The seasonality of sunlight and rainfall regulates net primary production in tropical forests. Previous studies have suggested that light is more limiting than water for tropical forest productivity, consistent with greening of Amazon forests during the dry season in satellite data. We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area or leaf reflectance, using a sophisticated radiative transfer model and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability.

Purpose of Review The adoption of Structure from Motion photogrammetry (SfM) is transforming the acquisition of three-dimensional (3D) remote sensing (RS) data in forestry. SfM photogrammetry enables surveys with little cost and technical expertise. We present the theoretical principles and practical considerations of this technology and show opportunities that SfM photogrammetry offers for forest practitioners and researchers. Recent Findings Our examples of key research indicate the successful application of SfM photogrammetry in forestry, in an operational context and in research, delivering results that are comparable to LiDAR surveys. Reviewed studies have identified possibilities for the extraction of biophysical forest parameters from airborne and terrestrial SfM point clouds and derived 2D data in area-based approaches (ABA) and individual tree approaches. Additionally, increases in the spatial and spectral resolution of sensors available for SfM photogrammetry enable forest health assessment and monitoring. The presented research reveals that coherent 3D data and spectral information, as provided by the SfM workflow, promote opportunities to derive both structural and physiological attributes at the individual tree crown (ITC) as well as stand levels. Summary We highlight the potential of using unmanned aerial vehicles (UAVs) and consumer-grade cameras for terrestrial SfM-based surveys in forestry. Offering several spatial products from a single sensor, the SfM workflow enables foresters to collect their own fit-for-purpose RS data. With the broad availability of non-expert SfM software, we provide important practical considerations for the collection of quality input image data to enable successful photogrammetric surveys. Keywords SfM. Point cloud. UAV. Close-range photogrammetry (CRP). Forest inventory. Forest health This article is part of the Topical Collection on Remote Sensing

Data from the Geoscience Laser Altimeter System (GLAS) aboard the Ice Cloud and land Elevation Satellite (ICESat) offer an unprecedented opportunity for canopy height retrieval at a regional to global scale. The data also provide useful information for forest stand level assessment at coincident locations. In this study height indices from light detection and ranging (LiDAR) waveforms were explored as a means of extracting canopy height; these were examined with reference to a mixed temperate forest in Gloucestershire, UK, containing planted stands with a mean age of 51 years and mean maximum height of 26.6 m. A method based on using a terrain index (TI; maximum minus minimum elevations from a 767 subset 10-m resolution digital terrain model (DTM)) to adjust the waveform extent (WE; signal begin minus signal end) produced an R 2 value of 0.89 when regressed against field measurements of maximum canopy height at footprint locations (field height50.91(WE2TI) + 4.86; root mean squared error (RMSE)52.99 m, coefficient significance p,0.001, intercept significance p.0.01). Multiple regression performed on both WE and TI with field measurements produced an R 2 of 0.90 and an RMSE of 2.86 m (field height51.0208WE20.7310TI; coefficient significance p,0.001, intercept not significant). Maximum canopy height estimates using an automated approach to ground return identification based on iterative fitting of Gaussian peaks (GP1_2 MAXAMP ) to the waveform explained 74% of variance when compared to field measurements (field height51.05(GP1_2 MAXAMP ); RMSE54.53 m, coefficient significance p,0.001, intercept not significant). The ability of satellite LiDAR to retrieve data for such a complex and diverse area further indicates the potential of this technique for both carbon accounting and forest management.