Texture augmented detection of macrophyte species using decision trees

Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 × 5 and 13 × 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier.     

Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach

Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = −0.83 for the fir and r = −0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.     

Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery

Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales.     

Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data

Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R² = 0.648, RMSE = 167.546 g/m², RMSE_r = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation.     

Quantifying determinants of cash crop expansion and their relative effects using logistic regression modeling and variance partitioning

Cash crop expansion has been a major land use change in tropical and subtropical regions worldwide. Quantifying the determinants of cash crop expansion should provide deeper spatial insights into the dynamics and ecological consequences of cash crop expansion. This paper investigated the process of cash crop expansion in Hangzhou region (China) from 1985 to 2009 using remotely sensed data. The corresponding determinants (neighborhood, physical, and proximity) and their relative effects during three periods (1985–1994, 1994–2003, and 2003–2009) were quantified by logistic regression modeling and variance partitioning. Results showed that the total area of cash crops increased from 58,874.1 ha in 1985 to 90,375.1 ha in 2009, with a net growth of 53.5%. Cash crops were more likely to grow in loam soils. Steep areas with higher elevation would experience less likelihood of cash crop expansion. A consistently higher probability of cash crop expansion was found on places with abundant farmland and forest cover in the three periods. Besides, distance to river and lake, distance to county center, and distance to provincial road were decisive determinants for farmers’ choice of cash crop plantation. Different categories of determinants and their combinations exerted different influences on cash crop expansion. The joint effects of neighborhood and proximity determinants were the strongest, and the unique effect of physical determinants decreased with time. Our study contributed to understanding of the proximate drivers of cash crop expansion in subtropical regions.     

利用骨架线端点匹配进行面状要素渐变变换

面向空间数据连续地图综合问题,提出了一种基于骨架线端点匹配的面状要素渐变方法,通过在两个关键表达之间进行尺度内插,实时、动态地派生任意中间比例尺地图数据。首先,对面状要素在大小比例尺下的两重表达分别进行约束Delaunay三角网剖分并提取各自的骨架线特征;然后,使用最优子序双射优化技术对骨架端点进行匹配获得多边形边界上相对应的特征点序列;最后,在剖分边界的基础上进行分段常规线性内插,获得面状要素介于始末尺度之间的多尺度表达。实验结果表明,该算法充分顾及了空间数据弯曲结构特征,对于光滑边界面状要素的渐变变换具有良好的渐变效果,可用于空间数据的连续地图综合和多尺度表达。     

Satellite-based automated burned area detection: A performance assessment of the MODIS MCD45A1 in the Brazilian savanna

Burnings, which cause major changes to the environment, can be effectively monitored via satellite data, regarding both the identification of active fires and the estimation of burned areas. Among the many orbital sensors suitable for mapping burned areas on global and regional scales, the moderate resolution imaging spectroradiometer (MODIS), on board the Terra and Aqua platforms, has been the most widely utilized. In this study, the performance of the MODIS MCD45A1 burned area product was thoroughly evaluated in the Brazilian savanna, the second largest biome in South America and a global biodiversity hotspot, characterized by a conspicuous climatic seasonality and the systematic occurrence of natural and anthropogenic fires. Overall, September MCD45A1 polygons (2000–2012) compared well to the Landsat-based reference mapping (r² = 0.92) and were closely accompanied, on a monthly basis, by MOD14 and MYD14 hotspots (r² = 0.89), although large omissions errors, linked to landscape patterns, structures, and overall conditions depicted in each reference image, were observed. In spite of its spatial and temporal limitations, the MCD45A1 product proved instrumental for mapping and understanding fire behavior and impacts on the Cerrado landscapes.     

Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.     

Very high spatial resolution optical and radar imagery in tracking water level fluctuations of a small inland reservoir

Tracking water level fluctuations in small lakes and reservoirs is important in order to better understand and manage these ecosystems. A geographic object-based image analysis (GEOBIA) method using very high spatial and temporal resolution optical (Pléiades) and radar (COSMO-SkyMed and TerraSAR-X) remote sensing imagery is presented here which (1) tracks water level fluctuations via variations in water surface area and (2) avoids common difficulties found in using single-band radar images for water-land image classification. Results are robust, with over 98% of image surface area correctly classified into land or water, R² = 0.963 and RMSE = 0.42 m for a total water level fluctuation range of 5.94 m. Multispectral optical imagery is found to be more straightforward in producing results than single-band radar imagery, but the latter crucially increase temporal resolution to the point where fluctuations can be satisfactorily tracked in time. Moreover, an analysis suggests that high and medium spatial resolution imagery is sufficient, in at least some cases, in tracking the water level fluctuations of small inland reservoirs. Finally, limitations of the methodology presented here are briefly discussed along with potential solutions to overcome them.     

Aboveground biomass mapping in French Guiana by combining remote sensing,forest inventories and environmental data

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter- and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R² = 0.54, RMSE = 48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain “wall-to-wall” AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ∼50 Mg/ha and R² = 0.66 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.     

Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years

Global climate change has led to significant vegetation changes in the past half century. North China Plain, the most important grain production base of china, is undergoing a process of prominent warming and drying. The vegetation coverage, which is used to monitor vegetation change, can respond to climate change (temperature and precipitation). In this study, GIMMS (Global Inventory Modelling and Mapping Studies)-NDVI (Normalized Difference Vegetation Index) data, MODIS (Moderate-resolution Imaging Spectroradiometer) – NDVI data and climate data, during 1981–2013, were used to investigate the spatial distribution and changes of vegetation. The relationship between climate and vegetation on different spatial (agriculture, forest and grassland) and temporal (yearly, decadal and monthly) scales were also analyzed in North China Plain. (1) It was found that temperature exhibiting a slight increase trend (0.20 °C/10a, P < 0.01). This may be due to the disappearance of 0 °C isotherm, the rise of spring temperature. At the same time, precipitation showed a significant reduction trend (−1.75 mm/10a, P > 0.05). The climate mutation period was during 1991–1994. (2) Vegetation coverage slight increase was observed in the 55% of total study area, with a change rate of 0.00039/10a. Human activities may not only accelerate the changes of the vegetation coverage, but also c effect to the rate of these changes. (3) Overall, the correlation between the vegetation coverage and climatic factor is higher in monthly scale than yearly scale. The correlation analysis between vegetation coverage and climate changes showed that annual vegetation coverage was better correlatend with precipitation in grassland biome; but it showed a better correlated with temperature i the agriculture biome and forest biome. In addition, the vegetation coverage had sensitive time-effect respond to precipitation. (4) The vegetation coverage showed the same increasing trend before and after the climatic variations, but the rate of increase slowed down. From the vegetation coverage point of view, the grassland ecological zone had an obvious response to the climatic variations, but the agricultural ecological zones showed a significant response from the vegetation coverage change rate point of view. The effect of human activity in degradation region was higher than that in improvement area. But after the climate abruptly changing, the effect of human activity in improvement area was higher than that in degradation region, and the influence of human activity will continue in the future.     

Resolution vs. image quality in pre-tsunami imagery used for tsunami impact models in Aceh,Indonesia

Land cover roughness coefficients (LCRs) have been used in multivariate spatial models to test the mitigation potential of coastal vegetation to reduce impacts of the 2004 tsunami in Aceh, Indonesia. Previously, a Landsat 2002 satellite imagery was employed to derive land cover maps, which were then combined with vegetation characteristics, i.e., stand height, stem diameter and planting density to obtain LCRs. The present study tested LCRs extracted from 2003 and 2004 Landsat (30 m) images as well as a combination of 2003 and 2004 higher spatial resolution SPOT (10 m) imagery, while keeping the previous vegetation characteristics. Transects along the coast were used to extract land cover, whenever availability and visibility allowed. These new LCRs applied in previously developed tsunami impact models on wave outreach, casualties and damages confirmed previous findings regarding distance to the shoreline as a main factor reducing tsunami impacts. Nevertheless, the models using the new LCRs did not perform better than the original one. Particularly casualties models using 2002 LCRs performed better (δAIC > 2) than the more recent Landsat and SPOT counterparts. Cloud cover at image acquisition for Landsat and low area coverage for SPOT images decreased statistical predictive power (fewer observations). Due to the large spatial heterogeneity of tsunami characteristics as well as topographic and land-use features, it was more important to cover a larger area. Nevertheless, if more land cover classes would be referenced and high resolution imagery with low cloud cover would be available, the full benefits of higher spatial resolution imagery used to extract more precise land use roughness coefficients could be exploited.     

A computational framework for generalized moving windows and its application to landscape pattern analysis

Land cover products based on remotely sensed data are commonly investigated in terms of landscape composition and configuration; i.e. landscape pattern. Traditional landscape pattern indicators summarize an aspect of landscape pattern over the full study area. Increasingly, the advantages of representing the scale-specific spatial variation of landscape patterns as continuous surfaces are being recognized. However, technical and computational barriers hinder the uptake of this approach. This article reduces such barriers by introducing a computational framework for moving window analysis that separates the tasks of tallying pixels, patches and edges as a window moves over the map from the internal logic of landscape indicators. The framework is applied on data covering the UK and Ireland at 250 m resolution, evaluating a variety of indicators including mean patch size, edge density and Shannon diversity at window sizes ranging from 2.5 km to 80 km. The required computation time is in the order of seconds to minutes on a regular personal computer. The framework supports rapid development of indicators requiring little coding. The computational efficiency means that methods can be integrated in iterative computational tasks such as multi-scale analysis, optimization, sensitivity analysis and simulation modelling.     

Estimating crop net primary production using national inventory data and MODIS-derived parameters

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m⁻² yr⁻¹ and 409 g C m⁻² yr⁻¹, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes

Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLST) derived from the Landsat ETM + thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use land cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m, 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The results indicate that wavelengths centered around 457, 671, 1488 and 2013–2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity.     

PTrees: A point-based approach to forest tree extraction from lidar data

This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (Z) and compute height (H = Z − ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segments extracted at the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10% of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections.     

Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)

Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps: (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness; (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Model (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangitikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. We also demonstrate that for high precision survey scanners, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases.     

Influences of forest roads and their edge effects on the spatial pattern of burn severity

Previous research has shown that forest roads are an important feature in many landscapes and have significant effects on wildfire ignition and cessation. However, forest road effects on burn severity have not been studied at the landscape level. Therefore, the overarching goal of our study is to identify the influences of road edge effects on the spatial patterns of burn severity. We analyzed six fires within the Okanogan–Wenatchee National Forest on the eastern slope of the Cascades mountain range of central Washington.We generated two categories for assessing road variables: (1) Primary Road Effect Zone (area within 150 m of the nearest road) and (2) Secondary Road Effect Zone (area from 150 m to 300 m to the nearest road). A regular sampling grid including one out of every 9 cells was created for each fire.These grids were intersected with burn severity data in the form of the Relative Differenced Normalized Burn Ratio (RdNBR), road distance category, stream distance, elevation, slope, terrain shape index, heat load index, canopy cover, and fuel type. We fit spatial regression models with RdNBR as the dependent variable.We found that high burn severity is less likely to occur in the Primary Road Effect Zone for most fires, although one fire exhibited the opposite relationship. Forest road edge effects were hypothesized to be an important determinant of burn severity because fragmentation created by roads alters the roadside fuel profile and environment and because road corridors create barriers to fire spread. Recognizing roadside effects on burn severity patterns highlights the need for further study of the range of effects that roads have on fuels and the fire environment and the potential for incorporating road effects into landscape-level assessments of fire risk.     

基于空间连续数据的小流域景观格局破碎化研究

基于空间连续数据，采用局部空间关联指标（LISA）——局部Moran指数（Local Moran Index, LMI），通过探测小流域内景观均质性和异质性的变化情况来反映景观格局破碎化的变化过程。作为一种空间明确的景观格局研究方法，LMI能够发现流域景观格局变化过程中的热点地区，并分析其与流域土地利用变化之间的联系，明确了土地利用变化是引起小流域景观格局变化的最主要的驱动因素。研究表明，基于空间连续数据的局部空间关联指标方法可以作为传统景观格局变化研究方法的有益补充。