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Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 38 - 47 Titre : Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery Type de document : Article/Communication Auteurs : Gang Chen, Auteur ; Margaret R. Metz, Auteur ; David M. Rizzo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 38 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse en composantes principales [Termes IGN] Californie (Etats-Unis) [Termes IGN] délimitation [Termes IGN] houppier [Termes IGN] image à ultra haute résolution [Termes IGN] image aérienne [Termes IGN] image MASTER [Termes IGN] impact sur l'environnement [Termes IGN] incendie de forêt [Termes IGN] maladie phytosanitaire [Termes IGN] réflectance végétale Résumé : (auteur) Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively similar performance, where both spectral responses and texture contributed to burn assessments. However, the application of PCA and MNF introduced much greater variation among models across the three stages. For the early-stage disease progression, neither band reduction algorithms improved or retained the accuracy of burn severity modeling (except for the use of 10 MNF components). Compared to the no-band-reduction scenario, band reduction led to a greater level of overestimation of low-degree burns and underestimation of medium-degree burns, suggesting that the spectral variation removed by PCA and MNF was vital for distinguishing between the spectral reflectance from disease-induced dried crowns (still retaining high structural complexity) and fire ash. For the middle-stage, both algorithms improved the model R2 values by 2–37%, while the late-stage models had comparable or better performance to those using the original 50 spectral bands. This could be explained by the loss of tree crowns enabling better signal penetration, thus leading to reduced spectral variation from canopies. Hence, spectral bands containing a high degree of random noise were correctly removed by the band reduction algorithms. Compared to the middle-stage, the late-stage forest stands were covered by large piles of fallen trees and branches, resulting in higher variability of MASTER imagery. The ability of band reduction to improve the model performance for these late-stage forest stands was reduced, because the valuable spectral variation representing the actual late-stage forest status was partially removed by both algorithms as noise. Our results indicate that PCA and MNF are promising for balancing computation efficiency and the performance of burn severity models in forest stands subject to the middle and late stages of sudden oak death disease progression. Compared to PCA, MNF dramatically reduced image spectral variation, generating larger image objects with less complexity of object shapes. Whereas, PCA-based models delivered superior performance in most evaluated cases suggesting that some key spectral variability contributing to the accuracy of burn severity models in diseased forests may have been removed together with true spectral noise through MNF transformations. Numéro de notice : A2015-475 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77183 [article]

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery / Lei Ma in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 14 - 27 Titre : Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery Type de document : Article/Communication Auteurs : Lei Ma, Auteur ; Liang Cheng, Auteur ; Manchung Li, Auteur ; Yongxue Liu, Auteur ; Xiaoxue Ma, Auteur Année de publication : 2015 Article en page(s) : pp 14 - 27 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] classification orientée objet [Termes IGN] drone [Termes IGN] échelle de prise de vue [Termes IGN] image à ultra haute résolution [Termes IGN] image aérienne [Termes IGN] image optique [Termes IGN] taille du jeu de données Résumé : (auteur) Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class. Numéro de notice : A2015-692 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.12.026 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78323 [article]

Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework / H. Croft in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 85 - 95 Titre : Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework Type de document : Article/Communication Auteurs : H. Croft, Auteur ; Jing M. Chen, Auteur ; Y. Zhang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 85 - 95 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Acer saccharum [Termes IGN] aiguille [Termes IGN] image Landsat-TM [Termes IGN] indice de stress [Termes IGN] Leaf Area Index [Termes IGN] modèle de transfert radiatif [Termes IGN] Picea mariana [Termes IGN] Pinus banksiana [Termes IGN] Populus tremuloides [Termes IGN] réflectance végétale [Termes IGN] surveillance forestière [Termes IGN] teneur en chlorophylle des feuilles Résumé : (auteur) Accurate modelling of leaf chlorophyll content over a range of spatial and temporal scales is central to monitoring vegetation stress and physiological condition, and vegetation response to different ecological, climatic and anthropogenic drivers. A process-based modelling approach can account for variation in other factors affecting canopy reflectance, providing a more accurate estimate of chlorophyll content across different vegetation species, time-frames, and broader spatial extents. However, physically-based modelling studies usually use hyperspectral data, neglecting a wealth of data from broadband and multispectral sources. In this study, we assessed the potential for using canopy (4-Scale) and leaf radiative transfer (PROSPECT4/5) models to estimate leaf chlorophyll content using canopy Landsat satellite data and simulated Landsat bands from leaf level hyperspectral reflectance data. Over 600 leaf samples were used to test the performance of PROSPECT for different vegetation species, including black spruce (Picea mariana), sugar maple (Acer saccharum), trembling aspen (Populus tremuloides) and jack pine (Pinus banksiana). At the leaf level, hyperspectral and simulated Landsat bands showed very similar results to laboratory measured chlorophyll (R2 = 0.77 and R2 = 0.75, respectively). Comparisons between PROSPECT4 modelled chlorophyll from simulated Landsat and hyperspectral spectra showed a very close correspondence (R2 = 0.97, root mean square error (RMSE) = 3.01 μg/cm2), as did simulated reflectance bands from other broadband and narrowband sensors (MODIS: R2 = 0.99, RMSE = 1.80 μg/cm2; MERIS: R2 = 0.97, RMSE = 2.50 μg/cm2 and SPOT5 HRG: R2 = 0.96, RMSE = 5.38 μg/cm2). Modelled leaf chlorophyll content from Landsat 5 TM canopy reflectance data, acquired from over 40 ground validation sites, demonstrated a strong relationship with measured leaf chlorophyll content (R2 = 0.78, RMSE = 8.73 μg/cm2, p Numéro de notice : A2015-691 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78326 [article]

A greedy-based multiquadric method for LiDAR-derived ground data reduction / Chuanfa Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 110 - 121 Titre : A greedy-based multiquadric method for LiDAR-derived ground data reduction Type de document : Article/Communication Auteurs : Chuanfa Chen, Auteur ; Changqing Yan, Auteur ; Xuewei Cao, Auteur ; Jinyun Guo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 110 - 121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] interpolation [Termes IGN] lissage de données [Termes IGN] modèle numérique de surface [Termes IGN] réduction Résumé : (auteur) A new greedy-based multiquadric method (MQ-G) has been developed to perform LiDAR-derived ground data reduction by selecting a certain amount of significant terrain points from the raw dataset to keep the accuracy of the constructed DEMs as high as possible, while maximally retaining terrain features. In the process of MQ-G, the significant terrain points were selected with an iterative process. First, the points with the maximum and minimum elevations were selected as the initial significant points. Next, a smoothing MQ was employed to perform an interpolation with the selected critical points. Then, the importance of all candidate points was assessed by interpolation error (i.e. the absolute difference between the interpolated and actual elevations). Lastly, the most significant point in the current iteration was selected and used for point selection in the next iteration. The process was repeated until the number of selected points reached a pre-set level or no point was found to have the interpolation error exceeding a user-specified accuracy tolerance. In order to avoid the huge computing cost, a new technique was presented to quickly solve the systems of MQ equations in the global interpolation process, and then the global MQ was replaced with the local one when a certain amount of critical points were selected. Four study sites with different morphologies (i.e. flat, undulating, hilly and mountainous terrains) were respectively employed to comparatively analyze the performances of MQ-G and the classical data selection methods including maximum z-tolerance (Max-Z) and the random method for reducing LiDAR-derived ground datasets. Results show that irrespective of the number of selected critical points and terrain characteristics, MQ-G is always more accurate than the other methods for DEM construction. Moreover, MQ-G has a better ability of preserving terrain feature lines, especially for the undulating and hilly terrains. Numéro de notice : A2015-693 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78327 [article]

Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass / Le Li in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 198 - 208 Titre : Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass Type de document : Article/Communication Auteurs : Le Li, Auteur ; Qinghua Guo, Auteur ; Shengli Tao, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 198 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] biomasse forestière [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] régression linéaire Résumé : (auteur) Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R2 = 0.74; RMSE = 183.57 Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250–1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53–74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R2 (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB. Numéro de notice : A2015-694 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.02.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.02.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78328 [article]

Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series / Xiaolin Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 222 - 231 Titre : Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series Type de document : Article/Communication Auteurs : Xiaolin Zhu, Auteur ; Desheng Liu, Auteur Année de publication : 2015 Article en page(s) : pp 222 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] biomasse forestière [Termes IGN] image Landsat [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] puits de carbone [Termes IGN] série temporelle [Termes IGN] variation saisonnière Résumé : (auteur) Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting. Landsat data have been widely used to provide efficient and timely estimates of forest AGB because of their long archive and relatively high spatial resolution. Previous studies have explored different empirical modeling approaches to estimate AGB, but most of them only used a single Landsat image in the peak season, which may cause a saturation problem and low accuracy. To improve the accuracy of AGB estimation using Landsat images, this study explored the use of NDVI seasonal time-series derived from Landsat images across different seasons to estimate AGB in southeast Ohio by six empirical modeling approaches. Results clearly show that NDVI in the fall season has a stronger correlation to AGB than in the peak season, and using seasonal NDVI time-series can result in a more accurate AGB estimation and less saturation than using a single NDVI. In comparing these different empirical approaches, it is difficult to decide which one is superior to the other because they have different strengths and their accuracy is generally similar, indicating that modeling methods may not be the key issue for improving the accuracy of AGB estimation from Landsat data. This study suggests that future research should pay more attention to seasonal time-series data, and especially the data from the fall season. Numéro de notice : A2015-695 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.08.014 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.08.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78329 [article]