Object-based image segmentation techniques are widely utilised in environmental disciplines to partition remotely sensed imagery into objects representing distinct conditions such as vegetation structure or landform. However, most approaches are applied to a single temporal snapshot, limiting their ability to update polygons over time. To address this, we proposed a temporally consistent segmentation algorithm based on a two-phase region growing approach designed to be applied to time series of annual Landsat surface reflectance composites. We developed and demonstrated this new approach over six fire-disturbed forested study areas in British Columbia, Canada, to dynamically delineate polygons over time as they underwent land cover change. Our approach maintained existing boundaries for forest polygons with no land cover change while updating those subject to change as forest regenerated and followed successional processes. Rapidly recovering areas such as Cariboo and Fraser-Fort George showed increases in mean segment area from 12 to 21 ha and 14 to 25 ha, respectively, approaching or exceeding pre-disturbance values. Additionally, segment shape complexity increased over time, reflecting the structural development of recovering stands. This work demonstrated the potential of utilizing Landsat surface reflectance data to update forest polygons over time with reference to forest development and increasing maturity.

Hyperion imagery, that has been and still is used for numerous studies, needs to undergo several preprocessing steps, including atmospheric correction, for the bottom of atmosphere reflectance to be retrieved. While multiple algorithms have been presented and used in specific studies, they may not be adapted to the user's needs or easy to implement. In this paper, a publicly available python package (SUREHYP) dedicated to the preprocessing of Hyperion data, implementing previously developed approaches, is presented. Its performances concerning atmospheric correction are compared to those of two other algorithms (FLAASH and QUAC) by examining the differences between predicted reflectances from four radiance images and their associated reflectances as delivered by NASA JPL. The results suggest that the atmospheric correction algorithm of SUREHYP and FLAASH present similar performances and outperform QUAC concerning the reflectance retrieval accuracy. Further work is needed to take topography and adjacency effects into account in SUREHYP.

Unmanned aerial vehicles (UAVs) and deep learning are important tools at the forefront of automated forest monitoring research, where classification of individual tree species is a critical forest management goal. Near-infrared (NIR) information provided by specialized UAV sensors may improve classification accuracy at the cost of added operational complexity; however, this potential for improvement is context-dependent and, therefore, may not be necessary. We assessed the performance of conventional red-green-blue (RGB) versus NIR imagery when classifying regenerating lodgepole pine and white spruce crowns automatically delineated by a trained deep learning algorithm. Models trained on NIR imagery slightly outperformed those trained on RGB imagery. Models trained on spectral bands outperformed those trained on spectral indices. The minor difference in performance between the two sets of imagery showed that accurate classification of lodgepole pine and white spruce can be carried-out using conventional RGB imagery.

Monitoring forest dynamics is of critical importance for both sustainable forest management and conservation purposes. Light detection and ranging (lidar) data provide a detailed representation of the 3-D structure of forest stands that can be used to analyze a number of trees and stand characteristics. Recently, multiple lidar acquisitions over the same area are becoming more common allowing changes in stand attributes to be assessed over time. In order to effectively utilize such multitemporal data sets for forest dynamics monitoring, we propose a method for unsupervised change detection (CD) of lidar data based on polar change vector analysis (CVA). The proposed method involves extracting relevant lidar point cloud metrics for a given area over time. Pixel-wise difference vectors of the metrics are then converted from Cartesian to polar coordinates to represent the magnitude and direction of change. Finally, the change vectors are analyzed in the polar domain to automatically discriminate between the different classes of change. The method is applied to a multitemporal lidar data set of coniferous forest on Vancouver Island, British Columbia, Canada, impacted by various types of land cover change. The experimental results demonstrate that the proposed method is capable of automatically discriminating between different classes of lidar change.

This paper presents a Change Detection method for bitemporal Light Detection And Ranging (LiDAR) data based on Change Vector Analysis in the polar domain. The method first extracts a suite of LiDAR metrics from the two LiDAR point clouds using a 2-D grid based approach. Second, it transforms the change in these metrics into a polar representation to examine variations in terms of magnitude and direction. The analysis of the magnitude discriminates between small magnitude changes or unchanged areas and areas affected by large disturbances related to forest removal. The analysis of the direction of change allows us to identify dominant directions to discriminate between the various types of forest change. The method has been tested on a multitemporal dataset acquired in a high productivity evergreen conifer forest in British Columbia, Canada. Experimental results indicated that the method effectively discriminates between the different types of forest change trough the analysis of the change direction.

In this study, we prototype the combination of samples of airborne LiDAR (LiDAR plots) and Landsat data to characterize the development of forest structure attributes through time. A nearest neighbor imputation model was developed using predictors generated from wall-to-wall Landsat best available pixel (BAP) composites and reference measurements of forest structure derived from LiDAR plots. The imputation model was then applied through time on a study area in Canada's boreal forest, resulting in forest structure maps with a 30 m resolution for the period 1984-2012. We characterize post-disturbance trends in these forest structural metrics following wildfire and harvest and offer insights on the large-area, temporally dense mapping opportunities offered by the synergistic use of samples of airborne LiDAR and Landsat BAP composites.

Light use efficiency (LUE) models offer an effective way for regional gross primary productivity (GPP) estimation. However, LUE is not easily determined at the landscape level due to its complexity and dependence on various environmental factors. One possible strategy to avoid the requirement for assessing environmental stressors is using the photochemical reflectance index (PRI) to determine LUE via the epoxidation state of the xanthophyll cycle. Integration of such measurements into GPP models could lead to more realistic GPP estimates of landscape level. Conventional, “one-leaf” LUE models, however, seem less suitable for integration of such remote sensing observations, as optically derived estimates are dependent on the shadow fraction viewed at a given time. Here, we utilize the two-leaf LUE (TL-LUE) model to parameterize LUE from multiangle PRI observations and compare it with MOD17 approach. Significant relationships were found between LUE (LUE, LUEsun, and LUEshaded) and PRI (PRI, PRIsιn, and PRIshaded) over 8and 16-day time steps. Similarly, R values for the relationships between modeled GPP and observed GPP (EC derived measurements of GPP) were 0.87 (TL-LUE) and 0.81 (MOD17) at deciduous forest and 0.54 (TL-LUE) and 0.46 (MOD17) at evergreen forest for eight-day periods, as well as 0.84 (TL-LUE) and 0.74 (MOD17) at deciduous forest and 0.49 (TL-LUE) and 0.46 (MOD17) at evergreen forest for 16-day periods. Our results are relevant when planning potential future satellite missions to help constrain existing GPP models using remotely sensed data, as such observations will likely be affected by canopy shading effects at the time of observation.

The objective of this paper is to gain insights into the reproducibility of light detection and ranging (LiDAR)-derived vegetation metrics for multiple acquisitions carried out on the same day, where we can assume that forest and terrain conditions at a given location have not changed. Four overlapping lines were flown over a forested area in Vancouver Island, British Columbia, Canada. Forty-six 0.04-ha plots were systematically established, and commonly derived variables were extracted from first and last returns, including height-related metrics, cover estimates, return intensities, and absolute scan angles. Plot-level metrics from each LiDAR pass were then compared using multivariate repeated-measures analysis-of-variance tests. Results indicate that, while the number of returns was significantly different between the four overlapping flight lines, most LiDAR-derived first return vegetation height metrics were not. First return maximum height and overstory cover, however, were significantly different and varied between flight lines by an average of approximately 2% and 4%, respectively. First return intensities differed significantly between overpasses where sudden changes in the metric occurred without any apparent explanation; intensity should only be used following calibration. With the exception of the standard deviation of height, all second return metrics were significantly different between flight lines. Despite these minor differences, the study demonstrates that, when the LiDAR sensor, settings, and data acquisition flight parameters remain constant, and time-related forest dynamics are not factors, LiDAR-derived metrics of the same location provide stable and repeatable measures of the forest structure, confirming the suitability of LiDAR for forest monitoring.

Hyperspectral reflectance data collected diurnally from an instrumented tower were examined in conjunction with the eddy correlation fluxes and meteorological measurements made throughout a growing season at a mature Douglas fir forest in British Columbia, Canada (DF49). Here we present 2006 in situ results relating the Photochemical Reflectance Index (PRI551) to photosynthetic light use efficiency (LUE). Canopy structure information was used to partition the forest canopy into sunlit and shaded fractions. At each observation period, the PRI551was examined for the sunlit, shaded, and mixed sunlit/shaded canopy segments as defined by their instantaneous position relative to the solar principal plane (SPP). We show that the PRI551clearly captures the differences in leaf groups on sunny days. We also examined PRI551from MODIS ocean band imagery acquired over DF49 during a five year period (2001-2006) from both Terra (late morning) and Aqua (early afternoon) platforms. The MODIS observations from Terra and Aqua were acquired in different viewing planes above the landscape over a range of view zenith angles, and sampled the backscatter (sunlit) and forward scatter (shaded) sectors of the forest's bidirectional reflectance distribution function. When tower-based bulk canopy LUE from 2006 was recalculated to estimate foliage-based values for the three foliage groups under their incident light environments, a strong linear relationship with PRI551was demonstrated (r2~ 0.80). A similar relationship between the MODIS PRI551and tower-based bulk LUE was obtained from satellite observations (r2~ 0.

Change detection approaches, such as computing change in spectral indexes through time, are a mature and established science, which is increasingly being applied in operational remote sensing programs. The quality and consistency of the changes detected using these approaches are linked, however, to the processing of the imagery required to address issues related to image radiometry, normalization, and computation of the spectral indexes. These processing steps are typically undertaken independently, providing opportunities for computation errors, increasing disk storage needs, and consuming processing time. In this letter, we present an approach for combining these processing steps to facilitate a more streamlined and computationally efficient approach to change detection using Landsat-5 and -7. The individual elements of the algorithm (raw Landsat-5 or -7, to calibrated Landsat-7, to top-of-atmosphere reflectance, to tasselled cap components) are described, followed by a description and illustration of the protocol to algebraically combine the elements. Rather than producing intermediate outputs, the sequentially integrated data processing protocol operates in memory and produces only the desired outputs. The proposed approach mitigates opportunities for inappropriate scaling between processing steps, the consistency of which is especially important for threshold-based change detection procedures. In addition, savings in both processing time and disk storage are afforded through the combination of processing steps, with processing of the time-1 images reduced from three to two stages and five to two stages for the time-2 images, resulting in savings of 50% and 69% in computing times and disk space requirements, respectively

Within Australian Pinus radiata plantations a diverse range of damaging agents are present. A significant issue is the presence of a fungal pathogen Sphaeropsis sapinea which is present in many softwood plantations. In this research we investigate the use of CASI-2 imagery to detect Sphaeropsis sapinea infestation using linear spectral mixture analysis approaches. Results indicate that four fraction endmember images could be reliably extracted from the 12 channel CASI-2 imagery with sunlit canopy, soil, shadow, and nonphotosynthetic vegetation (NPV) all well estimated. Using multiple linear stepwise regression, models were developed using mixed fractional abundances with model predictions found to be highly significant. The NPV and shadow endmembers, in order, were consistently identified as important in the regression models and confirm their importance in crown condition modelling.

Hyperspectral remote sensing methods are advancing rapidly and offer the promise of estimation of pigment, biochemical, and water content dynamics. The recent Earth Observer 1 (EO-1) Hyperion mission, and associated field campaigns, has allowed a range of biophysical and biochemistry attributes of eucalypt foliage to be analyzed in conjunction with remotely sensed spectra. This paper reports on a study at Tumbarumba (Bago-Maragle State Forest), Australia, which has a wide variety of eucalypt species, ranging in productivity and age. EO-1 Hyperion imagery was obtained in April 2001, and a field program was undertaken involving the establishment of plots, collection of standard forestry inventory data, and green leaf samples. Leaf nitrogen (N) content was measured from leaf samples using wet chemistry techniques and canopy N concentration estimated using leaf mass and proportional species leaf area index data. A number of models were developed from Hyperion reflectance, absorbance, and derivate transformations using partial least squares regression and multiple linear regression. The most significant calibration model predicted N with a correlation coefficient (r)=0.9 (82% variance explained) and a validation r/sup 2/=0.62 (P<0.01). The standard error of the estimate of foliar N was 0.16% equating to 13% of the mean observed %N at the site. These initial results indicate that predictions of canopy foliar N using Hyperion spectra is possible for native multispecies eucalypt forest. Similar studies worldwide, particular those associated with the flux tower network, will allow these findings to be placed in context with other biomes and functional types.

Hyperspectral remote sensing methods are advancing rapidly and offer the promise of estimating canopy pigment, bio-chemistry and water content dynamics, which can in turn be linked to carbon assimilation, forest growth and photosynthetic capacity models. The recent EO-1 HYPERION mission and associated field campaigns with ground based spectra and HYMAP airborne 5m hyperspectral imagery has allowed foliage bio-physical and bio-chemistry variables of eucalypt vegetation to be analyzed in conjunction with remotely sensed spectra. The paper will report on the prediction of eucalypt leaf bio-chemistry from ground based, airborne and satellite spectra and detail the approaches used to extrapolate these results from individual leaves to regional scales to allow estimates of the carbon cycle to be made across the landscape using combinations of inverse modeling and remote sensing. The paper details results from eucalypt forest near Tumbarumba (Bago-Maragle State Forest), Australia which has a number of eucalypt species, ranging in productivity and age.

In absence of other limitations, the growth rate of a plant is dependent upon the amount of heat it receives. Each species, whether a crop, weed or disease organism, is adapted to grow at its optimum rate within a specific temperature range. Within this range, the growing degree days (GDD) is the heat accumulation above a given base temperature for a specific time period, such as a crop's growing season or phenological stage. In this paper we detail a methodology to predict GDD for synthetically generated average growing seasons derived from long term average climate data over the Australian continent. An application of these techniques has been made using the GEODATA 9 second DEM, with temperature threshold values estimated to characterize optimum growth in citrus (Citrus sinensis (L.) Osbeck). Three major determinants of the annual growth cycle of Citrus sp. were established and predicted on a spatial basis including the starting day of the growing season, the GDD for a growing season, and the time required to accumulate an arbitrarily selected 2000 GDD from the estimated starting day. When these critical environmental factors are expressed on a spatial basis, covering the Australian continent, the combination can be used to identify locations where new crop varieties can most effectively be grown to maximize fruit quality and productivity, or to extend the harvest season. Likewise, new germplasm introduced to Australia from overseas can be horticulturally assessed at sites climatically matched to the source location.