究区2000—2019年产草量进行反演,获得的产草量空间格局与年际变化与已有研究结果趋势吻合,进一步印证了结果的可靠性和插值方法的可行性。本研究通过插值的方式改善数据源从而提高估算模型精度是一种全新的思路与尝试,对于“一带一路”等境外区域资源环境监测具有借鉴意义。 相似文献
7.
植被等效水厚度对路域生态环境的监测评估具有重要意义。本研究以湖南醴潭高速一段为研究对象,以地面实测光谱和等效水厚度以及PRO4SAIL模拟光谱和模拟等效水厚度为数据源,利用PRO4SAIL冠层模型模拟光谱与地面实测光谱建立12种常用水分指数,引入随机森林算法对水分指数与等效水厚度进行重要性分析,得到12种水分指数的重要性排序;利用调整R 2确定建立等效水厚度估算模型中输入水分指数的最佳个数;在优选水分指数基础上,以PRO4SAIL模拟光谱计算得到水分指数和等效水厚度为训练集,分别构建随机森林耦合偏最小二乘（RF-PLS）、随机森林耦合支持向量机（RF-SVM）和随机森林耦合遗传算法优化支持向量机（RF-GA-SVM）等效水估算模型,并用地面实测等效水厚度对估算模型进行精度验证与分析。结果表明：RF-SVM估算模型中输入重要性前9的水分指数（NDWI、NMDI、SRWI、SR、NDII、WI、DWI、MSI、SAVI）时,调整R 2最高,验证集决定系数为0.8877;RF-PLS和RF-GA-SVM估算模型中输入重要性前4的水分指数（NDWI、NMDI、SRWI、SR）时,调整R 2最高,验证集决定系数分别为0.8053、0.8952,其中RF-GA-SVM模型估算等效水厚度效果最佳,其精度满足路域植被等效水厚度监测要求。本文研究成果为等效水厚度估算提供一种有效且精确的方法,同时为发展基于高光谱遥感的路域环境监测提供重要支撑。 相似文献
8.
芒萁是南方红壤侵蚀区生态恢复重要的地带性草本植物,对生态系统修复具有重要作用,监测其叶绿素含量能有效诊断生长健康状况。本文以福建省长汀县朱溪流域6个不同生态恢复年限下的芒萁叶片高光谱反射数据以及实测叶绿素含量为数据源,借助高光谱遥感技术分析不同恢复年限芒萁叶片原始光谱特征,筛选出光谱敏感波段并构建光谱指数,基于相关性分析,建立芒萁叶绿素单变量以及多元逐步回归模型,并确定最佳估算模型。结果表明：高光谱指数建立的单变量估算模型中,改进红边归一化植被指数（mNDVI705）、叶面叶绿素指数（LCI）、红边指数（Vog）、比值光谱指数（RVI603/407）、NDVI[603,407]高光谱指数建立的二次模型精度高,建模决定系数R2均超过了0.8,其中以高光谱指数为自变量建立的多元回归模型拟合R2值（0.886）最高。综合建模精度和模型验证精度,LCI指数构建的单变量模型以及基于高光谱指数的多元回归模型是估算芒萁叶片叶绿素含量最佳模型。本研究建立的叶绿素高光谱估算模型对快速、无损地监测水保植物芒萁生长具有重要意义。 相似文献
9.
植被总初级生产力(GPP)作为衡量陆地生态系统健康的重要指标,可直接反映区域环境状况和改善情况,因此准确估算植被GPP变化对区域可持续发展具有重要意义。本文利用中国及日本涡度通量观测数据,构建了基于CatBoost算法融合地形特征的GPP估算模型;并将模型应用于具有复杂地形特征的福建省,实现了该省GPP长时序模拟。研究结果表明：(1)地形特征是GPP机器学习估算的重要参数,融合地形特征建模的GPP模拟结果精度明显提高,均方根误差(RMSE)下降16%。(2) CatBoost GPP估算模型有效降低了传统GPP估算模型和常用机器学习(随机森林和支持向量机)GPP估算模型中存在的高估和低估现象,模型拥有更高的精度和更强的鲁棒性。本文GPP模拟精度：决定系数(R2)为0.888,RMSE为1.164 gC·m-2·day-1,平均绝对误差(MAE)为0.773 gC·m-2·day-1。(3)基于CatBoost GPP估算模型模拟的福建省多年GPP变化与GOSIF GPP估算结果... 相似文献
10.
1975-2009年锡林郭勒盟东部地区草地退化态势及其空间格局分析 总被引：1，自引：0，他引：1
研究采用锡林郭勒盟东部3旗、市(东乌珠穆沁旗、西乌珠穆沁旗以及锡林浩特市)1975年MSS数据、1990,2000\2005年的Landsat TM数据,以及2009年的HJ-1等遥感影像,在分析研究区陆地植被覆盖与变化特点的基础上,建立了研究区草地变化遥感解译的分类系统,构建了锡林郭勒盟东部地区5期草地现状、4期草地... 相似文献
11.
Under conditions of a warmer climate, the advance of the alpine treeline into alpine tundra has implications for carbon dynamics in mountain ecosystems. However, the above- and below-ground live biomass allocations among different vegetation types within the treeline ecotones are not well investigated. To determine the altitudinal patterns of above-/below-ground carbon allocation, we measured the root biomass and estimated the above-ground biomass (AGB) in a subalpine forest, treeline forest, alpine shrub, and alpine grassland along two elevational transects towards the alpine tundra in southeast Tibet. The AGB strongly declined with increasing elevation, which was associated with a decrease in the leaf area index and a consequent reduction in carbon gain. The fine root biomass (FRB) increased significantly more in the alpine shrub and grassland than in the treeline forest, whereas the coarse root biomass changed little with increasing altitudes, which led to a stable below-ground biomass (BGB) value across altitudes. Warm and infertile soil conditions might explain the large amount of FRB in alpine shrub and grassland. Consequently, the root to shoot biomass ratio increased sharply with altitude, which suggested a remarkable shift of biomass allocation to root systems near the alpine tundra. Our findings demonstrate contrasting changes in AGB and BGB allocations across treeline ecotones, which should be considered when estimating carbon dynamics with shifting treelines. 相似文献
12.
Response of artificial grassland carbon stock to management in mountain region of Southern Ningxia,China 总被引：1，自引：1，他引：0
Grassland is a major carbon sink in the terrestrial ecosystem. The dynamics of grassland carbon stock profoundly influence the global carbon cycle. In the published literatures so far, however, there are limited studies on the long-term dynamics and influential factors of grassland carbon stock, including soil organic carbon. In this study, spatial-temporal substitution method was applied to explore the characteristics of Medicago sativa L.(alfalfa) grassland biomass carbon and soil organic carbon density(SOCD) in a loess hilly region with different growing years and management patterns. The results demonstrated that alfalfa was the mono-dominant community during the cutting period(viz. 0–10 year). Community succession began after the abandonment of alfalfa grassland and then the important value of alfalfa in the community declined. The artificial alfalfa community abandoned for 30 years was replaced by the S. bungeana community. Accordingly, the biomass carbon density of the clipped alfalfa showed a significant increase over the time during 0–10 year. During 0–30 year, the SOCD from 0–100 cm of the soil layer of all 5 management patterns increased over time with a range between 5.300 ± 0.981 kg/m2 and 12.578 ± 0.863 kg/m2. The sloping croplands had the lowest SOCD at 5.300 ± 0.981 kg/m2 which was quite different from the abandoned grasslands growing for 30 years which exhibited the highest SOCD with 12.578 ± 0.863 kg/m2. The ecosystem carbon density of the grassland clipped for 2 years increased 0.1 kg/m2 compared with the sloping cropland, while that of the grassland clipped for 10 years substantially increased to 10.30 ± 1.26 kg/m2. Moreover, the ecosystem carbon density for abandoned grassland became 12.62 ± 0.50 kg/m2 at 30 years. The carbon density of the grassland undisturbed for 10 years was similar to that of the sloping cropland and the grassland clipped for 2 years. Different management patterns imposed great different effects on the accumulation of biomass carbon on artificial grasslands, whereas the ecosystem carbon density of the grassland showed a slight increase from the clipping to abandonment of grassland in general. 相似文献
13.
四川草原是我国5大牧区之一,其可利用的天然草地占全省草原总面积的85%,准确掌握草原产草量信息对草原管理和当地经济发展具有重要意义。本研究利用2011年7月MODIS不同分辨率(250m、500m、1km)NDVI、EVI产品和同期地面调查数据(共181个采样点),对四川草原4种主要草地类型(即高寒草甸草地、高寒灌木草地、高寒沼泽草地和山地疏林草地)产草量鲜重分类型建立估产模型。研究发现,NDVI对该地区4种主要草地类型产草量的拟合效果普遍优于EVI;相对于500m和1km的遥感数据,250m的遥感数据拟合效果较好;分草地类型建立模型的效果优于对全体样本建立模型;该地区除高寒沼泽草地用幂函数模型拟合效果较好外,其余均用指数模型进行建模效果较好;对该地区各草地类型建立的最优估产模型,精度均在70%以上,回归判定系数R2在0.75以上;利用最优模型对2011年四川省草原进行估产,总体估产精度约为90%。 相似文献
14.
Biomass is an important component of global carbon cycling and is vulnerable to climate change. Previous studies have mainly focused on the responses of aboveground biomass and phenology to warming, while studies of root architecture and of root biomass allocation between coarse and fine roots have been scarcely reported in grassland ecosystems. We conducted an open-top-chamber warming experiment to investigate the effect of potential warming on root biomass and root allocation in alpine steppe on the north Tibetan Plateau. The results showed that Stipa purpurea had significantly higher total root length, root surface area and tips than Carex moocroftii. However,there were no differences in total root volume, mean diameter and forks for the two species. Warming significantly increased total root biomass(27.60%), root biomass at 0–10 cm depth(27.84%) and coarse root biomass(diameter 0.20 mm, 57.68%) in the growing season(August). However, warming had no significant influence on root biomass in the non-growing season(April). Root biomass showed clear seasonalvariations: total root biomass, root biomass at 0–10 cm depth and coarse root biomass significantly increased in the growing season. The increase in total root biomass was due to the enhancement of root biomass at 0–10 cm depth, to which the increase of coarse root biomass made a great contribution. This research is of significance for understanding biomass allocation, carbon cycling and biological adaptability in alpine grassland ecosystems under future climate change. 相似文献
15.
Artificial planting is an important measure to promote the restoration of degraded grassland and protect the ecological environment. The objectives of the current study were to investigate the allocation pattern between aboveground biomass(AGB) and belowground biomass(BGB) in different seeding types of artificially-planted pastures. We explored the variation in biomass and the relationship between above-and belowground biomass in four artificiallyplanted pastures with one species from Elymus nutans Griseb(EN, perennial), Elymus sibiricus Linn(ES, perennial), Medicago sativa Linn(MS, perennial), and Avena sativa Linn(AS, annual) and in six artificially-planted communities with mixtures of two species by seeding ratio 1:1 from the abovementioned grasses(EN + AS, MS + AS, EN + ES, MS + EN, MS + ES, AS + ES) in 2015 and 2016. The results showed that E. nutans is the most productive species with the highest biomass production among the single crops. MS + ES was the most productive group in 2015, while the group with the highest biomass production changed to AS + ES in 2016. AGB was positively correlated to BGB in the surface soil layer in the first year, but positively related to BGB in the subsoil layer in the second year. In the early stageof artificial grassland succession, plants allocated more biomass to aboveground parts, with a root to shoot(R/S) ratio of 1.98. The slope of the log-log relationship between AGB and BGB was 1.07 in 2016, which is consistent with the isometric theory. Different sowing patterns strongly affected the accumulation and allocation of biomass in artificiallyplanted grassland, E. sibiricus was the suitable plant in the alpine regions, which will be conducive to understanding vegetation restoration and plant interactions in the future. 相似文献
16.
Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau. 相似文献
17.
华南地区种植园地广泛分布,类型混杂多样,导致园地分布信息难以正确获取,为农业管理造成了较大困难。本研究基于Landsat8 OLI数据,通过数据融合、特征优化,应用随机森林算法构建面向对象的种植园地分类规则集,对华南地区典型经济作物香蕉、柑橘、葡萄、蒲葵、海枣、番木瓜和火龙果等进行类别识别,同时对比贝叶斯分类法、K最邻近分类法、支持向量机法、决策树分类法的分类效果。结果表明：数据融合会在一定程度上影响分类结果精度;植株形态、光谱特征接近,种植期交错是影响华南地区典型园地分类精度的重要原因;以中分辨率影像为数据源,面向对象的随机森林算法应用于种植园地分类研究总体精度可达88.05%,Kappa系数0.87,可以有效区分华南地区典型种植园地类别;相比于其他算法,随机森林算法在分类精度、可靠性和稳定性上具有一定优势,可为园地作物生长监测和种植管理提供科学依据。 相似文献
18.
基于面向对象与深度学习的榆树疏林识别方法研究 总被引：1，自引：0，他引：1
榆树疏林是浑善达克沙地中一种特殊的植被类型,它对于维持区域生态系统稳定具有重要意义,在防风固沙、涵养水源、调节气候等方面发挥着重要的作用。本文利用无人机影像与GF-2影像,对高分辨率数据源中榆树疏林的两种自动识别方法进行了研究。在面向对象方法中,首先通过计算影像对象的局部方差变化率得到了最佳分割尺度;其次采用随机森林算法对初选特征的重要性进行排序,并删除无关特征;最后分别对支持向量机（SVM）、随机森林（RF）、深度神经网络（DNN）3种分类器进行参数寻优与榆树疏林提取。此外,在ENVI5.5中基于TensorFlow框架,利用U-Net构建深度学习模型对榆树疏林进行了提取,并与面向对象方法进行对比。结果显示：① 通过面向对象方法过程的优化,最终的识别精度较以往研究有所提升,GF-2影像中SVM总体精度为90.14%,RF总体精度为 90.57%,DNN总体精度为91.14%;无人机影像中SVM总体精度为97.70%, RF与DNN总体精度为97.42%。② 深度学习方法中,GF-2影像的总体精度为91.00%,无人机影像的总体精度达到了98.43%。研究结果说明在榆树疏林提取中,无人机影像具有更高的空间分辨率,更丰富的纹理、形状等信息,能达到比GF-2影像更高的精度。面向对象方法对于2种影像都有较高的适用性;深度学习的方法在本文中更适用于无人机影像,它可以有效地减少无人机影像中的错分现象。 相似文献