Free download. Book file PDF easily for everyone and every device. You can download and read online The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) book. Happy reading The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) Bookeveryone. Download file Free Book PDF The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) Pocket Guide.
Accessibility Quick Links

We validated this map with a field-based forest degradation typology built on canopy height and structure observations. We conclude that the modelling framework developed here combined with high-resolution vegetation status indicators can help improve the management of degraded forests at the regional scale. In the western part of the municipality stripped area , Sentinel-1 data was not available.

The selective logging area and the logging road were validated during the field trip. Considering the temporal resolution and data acquisition costs, digital [ Forest aboveground biomass AGB and leaf area index LAI are two important parameters for evaluating forest growth and health. Considering the temporal resolution and data acquisition costs, digital aerial photographs DAPs from a digital camera mounted on an unmanned aerial vehicle or light, small aircraft have been widely used in forest inventory.

In this study, the aerial photograph data was acquired on 5 and 9 June, by a Hasselblad60 digital camera of the CAF-LiCHy system in a Y-5 aircraft in the Mengjiagang forest farm of Northeast China, and the digital orthophoto mosaic DOM and photogrammetric point cloud PPC were generated from an aerial overlap photograph. Forest vertical structure features and canopy cover were extracted from normalized PPC. A recursive feature elimination RFE method using a random forest was used for variable selection. Four different machine-learning ML algorithms random forest, k-nearest neighbor, Cubist and supporting vector machine were used to build regression models.

Yellow locations are the sites of Larch plots in Dark blue locations are the sites of Larch plots in Light blue locations are the sites of Scots pine plots. Purple locations are the sites of Korean pine plots. Simultaneous GPS observations are collected with one receiver in the open [ Simultaneous GPS observations are collected with one receiver in the open and three inside a forest.

Recommended for you

Comparing the GPS SNRs observed in the forest to those observed in the open allows for a rapid determination of signal loss. This study includes data from 15 forests and includes two forests with inter-seasonal data.

The results support the use of the CCPM for individual forests but suggest that an initial calibration is needed for a location and time of year due to different absorption characteristics. Dark colored rings indicate a high level of canopy closure and white rings indicate less forest media. The polarization coherence tomography PCT technology which can generate [ The polarization coherence tomography PCT technology which can generate vertical profiles of forest relative reflectivity, has the potential to improve the accuracy of biomass inversion.

The relationship between vertical profiles and forest AGB is modeled by some parameters defined based on geometric characteristics of the relative reflectivity distribution curve. But these parameters are defined without physical characteristics. Among these parameters, tomographic height TomoH is considered as the most important one. However, TomoH only corresponds to the highest volume relative reflectivity, which is lower than the actual forest height, affecting the accuracy of forest height and AGB inversion.

In this paper, we introduce a new parameter, the canopy height H ac , for AGB inversion by analyzing the vertical backscatter power loss. Then, we construct an inversion model based on the combination of the new parameter H ac and other parameters from the tomographic profile.

The inversion root mean square error RMSE of the proposed method is The red polygons indicate the forest stands. The green lines denote the LiDAR height. PD: phase diversity.

Remote Sensing and Ecological Modelling

Abstract Estimating clumping indices is important for determining the leaf area index LAI of forest canopies. The spatial distribution of the clumping index is vital for LAI estimation.

Application of Remote Sensing in Forestry

However, the neglect of woody tissue can result in biased clumping index estimates when indirectly deriving [ Estimating clumping indices is important for determining the leaf area index LAI of forest canopies. However, the neglect of woody tissue can result in biased clumping index estimates when indirectly deriving them from the gap probability and LAI observations. It is difficult to effectively and automatically extract woody tissue from digital hemispherical photos. Between-crown and within-crown gaps from TLS data were separated to calculate the clumping index.

Sharing data for improved forest protection and monitoring

Subsequently, we analyzed the gap probability, clumping index, and LAI estimates based on TLS and HemiView data in consideration of woody tissue trunks. These results also showed that the detection of woody tissue was more helpful for the estimation of clumping index distribution. Moreover, the angular distribution of the clumping index is more important for the LAI estimate than the average clumping index value.

We concluded that woody tissue should be detected for the clumping index estimate from TLS data, and 3D information could be used for estimating the angular distribution of the clumping index, which is essential for highly accurate LAI field measurements. The sixteen white solid crosses represent the locations of the centers of the subplots and the thirteen red solid circles represent the locations at which data were measured. The black areas indicate where no points were recorded. The center of the circle denotes the position of the laser scan; the green curve denotes the cross section of the trunk surface; and the black zone denotes no point cloud; top view.

Abstract The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. Despite innovative advantages [ The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. Despite innovative advantages on high-precision satellites, data acquisition is not yet available to the public at a reasonable cost. Unmanned aerial vehicles UAVs have the practical advantage of data acquisition at a higher spatial resolution than that of satellites.

This study is divided into two main parts: 1 we describe the estimation of basic tree attributes, such as tree height, crown diameter, diameter at breast height DBH , and stem volume derived from UAV data based on structure from motion SfM algorithms; and 2 we consider the extrapolation of the UAV data to a larger area, using correlation between satellite and UAV observations as an economically viable approach. Results have shown that UAVs can be used to predict tree characteristics with high accuracy i.

Source: Google Earth elaborated work in ArcGis for higher map detail. The medians of the measured values are marked by vertical lines inside the boxes. The ends of the boxes are the interquartile range upper: Q3 and lower: Q1. The whiskers show the highest and lowest observations. We also quantify uncertainty of the product by utilizing available ground measurements. For both consistency and uncertainty evaluation, we account for variations in biome type and temporal resolution.

Remote Sensing and Ecological Modelling - Helmholtz-Centre for Environmental Research

It is noteworthy that the rate of retrievals from the radiative transfer-based main algorithm is also comparable between two sensors. However, a relatively larger discrepancy over tropical forests was observed due to reflectance saturation and an unexpected interannual variation of main algorithm success was noticed due to instability in input surface reflectances. Therefore, more ground measurements are needed to achieve a more comprehensive evaluation result of product uncertainty.

An equal-area sinusoidal projection is used here. The solid black line is the linear fit for all pixels and the colorbar shows the density of pixels falling at each grid. The eight biomes are B1 grasses and cereal crops, B2 shrubs, B3 broadleaf crops, B4 savannas, B5 evergreen broadleaf forest, B6 deciduous broadleaf forest, B7 evergreen needleleaf forest, B8 deciduous needleleaf forest.

The blue and black solid lines represent the overall fit and line, respectively. The red dashed lines show the GCOS specifications boundaries max 0. Abstract Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants.

Leaf area index LAI can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure [ Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants. Leaf area index LAI can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure assessments that use them. LAI estimates for minimally managed or natural forest stands can be particularly difficult to develop as a result of interspecies competition, age and spatial distribution.

Satellite-based LAI estimates hold promise for retrospective analyses, but we must continue to rely on numerical models for alternative management analysis.

Support Faculty:

Measurements of forest composition species and number , LAI, tree diameter, basal area, and canopy height were recorded at each site during the field season. We close by illustrating the extension of this site-level approach to scales that could support regional air quality model simulations.


  • Accessibility Quick Links!
  • Lead Faculty:!
  • The Use of Remote Sensing in the Modeling of Forest Productivity.
  • Information!
  • A Blizzard Wedding!

Abstract The methods for measuring vegetation cover in Mexican forest surveys are subjective and imprecise. The objectives of this research were to compare the sampling designs used to measure the vegetation cover and estimate the over and understory cover in different land uses, using [ The methods for measuring vegetation cover in Mexican forest surveys are subjective and imprecise. The objectives of this research were to compare the sampling designs used to measure the vegetation cover and estimate the over and understory cover in different land uses, using digital photography.

The study was carried out in circular sampling sites in central Mexico. Four spatial sampling designs were evaluated in three spatial distribution patterns of the trees. The image processing was performed using threshold segmentation techniques and was automated through an application developed in the Python language.

The two proposed methods to estimate vegetation cover through digital photographs were robust and replicable in all sampling plots with different land uses and different illumination conditions. The automation of the process avoided human estimation errors and ensured the reproducibility of the results.

This method is working for regional surveys and could be used in national surveys due to its functionality.