Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling

Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D virtual forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and derived biophysical products by helping us understand the assumptions made in data processing algorithms. We present a workflow that uses highly detailed 3D terrestrial laser scanning (TLS) data to generate virtual forests for RT model simulations. Our approach to forest stand reconstruction from a co-registered point cloud is unique as it models each tree individually. Our approach follows three steps: (1) tree segmentation; (2) tree structure modelling and (3) leaf addition. To demonstrate this approach, we present the measurement and construction of a one hectare model of the deciduous forest in Wytham Woods (Oxford, UK). The model contains 559 individual trees. We matched the TLS data with traditional census data to determine the species of each individual tree and allocate species-specific radiometric properties. Our modelling framework is generic, highly transferable and adjustable to data collected with other TLS instruments and different ecosystems. The Wytham Woods virtual forest is made publicly available through an online repository

Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling

Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D "virtual" forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and derived biophysical products by helping us understand the assumptions made in data processing algorithms. We present a workflow that uses highly detailed 3D terrestrial laser scanning (TLS) data to generate virtual forests for RT model simulations. Our approach to forest stand reconstruction from a co-registered point cloud is unique as it models each tree individually. Our approach follows three steps: (1) tree segmentation; (2) tree structure modelling and (3) leaf addition. To demonstrate this approach, we present the measurement and construction of a one hectare model of the deciduous forest in Wytham Woods (Oxford, UK). The model contains 559 individual trees. We matched the TLS data with traditional census data to determine the species of each individual tree and allocate species-specific radiometric properties. Our modelling framework is generic, highly transferable and adjustable to data collected with other TLS instruments and different ecosystems. The Wytham Woods virtual forest is made publicly available through an online repository

Leaf orientation and the spectral reflectance of field crops

Leaf angle distribution (LAD) is one of the most important parameters used to describe the structure of horizontally homogeneous vegetation canopies, such as field crops. LAD affects how incident photosynthetically active radiation is distributed on plant leaves, thus directly affecting plant productivity. However, the LAD of crops is difficult to quantify; usually it is assumed to be spherical. The purpose of this dissertation is to develop leaf angle estimation methods and study their effect on leaf area index (LAI) and chlorophyll a and b content (Cab) measured from optical observation. The study area was located in Viikki agricultural experimental field, Helsinki, Finland. Six crop species, faba bean, narrow-leafed lupin, turnip rape, oat, barley and wheat, were included in this study. A digital camera was used to take photographs outside the plot to record crop LAD. LAI and Cab were determined for each plot. Airborne imaging spectroscopy data was acquired using an AISA Eagle II imaging spectrometer covering the spectral range in visible and near-infrared (400 1000 nm). A recently developed method for the determination of leaf inclination angle was applied in field crops. This method was previously applied only to small and flat leaves of tree species. The error of LAI determination caused by the assumption of spherical LAD varied between 0 and 1.5 LAI units. The highest correlation between leaf mean tilt angle (MTA) and spectral reflectance was found at a wavelength of 748 nm. MTA was retrieved from imaging spectroscopy data using two algorithms. One method was to retrieve MTA from reflectance at 748 nm using a look-up table. The second method was to estimate MTA using the strong dependence of blue (479 nm) and red (663 nm) on MTA. The two approaches provide a new means to determine crop canopy structure from remote sensing data. LAI and MTA effects on Cab sensitive vegetation indices were examined. Three indices (REIP, TCARI/OSAVI and CTR6) showed strong correlations with Cab and similar performance in model-simulated and empirical datasets. However, only two (TCARI/OSAVI and CTR6) were independent from LAI and MTA. These two indices were considered as robust proxies of crop leaf Cab. Keywords: leaf angle; leaf area index; leaf chlorophyll; digital photograph; imaging spectroscopy; PROSAIL model; vegetation indice

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Measuring leaf angle distribution using terrestrial laser scanning in a European Beech forest

Leaf angle distribution (LAD) is an important canopy structure metric. It controls the flux of radiation, carbon and water, and has therefore been used in many radiative transfer, meteorological and hydrological models. However, LAD is too tedious to measure using conventional manual methods. Terrestrial laser scanning (TLS) has recently been proposed to estimate LAD due to its ability to record unprecedented detailed plant 3D structure. However, previous research was restricted to a controlled environment with simple canopy structure. In this research, TLS was used in a natural deciduous European beech forest to estimate LAD. Digital hemispherical photograph (DHP) was also used as a reference. The results demonstrated that both TLS and DHP could capture a variation of LAD in beech plots at different succession stages. Compared to DHP, TLS has the advantage of resolving foliar and woody materials, as well as deriving the 3D distribution of leaf angles

Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest

The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau

Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest

The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau