共查询到20条相似文献，搜索用时 187 毫秒
1.
Ruben Valbuena Ana Hernando Jose Antonio Manzanera Eugenio Martínez-Falero Antonio García-Abril Blas Mola-Yudego 《International Journal of Digital Earth》2018,11(12):1205-1218
In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis. 相似文献
2.
Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence. 相似文献
3.
Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m?2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. 相似文献
4.
Traditionally, forest-stand delineation has been assessed based on orthophotography. The application of LiDAR has improved forest management by providing high-spatial-resolution data on the vertical structure of the forest. The aim of this study was to develop and test a semi-automated algorithm for stands delineation in a plantation of Pinus sylvestris L. using LiDAR data. Three specific objectives were evaluated, i) to assess two complementary LiDAR metrics, Assmann dominant height and basal area, for the characterization of the structure of P. sylvestris Mediterranean forests based on object-oriented segmentation, ii) to evaluate the influence of the LiDAR pulse density on forest-stand delineation accuracy, and iii) to investigate the algorithmś effectiveness in the delineation of P. sylvestris stands for map prediction of Assmann dominant height and basal area. Our results show that it is possible to generate accurate P. sylvestris forest-stand segmentations using multiresolution or mean shift segmentation methods, even with low-pulse-density LiDAR − which is an important economic advantage for forest management. However, eCognition multiresolution methods provided better results than the OTB (Orfeo Tool Box) for stand delineation based on dominant height and basal area estimations. Furthermore, the influence of pulse density on the results was not statistically significant in the basal area calculations. However, there was a significant effect of pulse density on Assmann dominant height [F2,9595 = 5.69, p = 0.003].for low pulse density. We propose that the approach shown here should be considered for stand delineation in other large Pinus plantations in Mediterranean regions with similar characteristics. 相似文献
5.
利用机载激光雷达点云数据,计算了9种度量指标,并将其分为冠层的高度指标、结构复杂度指标和覆盖度指标。利用高度指标和结构复杂度指标,结合大量实测单木结构与年龄估测数据,从样点和区域尺度分别分析了青海云杉林冠层垂直结构分布,分析得知实验区内主要以中龄林和成熟林为主,冠层垂直分布复杂程度偏低,高度分化程度一般。通过回归分析发现首次回波覆盖度指标FCI与实测的有效植被面积指数PAIe有良好的相关性（R2=0.66）,在此基础上基于辐射传输模型反演了实验区内PAIe的水平分布,且用实测数据验证发现反演的PAIe略高于实测值（R2=0.67）,绝对平均误差为0.65。分析结果很好地反映了激光雷达在森林空间结构信息提取方面的应用潜力。 相似文献
6.
Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data. 相似文献
7.
机载多光谱LiDAR的随机森林地物分类 总被引：1，自引：0，他引：1
机载多光谱LiDAR技术利用激光进行探测和测距，不仅可以快速获取地面物体的三维坐标，还可以获得多个波段的地物光谱信息，可广泛用于地形测绘、土地覆盖分类、环境建模、森林资源调查等。本文提出了多光谱LiDAR的随机森林地物分类方法。该方法通过对LiDAR强度数据和高程数据提取分类特征，完成多光谱LiDAR的随机森林地物分类；并分析随机森林的特征贡献度特性，采用后向特征选择方法实现分类特征选择。通过对加拿大Optech Titan多光谱LiDAR数据的试验表明：随机森林方法可以获得较好的地物分类精度，而且可以适当地去除部分冗余和相关的特征，从而有效提高分类精度。 相似文献
8.
Fabio Maselli Marta ChiesiAlessandro Montaghi Enzo Pranzini 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):662-671
Airborne LiDAR techniques can provide accurate measurements of tree height, from which estimates of stem volume and forest woody biomass can be obtained. These techniques, however, are still expensive to apply repeatedly over large areas. The current paper presents a methodology which first transforms mean stand heights obtained from LiDAR over small strips into relevant stem volume estimates. These are then extended over an entire forest by applying two estimation methods (k-NN and locally calibrated regression) to Landsat ETM+ images. The methodology is tested over a coastal area covered by pine forest in the Regional Park of San Rossore (Central Italy). The results are evaluated by comparison with the ground stem volumes of a recent forest inventory, taking into consideration the effect of stand size. In general, the accuracies of two estimation methods are dependent on the size of the forest stands and are satisfactory only when considering stands larger than 5-10 ha. The outputs of the parametric regression procedure are slightly more stable than those of k-NN and more faithfully reproduce the spatial patterns of the ground data. 相似文献
9.
Inventories of temperate forests of Central Europe mainly rely on terrestrial measurements. Rapid alterations of forests by disturbances and multilayer silvicultural systems increasingly challenge the use of conventional plot based inventories, particularly in protected areas. Airborne LiDAR offers an alternative or supplement to conventional inventories, but despite the possibility of obtaining such remote sensing data, its operational use for broader areas in Central Europe remains experimental. We evaluated two methods of forest inventory that use LiDAR data at the landscape level: the single tree segment-based method and an area-based method. We compared a set of structural forest attributes modeled by these methods with a conventional forest inventory of the highly heterogeneous forest of the Bavarian Forest National Park (Germany), which partially includes stands affected by severe natural disturbances. Area-based models were accurate for all structural attributes, with cross-validated average root mean squared error ranging from ∼3.4 to ∼13.4 in the best modeling case. The coefficients of variation for the mapped area-based estimations were mostly minor. The area-based estimations were varied but highly correlated (Pearson’s correlations between ∼ 0.56 and 0.85) with single tree segmentation estimations; undetected trees in the single tree segmentat-based method were the main sources of inconsistency. The single tree segment-based method was highly correlated (∼ 0.54 to 0.90) with data from ground-based forest inventories. The single tree-based algorithm delivered highly reliable estimates for a set of forest structural attributes that are of interest in forest inventories at the landscape scale. We recommend LiDAR forest inventories at the landscape scale in both heterogeneous commercial forests and large protected areas in the central European temperate sites. 相似文献
10.
The leaf area index (LAI) of plant canopies is an important structural parameter that controls energy, water, and gas exchanges of plant ecosystems. Remote sensing techniques may offer an alternative for measuring and mapping forest LAI at a landscape scale. Given the characteristics of high spatial/spectral resolution of the WorldView-2 (WV2) sensor, it is of significance that the textural information extracted from WV2 multispectral (MS) bands will be first time used in estimating and mapping forest LAI. In this study, LAI mapping accuracies would be compared from (a) spatial resolutions between 2-m WV2 MS data and 30-m Landsat TM imagery, (b) the nature of variables between spectrum-based features and texture-based features, and (c) sensors between TM and WV2. Therefore spectral/textural features (SFs) were first selected and tested; then a canonical correlation analysis was performed with different data sets of SFs and LAI measurement; and finally linear regression models were used to predict and map forest LAI with canonical variables calculated from image data. The experimental results demonstrate that for estimating and mapping forest LAI, (i) using high resolution data (WV2) is better than using relatively low resolution data (TM); (ii) extracted from the same WV2 data, texture-based features have higher capability than that of spectrum-based features; (iii) a combination of spectrum-based features with texture-based features could lead to even higher accuracy of mapping forest LAI than their either one separately; and (iv) WV2 sensor outperforms TM sensor significantly. However, we need to address the possible overfitting phenomenon that might be brought in by using more input variables to develop models. In addition, the experimental results also indicate that the red-edge band in WV2 was the worst on estimating LAI among WV2 MS bands and the WV2 MS bands in the visible range had a much higher correlation with ground measured LAI than that red-edge and NIR bands did. 相似文献
11.
Tree species composition of forest stand is an important indicator of forest inventory attributes for assessing ecosystem health, understanding successional processes, and digitally displaying forest biodiversity. In this study, we acquired high spatial resolution multispectral and RGB imagery over a subtropical natural forest in southwest China using a fixed-wing UAV system. Digital aerial photogrammetric (DAP) technique was used to generate multi-spectral and RGB derived point clouds, upon which individual tree crown (ITC) delineation algorithms and a machine learning classifier were used to identify dominant tree species. To do so, the structure-from-motion method was used to generate RGB imagery-based DAP point clouds. Then, three ITC delineation algorithms (i.e., point cloud segmentation (PCS), image-based multiresolution segmentation (IMRS), and advanced multiresolution segmentation (AMRS)) were used and assessed for ITC detection. Finally, tree-level metrics (i.e., multispectral, texture and point cloud metrics) were used as metrics in the random forest classifier used to classify eight dominant tree species. Results indicated that the accuracy of the AMRS ITC segmentation was highest (F1-score = 82.5 %), followed by the segmentation using PCS (F1-score = 79.6 %), the IMRS exhibited the lowest accuracy (F1-score = 78.6 %); forest types classification (coniferous and deciduous) had a higher accuracy than the classification of all eight tree species, and the combination of spectral, texture and structural metrics had the highest classification accuracy (overall accuracy = 80.20 %). In the classification of both eight tree species and two forest types, the classification accuracies were lowest when only using spectral metrics, indicated that the texture metrics and point cloud structural metrics had a positive impact on the classification (the overall accuracy and kappa accuracy increased by 1.49–4.46 % and 2.86–6.84 %, respectively). 相似文献
12.
Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features – optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense. 相似文献
13.
森林地上生物量遥感反演方法综述 总被引：9，自引：0，他引：9
森林地上生物量反演对理解和监测生态系统及评估人类生产生活的影响有着重要作用,日益发展的遥感技术使全球及大区域的生物量估算成为可能。近年来,不同的遥感技术和反演方法被广泛用于估算森林生物量。本文首先总结了现有的全球及区域生物量产品及其不确定性,然后综述了3类方法在森林地上生物量遥感反演中的应用,即基于单源数据的参数化方法、基于多源数据的非参数化方法和基于机理模型的反演方法,阐述了各类反演方法的特点、优势及局限性。最后从机理模型研究、多源遥感数据协同、生物量季节变化研究和遥感数据源不断丰富4个方面对今后的生物量遥感反演研究进行了展望。 相似文献
14.
机载激光雷达平均树高提取研究 总被引：16，自引：3，他引：13
为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求. 相似文献
15.
激光雷达森林参数反演研究进展 总被引：6，自引：0，他引：6
激光雷达通过发射激光能量和接收返回信号的方式,来获取高精度的森林空间结构和林下地形信息。全波形激光雷达通过记录返回信号的全部能量,得到亚米级植被垂直剖面;离散回波激光雷达记录的单个或多个回波,表示来自不同冠层的回波信号。星载激光雷达一般采用全波形或光子计数激光剖面系统,仅能获取卫星轨道下方的单波束或多波束数据,用于区域/全球范围的森林垂直结构及变化观测。机载激光雷达多采用离散回波或全波形激光扫描系统,能够获取飞行轨迹下方特定视场范围内的扫描数据,用于林分/区域范围的森林结构观测。地基激光雷达多采用离散回波激光扫描系统,获取以测站为中心的球形空间内扫描数据,用于单木/样地范围的森林结构观测。激光雷达单木因子估测方法可分为CHM单木法、NPC单木法和体元单木法3类。CHM单木法通过局部最大值识别树冠顶点,采用区域生长或图像分割算法识别树冠边界或树冠主方向,NPC单木法一般通过空间聚类或形态学算法识别单木,体元单木法在3维体元空间采用区域生长或空间聚类算法识别树冠。根据激光雷达冠层高度分布可以估测林分因子,冠层高度分布特征来自于离散点云或全波形。多时相激光雷达可用于森林生长量、生物量变化等监测,以及森林采伐、灾害等引起的结构变化监测。随着激光雷达技术的发展,它将在森林调查、生态环境建模等生产与科学研究领域中得到更为广泛的应用。 相似文献
16.
Eric Ariel L Salas 《地球空间信息科学学报》2021,24(2):179-200
Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height. There are a number of studies conducted utilizing waveform LiDAR products for terrestrial monitoring, but those that deal specifically with the assessment of space-borne waveform LiDAR for monitoring and modeling of phenology is very limited. This review highlights the waveform LiDAR system and looks into satellite sensors that could link waveform LiDAR and vegetation phenology, such as the proposed NASA's Global Ecosystem Dynamics Investigation (GEDI) and the Japanese Experimental Module (JEM)-borne LiDAR sensor named MOLI (Multi-footprint Observation LIDAR and Imager). Further, this work examines the richness and utility of the waveform returns and proposes a spline-function-derived model that could be exploited for estimating the leaf-shooting date. The new approach may be utilized for ecosystem-level phenological studies. 相似文献
17.
Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible. 相似文献
18.
亚热带森林参数的机载激光雷达估测 总被引：5，自引：2，他引：3
通过应用机载激光雷达数据,在分析云南省中部的78块样地的基础上提出2个预测森林不同生物特性的统计模型(加权平均高度的预测模型和生物量的预测模型),并讨论了预测结果及其精确性。从激光雷达数据中提取了2组变量(树冠高度变量组和植被密度变量组)作为自变量,采用逐步回归方法进行自变量选择。结果表明,激光雷达数据与森林的平均树高和地上各部分生物量有很强的相关性。对于3种不同森林类型(针叶林,阔叶林和混交林),平均树高估测均能达到比较高的精度;生物量的估测结果是针叶林优于阔叶林,混交林的生物量与激光雷达数据则没有明显相关性。最后,对回归分析的结果和影响预测精度的因素进行讨论,认为预测结果的精度可能与森林类型、激光雷达采样时间和采样密度以及坐标误差等因素有关。 相似文献
19.
Moses Azong Cho Andrew K. Skidmore Istiak Sobhan 《International Journal of Applied Earth Observation and Geoinformation》2009
Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI(i,j) = (Ri − Rj)/(Ri + Rj), where Ri and Rj = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors. 相似文献
20.
Carlos Alberto Silva Andrew Thomas Hudak Carine Klauberg Lee Alexandre Vierling Carlos Gonzalez-Benecke Samuel de Padua Chaves Carvalho Luiz Carlos Estraviz Rodriguez Adrián Cardil 《Carbon balance and management》2017,12(1):13