Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequality

Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequalit

Diversity and Equitability Ordering Profiles Applied to the Study of Forest Structure

Diversity and Equitability Ordering Profiles Applied to the Study of Forest Structur

Cartography of uncertainty of predicted forest variables estimated from ALS data

cartografía de incertidumbre

Comparison of Airborne Laser Scanning Methods for Estimating Forest Structure Indicators Based on Lorenz Curves

The purpose of this study was to compare a number of state-of-the-art methods in airborne laser scan- ning (ALS) remote sensing with regards to their capacity to describe tree size inequality and other indi- cators related to forest structure. The indicators chosen were based on the analysis of the Lorenz curve: Gini coefficient ( GC ), Lorenz asymmetry ( LA ), the proportions of basal area ( BALM ) and stem density ( NSLM ) stocked above the mean quadratic diameter. Each method belonged to one of these estimation strategies: (A) estimating indicators directly; (B) estimating the whole Lorenz curve; or (C) estimating a complete tree list. Across these strategies, the most popular statistical methods for area-based approach (ABA) were used: regression, random forest (RF), and nearest neighbour imputation. The latter included distance metrics based on either RF (NN–RF) or most similar neighbour (MSN). In the case of tree list esti- mation, methods based on individual tree detection (ITD) and semi-ITD, both combined with MSN impu- tation, were also studied. The most accurate method was direct estimation by best subset regression, which obtained the lowest cross-validated coefficients of variation of their root mean squared error CV(RMSE) for most indicators: GC (16.80%), LA (8.76%), BALM (8.80%) and NSLM (14.60%). Similar figures [CV(RMSE) 16.09%, 10.49%, 10.93% and 14.07%, respectively] were obtained by MSN imputation of tree lists by ABA, a method that also showed a number of additional advantages, such as better distributing the residual variance along the predictive range. In light of our results, ITD approaches may be clearly inferior to ABA with regards to describing the structural properties related to tree size inequality in for- ested areas

Mapping wood production in European forests

Wood production is an important forest use, impacting a range of other ecosystem services. However, information on the spatial patterns in wood production is limited and often available only for larger administrative units. In this study, we developed high-resolution wood production maps for European forests. We collected wood production statistics for 29 European countries from 2000 to 2010, as well as comprehensive sets of biophysical and socioeconomic location factors. We used regression analyses to produce maps indicating the harvest likelihood on a 1 × 1 km2 grid. These likelihood maps were validated using national forest inventory plot data. We then disaggregated wood production statistics from larger administrative units to the grid level using the harvest likelihood as weights. We verified the resulting wood production maps by correlating predicted and observed wood production at the level of smaller administrative units not used for generating the wood production maps. We conclude that (i) productivity, tree species composition and terrain ruggedness are the most important location factors that determine the spatial patterns of wood production at the pan-European scale and that (ii) incorporating these location factors substantially improves the results of disaggregating wood production statistics compared to a disaggregation based on forest cover only. Our wood production maps give insight into forest ecosystem service provisioning and can be used to improve the assessment of potentials and costs of woody biomass supply

Mean height and variability of height derived from Lidar data and Landsat images relationship

The mean height and standard deviat ion of the height of the forest canopy, derived from lidar data show to be important variables to summarize forest st ructure. However lidar data has a limited spat ial extent and very high economic cost . Landsat data provide useful st ructural informat ion in the horizontal plane and have easy access. The integrat ion of both data sources is an interest ing goal for sustainable forest management. Different spect ral indices (NDVI and Tasseled Cap) were obtained from 3 Landsat scenes (March 2000, June 2001 and September 2001). In addit ion, mean and standard deviat ion of lidar height werecalculated in 30x30m blocks. Correlat ion and forward stepwise regression analysis was applied between these two variables sets. Best correlat ion coefficients are achieved among mean lidar height versus NDVI and wetness for the three dates (range between 0.65 to -0.73). Others authors indicate that wetness is one of the best spectral indices to characterize forest st ructure. Best regression models include NDVI and wetness of June and September as dependent variables (adjusted r2: 0.55 – 0.62). These results show that lidar data can be useful for training Landsat to map forest st ructure but it should be interest ing to opt imize this approach

Lidar and true-orthorectification of infrared aerial imagery of high Pinus sylvestris forest in mountainous relief

Combination of various data sources has been demonstrated more effective than using them separately. Information retrieval is significantly improved by synergies between laser scanner and optical imagery. Digital photography relies on traditional methods for orthorectification in order to accomplish an accurate correspondence with Lidar. We investigated combinatorial techniques in a high pine forest situated in mountainous relief in the Guadarrama Range (Spain). Results have shown critical inaccuracies in the integration of these data, even when obtained simultaneously. We propose the use of Lidar-derived Digital Surface Model in the process of orthorectification of aerial imagery. We hypothesised that the use of true-orthophoto techniques for improving the planimetric accuracy of VHR can be reliable for forestry applications. The methodology slightly improved the geometrical results obtained, though radiometric results might be meaningless. Consequently, other possible solutions are also discussed

LINHE Project: Development of new protocols for the integration of digital cameras and LiDAR, NIR and Hyperspectral sensors.

The LINHE project aims to develop applications for forest management based on the combined use of LiDAR data, images from spaceborne (multi and hyperspectral) and airborne sensors (panchromatic, colour, near infrared), and NIR field data from a portable sensor. The integration of the different types of data should be performed in a rapid, intuitive, cost-effective and dynamic way. In order to achieve this objective, new algorithms were developed and existing ones were tested, for the correlation of data collected in the field and those gathered by the different sensors. Specific software (LINHE prototype viewer) was developed to support data gathering and consultations, and it was tested in three different forest ecosystems, so as to validate the tool for forest management purposes. The optimisation of the synergic capabilities derived from the combined use of the different sensors will allow the enhancement of their efficiency and provide accurate information for operational forestry

Optimización de la Exactitud Geométrica al Integrar Diversas Fuentes de Datos en Proyectos de Inventario Forestal Basados en Escaneo Laser Aerotransportado=Optimizing Geometric Accuracy in Data Source Integration for Airborne Laser Scanning-based Forest Inventory Projects

La mayoría de las aplicaciones forestales del escaneo laser aerotransportado (ALS, del inglés airborne laser scanning) requieren la integración y uso simultaneo de diversas fuentes de datos, con el propósito de conseguir diversos objetivos. Los proyectos basados en sensores remotos normalmente consisten en aumentar la escala de estudio progresivamente a lo largo de varias fases de fusión de datos: desde la información más detallada obtenida sobre un área limitada (la parcela de campo), hasta una respuesta general de la cubierta forestal detectada a distancia de forma más incierta pero cubriendo un área mucho más amplia (la extensión cubierta por el vuelo o el satélite). Todas las fuentes de datos necesitan en ultimo termino basarse en las tecnologías de sistemas de navegación global por satélite (GNSS, del inglés global navigation satellite systems), las cuales son especialmente erróneas al operar por debajo del dosel forestal. Otras etapas adicionales de procesamiento, como la ortorectificación, también pueden verse afectadas por la presencia de vegetación, deteriorando la exactitud de las coordenadas de referencia de las imágenes ópticas. Todos estos errores introducen ruido en los modelos, ya que los predictores se desplazan de la posición real donde se sitúa su variable respuesta. El grado por el que las estimaciones forestales se ven afectadas depende de la dispersión espacial de las variables involucradas, y también de la escala utilizada en cada caso. Esta tesis revisa las fuentes de error posicional que pueden afectar a los diversos datos de entrada involucrados en un proyecto de inventario forestal basado en teledetección ALS, y como las propiedades del dosel forestal en sí afecta a su magnitud, aconsejando en consecuencia métodos para su reducción. También se incluye una discusión sobre las formas más apropiadas de medir exactitud y precisión en cada caso, y como los errores de posicionamiento de hecho afectan a la calidad de las estimaciones, con vistas a una planificación eficiente de la adquisición de los datos. La optimización final en el posicionamiento GNSS y de la radiometría del sensor óptico permitió detectar la importancia de este ultimo en la predicción de la desidad relativa de un bosque monoespecífico de Pinus sylvestris L. ABSTRACT Most forestry applications of airborne laser scanning (ALS) require the integration and simultaneous use of various data sources, pursuing a variety of different objectives. Projects based on remotely-sensed data generally consist in upscaling data fusion stages: from the most detailed information obtained for a limited area (field plot) to a more uncertain forest response sensed over a larger extent (airborne and satellite swath). All data sources ultimately rely on global navigation satellite systems (GNSS), which are especially error-prone when operating under forest canopies. Other additional processing stages, such as orthorectification, may as well be affected by vegetation, hence deteriorating the accuracy of optical imagery’s reference coordinates. These errors introduce noise to the models, as predictors displace from their corresponding response. The degree to which forest estimations are affected depends on the spatial dispersion of the variables involved and the scale used. This thesis reviews the sources of positioning errors which may affect the different inputs involved in an ALS-assisted forest inventory project, and how the properties of the forest canopy itself affects their magnitude, advising on methods for diminishing them. It is also discussed how accuracy should be assessed, and how positioning errors actually affect forest estimation, toward a cost-efficient planning for data acquisition. The final optimization in positioning the GNSS and optical image allowed to detect the importance of the latter in predicting relative density in a monospecific Pinus sylvestris L. forest

Youth unemployment in Western Germany Facts, causes, cures. Uberarbeitete Fassung

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