Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
AbstractRemote sensing has been a useful tool to monitor net primary productivity (NPP) and evapotranspiration (ET). In this paper, based on field measurements and Landsat enhanced thematic mapper plus (ETM+) data, NPP and ET are estimated in 2001 in the Changbaishan Natural Reserve, China. Maps of land cover, leaf area index, and biomass of this forested region are first derived from ETM+ data. With these maps and additional soil texture and daily meteorological data, NPP and ET maps are produced for 2001 using the boreal ecosystem productivity simulator (BEPS). The results show that the estimated and observed NPP values for forest agree fairly well, with a mean relative error of 8.6%. The NPP of mixed forests is the highest, with a mean of 500 g C m–2·a–1, and that of alpine tundra and shrub is the lowest, with a mean of 136 g C m–2·a–1. Unlike the spatial pattern of NPP, the annual ET changes distinctly with altitude from greater than 600 mm at the foot of the mountain to about 200 mm at the top of the mountain. ET is highest for broadleaf forests and lowest for urban and built-up areas.La télédétection est un outil utile pour faire le suivi de la productivité primaire nette (PPN) et de l'évapotranspiration (ET). Dans cet article, basé sur des mesures de terrain et des données ETM+ de Landsat, on fait l'estimation de la PPN et de l'ET pour l'année 2001, dans la réserve naturelle de Changbaishan, en Chine. Des cartes du couvert, d'indice de surface foliaire et de biomasse de cette région forestière sont dérivées au départ des données ETM+. À l'aide de ces cartes, de données supplémentaires sur la texture du sol et des données météorologiques journalières, on a produit des cartes de PPN et ET à l'aide du simulateur BEPS (« boreal ecosystem productivity simulator ») pour 2001. Les résultats montrent que les valeurs estimées et observées de PPN de la forêt concordent plutôt bien, avec une erreur relative moyenne de 8,6%. La valeur de PPN des forêts mixtes est plus élevée, avec une moyenne de 500 g C m–2·a–1, alors que la valeur de PPN de la toundra alpine et des arbustes est plus faible, avec une moyenne de 136 g C m–2·a–1. Contrairement au patron spatial de PPN, la valeur annuelle de ET change de façon marquée avec l'altitude à partir de 600 nm, au pied de la montagne, à environ 200 nm au sommet de la montagne. La valeur de ET est plus élevée pour les forêts de feuillus et à son plus bas pour les zones urbaines et construites.[Traduit par la Rédaction]
Topographic effects are the main obstacles to further analysis of satellite spectral data in mountainous area, especially to quantitative remote sensing. To obtain the true reflectance ofthe land surface, we must remove the topographic effects first. A physical model for rugged terrain is developed in this paper. Both atmospheric and topographic effects are considered in the model. 3 illumination sources are expressed analytically which include: 1) direct solar irradiance; 2) diffuse sky irradiance; 3) reflected irradiance from the adjacent terrain. Based on a quick searching algorithm for local horizon. the most complex part in the model—the reflected radiation can be calculated throughout the whole image in an economic computation time. In stead of using the field measured data or standard atmospheric condition obtained from the commercial software, we invert the atmospheric parameters from the image itself based on stochastic programming theory, a priori knowledge is used in the inversion process. To test the model, a Landsat TM-scene is matched to a digital elevation model(DEM) which has a resolution of Im for elevation. The true reflectance map is obtained from the model. It is found that most ofthe topographic effects are removed in the map.
Urban areas have profound environmental impacts, while the existed products of urban areas have some issues, such as low spatial resolution and confused definition of urban. In this study, we developed a method based on image pattern recognition is developed to classify urban and rural from the artificial surfaces class in GlobaLand30. The global urban areas with 30m resolution in years 2000 and 2010 are extracted. The results are compared with the data from the China City Statistical Yearbooks (CCSY) and the US Census Bureau (USCB) in year 2010. The correlation coefficient between our urban areas and CCSY reached at 0.877. The user accuracy between our urban areas and USCB can reach at 91.82%. The major difference is from the green land and water in the urban areas and the urban fringe with more green lands, where are ignored by our data.
The academician Xiaowen Li devoted much of his life to pursuing fundamental research in remote sensing. A pioneer in the geometric-optical modeling of vegetation canopies, his work is held in high regard by the international remote sensing community. He codeveloped the Li–Strahler geometric-optic model, and this paper was selected by a member of the International Society for Optical Engineering (SPIE) milestone series. As a chief scientist, Xiaowen Li led a scientific team that made outstanding advances in bidirectional reflectance distribution modeling, directional thermal emission modeling, comprehensive experiments, and the understanding of spatial and temporal scale effects in remote sensing information, and of quantitative inversions utilizing remote sensing data. In addition to his broad research activities, he was noted for his humility and his dedication in making science more accessible for the general public. Here, the life and academic contributions of Xiaowen Li to the field of quantitative remote sensing science are briefly reviewed.
Soil-Adjusted Vegetation Index (SAVI) is found to be undesirable to estimate Leaf Area Index (LAI) with heterogeneous canopy structure in low vegetation cover. In this article, three new vegetation indices (VIs), such as Normalized Hotspot-Signature Vegetation Index 2 (NHVI2), Hotspot-Signature Soil-Adjusted Vegetation Index (HSVI), and Hotspot-Signature 2-Band Enhanced Vegetation Index (HEVI2), are proposed for a better quantitative estimation of LAI and soil-noise resistance than with SAVI. To obtain these new indices, the angular index called Normalized Difference between Hotspot and Darkspot (NDHD) is introduced which represents the distribution of foliage in vegetation canopy. The validity of new VIs is statistically verified using simulated data and field measurements. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate both the homogeneous and heterogeneous canopy for analyzing vegetation isolines behaviors, soil-noise resistance, and LAI estimation. In situ measurements of LAI and bidirectional reflectance factor from the Boreal Ecosystem-Atmosphere Study (BOREAS) are also used to test the robustness of the new VIs for the estimation of LAI. By considering the distribution of the foliage, the accuracy of LAI estimation of SAVI for heterogeneous canopy improved almost 16% using exponential regression analysis. With the improvement of multiangular remote-sensing and Bidirectional Reflectance Distribution Function (BRDF) models in the future, hotspot-signature VIs have the potential to provide a more accurate LAI estimation for heterogeneous canopy in strong soil-noise interference area.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
