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This study proposes a new method (profile area change, PAC) to quantify fire-induced forest structural changes at the individual tree and clump of trees scales using pre- and post-fire LiDAR data. The PAC measures the difference in profile area summarized from pre- and post-fire LiDAR points. We applied the PAC method to assess the effects of the 2013 American Fire in the Sierra Nevada, California, USA. Our LiDAR PAC metrics were compared to changes in commonly used LiDAR-derived canopy cover and tree height metrics at tree level, and to Landsat-8 imagery-derived relative differenced normalized burn ratio (RdNBR) at the 30m pixel level. A quantitative validation using field measured changes in basal area and leaf area index (LAI) confirmed that correlations between PAC metrics and field measurements (R2 ≥ 0.67) were significantly higher than those from canopy cover or tree height metrics (R2 ≤ 0.43), and much stronger than that from RdNBR (R2 ≤ 0.26). The PAC metrics can also be used to infer the extent of tree canopy disturbance caused by fire, based on whether the majority of biomass loss occurred above or below the tree crown base height. Mapping of canopy disturbance indicated that over half (57.0%) of the American Fire region had tree canopy loss from fire, 22.5% of trees had sub-canopy loss, while the remaining area had no detectable tree canopy change. Overall, the LiDAR PAC metric, as a simple and integrated method, demonstrated promising potential in characterizing fine-grained changes in forest structure. The method can be beneficial for forest managers in evaluating fire-induced environmental and economic losses, and provide useful information for forest restoration design.

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush) at the Blue Oak Ranch Reserve (BORR) ~20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index), leaf water content and water potential as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit across treatments; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope).We found that PlanetScope data was able to capture the overall trend in treatments but unable to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments FTs may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources e.g. perennial streams, cultural resources, wildlife habitats. Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging lidar is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model CHM and canopy cover CC products derived from multi-temporal airborne laser scanning ALS data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest OBI segmentation method. We also investigated forest change using normalized difference vegetation index NDVI and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy 93.5% and kappa coefficient κ 0.70, and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products the total accuracy was 91.6%, and the κ was 0.59 was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre-and post-treatment ALS point clouds.

© 2018 Optical Society of America. Crown base height (CBH) is an essential tree biophysical parameter for many applications in forest management, forest fuel treatment, wildfire modeling, ecosystem modeling and global climate change studies. Accurate and automatic estimation of CBH for individual trees is still a challenging task. Airborne light detection and ranging (LiDAR) provides reliable and promising data for estimating CBH. Various methods have been developed to calculate CBH indirectly using regression-based means from airborne LiDAR data and field measurements. However, little attention has been paid to directly calculate CBH at the individual tree scale in mixed-species forests without field measurements. In this study, we propose a new method for directly estimating individual-tree CBH from airborne LiDAR data. Our method involves two main strategies: 1) removing noise and understory vegetation for each tree; and 2) estimating CBH by generating percentile ranking profile for each tree and using a spline curve to identify its inflection points. These two strategies lend our method the advantages of no requirement of field measurements and being efficient and effective in mixed-species forests. The proposed method was applied to a mixed conifer forest in the Sierra Nevada, California and was validated by field measurements. The results showed that our method can directly estimate CBH at individual tree level with a root-mean-squared error of 1.62 m, a coefficient of determination of 0.88 and a relative bias of 3.36%. Furthermore, we systematically analyzed the accuracies among different height groups and tree species by comparing with field measurements. Our results implied that taller trees had relatively higher uncertainties than shorter trees. Our findings also show that the accuracy for CBH estimation was the highest for black oak trees, with an RMSE of 0.52 m. The conifer species results were also good with uniformly high R2ranging from 0.82 to 0.93. In general, our method has demonstrated high accuracy for individual tree CBH estimation and strong potential for applications in mixed species over large areas.

Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R2 = 0.74; RMSE = 183.57 Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250–1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53–74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R2 (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB.

The use of remote sensing for forest inventory, fire management and wildlife habitat conservation planning has a decades-long and productive history in California. In the 1980s, mappers transitioned from aerial photography to digital remote sensing, in particular Landsat satellite imagery, which still plays a significant role in forest mapping, but today mappers increasingly rely on Lidar analysis. In California, where forests are complex and difficult to accurately map, numerous remote sensing scientists have pioneered development of methodologies for forest mapping with Lidar. Lidar has been used successfully here in a number of ways: to capture forest structure, to map individual trees in forests and critical wildlife habitat characteristics, to predict forest volume and biomass, to develop inputs for forest fire behavior modeling, and to map forest topography and infrastructure. Lidar can be costly to acquire and difficult to analyze, but as costs decline and new data processing methods are developed, it is likely that forest managers who need detailed information on forest structure across large spatial scales will incorporate Lidar data into their mapping toolkits.

Estimating aboveground biomass (AGB) is essential to quantify the carbon balance of terrestrial ecosystems, and becomes increasingly important under changing global climate. Volume metrics of individual trees, for example stem volume, have been proven to be strongly correlated to AGB. In this paper, we compared a range of airborne Lidar-derived volume metrics (i.e. stem volume, crown volume under convex hull, and crown volume under Canopy Height Model (CHM)) to estimate AGB. In addition, we evaluated the effect of horizontal crown overlap (which is often neglected in Lidar literature) on the accuracy of AGB estimation by using a hybrid method that combined marker-controlled watershed segmentation and point cloud segmentation algorithms. Our results show that: (1) when the horizontal crown overlap issue was not addressed, models based on point cloud segmentation outperformed models based on marker-controlled watershed segmentation; models using stem volume estimated AGB more accurately than models using crown volume under convex hull and crown volume under CHM. (2) Once the horizontal crown overlap issue was taken into consideration, the model using crown volume under CHM yielded a more accurate estimation of AGB. Our study provides a comprehensive evaluation of the use of airborne Lidar-derived volume metrics for AGB estimation and could help researchers choose the appropriate airborne Lidar-derived volume metric. Moreover, the results also indicate that horizontal crown overlap should be addressed when the airborne Lidar-derived forest crown volume is used for estimating AGB.

article i nfo There isaneedtoquantify large-scale plantproductivityincoastalmarshestounderstandmarshresiliencetosea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral re- flectance. Toaddress these challenges,we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species,Typhaspp. andSchoenoplectus acutus, at two restored marshes in the Sacra- mento-San Joaquin River Delta, California, USA. We usedfield spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data (R 2

Light detection and ranging (lidar) data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m2), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r2: 0.93–0.96). Tree detection rates increased for more dominant trees (8–100 percent). The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.

Discrete airborne lidar is increasingly used to analyze forest structure. Technological improvements in lidar sensors have led to the acquisition of increasingly high pulse densities, possibly reflecting the assumption that higher densities will yield better results. In this study, we systematically investigated the relationship between pulse density and the ability to predict several commonly used forest measures and metrics at the plot scale. The accuracy of predicted metrics was largely invariant to changes in pulse density at moderate to high densities. In particular, correlations between metrics such as tree height, diameter at breast height, shrub height and total basal area were relatively unaffected until pulse densities dropped below 1 pulse/m2. Metrics pertaining to coverage, such as canopy cover, tree density and shrub cover were more sensitive to changes in pulse density, although in some cases high prediction accuracy was still possible at lower densities. Our findings did not depend on the type of predictive algorithm used, although we found that support vector regression (SVR) and Gaussian processes (GP) consistently outperformed multiple regression across a range of pulse densities. Further, we found that SVR yielded higher accuracies at low densities ( 1 pl/m2). Our results suggest that low-density lidar data may be capable of estimating typical forest structure metrics reliably in some situations. These results provide practical guidance to forest ecologists and land managers who are faced with tradeoff in price, quality and coverage, when planning new lidar data acquisition.

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m 2 ), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. For example, from the top of canopy, we predicted canopy height (r 2 0.87), canopy cover (r 2 0.83), basal area (r 2 0.82), shrub cover (r 2 0.62), shrub height (r 2 0.59), combined fuel loads (r 2 0.48), and fuel bed depth (r 2 0.35). While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and 50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management.

© 2016 Kramer et al. Large trees are important to a wide variety of wildlife, including many species of conservation concern, such as the California spotted owl (Strix occidentalis occidentalis). Light detection and ranging (LiDAR) has been successfully utilized to identify the density of large-diameter trees, either by segmenting the LiDAR point cloud into individual trees, or by building regression models between variables extracted from the LiDAR point cloud and field data. Neither of these methods is easily accessible for most land managers due to the reliance on specialized software, and much available LiDAR data are being underutilized due to the steep learning curve required for advanced processing using these programs. This study derived a simple, yet effective method for estimating the density of large-stemmed trees from the LiDAR canopy height model, a standard raster product derived from the LiDAR point cloud that is often delivered with the LiDAR and is easy to process by personnel trained in geographic information systems (GIS). Ground plots needed to be large (1 ha) to build a robust model, but the spatial accuracy of plot center was less crucial to model accuracy. We also showed that predicted large tree density is positively linked to California spotted owl nest sites.

Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical changes, and it has contributed to uncharacteristically large and severe fires. The abundance of ladder fuels makes it difficult to return these forests to their natural fire regime or to meet management objectives. Despite the importance of ladder fuels, methods for quantifying them are limited and imprecise. LiDAR (Light Detection and Ranging), a form of active remote sensing, is able to estimate many aspects of forest structure across a landscape. This study investigates a new method for quantifying ladder fuel in the field (using photographs with a calibration banner) and remotely (using LiDAR data). We apply these new techniques in the Klamath Mountains of Northern California to predict ladder fuel levels across the study area. Our results demonstrate a new utility of LiDAR data to identify fire hazard and areas in need of fuels reduction.

Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3Dpoint data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diameter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Mountains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees (“recall”), 94 percent of the segmented trees were correct (“precision”), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.

Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging) sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst’s time in the delineation and classification of downed logs in the lidar data.

The continued spread of invasive weeds is threatening ecosystem health throughout North America. Understanding the relationships between invasive weeds’ key phenological phases and structural and/or functional canopy development is an essential step for making informed decisions regarding their management. We analyzed a three-year image archive obtained from an inexpensive webcam overlooking a perennial pepperweed (Lepidium latifoliumL.) infestation in California to explore the ability of red (R)‐green (G)‐blue (B) color space information to track the structural and functional development of the pepperweed.Wecharacterizedstructuralandfunctionalcanopydevelopmentthroughsurfaceroughness length (z 0m ; a proxy for canopy height and leaf area index) and canopy photosynthesis (F A ), respectively, both of which we derived from eddy covariance measurements. Here we demonstrate the use of cross-correlation functions to determine the temporal lags between chromatic coordinates and two color indices, all calculated fromRGBbrightness levels, withz 0m andF A . We found that these color metrics fail to represent the structural and/or functional state of the canopy. In contrast,relative luminance(CIE Y) appears to be a better indicator forz 0m and especially forF A . We calculatedCIE Yfrom pepperweedRGB brightness levels in relation to hypothetical horizontal referenceRGBbrightness levels. We obtained the latter by applying the ratio between horizontally measured and hypothetical incoming solar radiation on a vertical surface toRGBbrightness levels of a vertically oriented reference of invariant light-grey color. We conclude that webcam image archives may provide an inexpensive tool for making informed decisions regarding the timing but not for assessing the effectiveness of invasive plant control measures such as mowing.

Although remote sensing offers the ability to monitor wetland restoration, few have tested automated methods for quantifying vegetation change. We implemented a semiautomated technique using color infrared aerial photography and a common vegetation index, Normalized Difference Vegetation Index (NDVI), to document vegetation colonization in a restoring salt marsh. Change in vegetation over a period of 10 years was analyzed using a postclassification comparison technique where each image year was classified individually into vegetated and nonvegetated areas using NDVI thresholds and then differenced between years to identify areas of vegetation change. Vegetated and nonvegetated areas were identified using this technique, as were areas and time periods of vegetation change. By comparing classified NDVI imagery, we calculated that 90% of our study site was vegetated 10 years after restoration. This study demonstrated that high-resolution remotely sensed data can be analyzed with common geospatial software to monitor change in a rapidly vegetating wetland and that long time frames with yearly image acquisition are needed to quantify plant colonization rates. This method was effective at detecting change in vegetation over time in a variable tidal marsh environment using imagery that had inconsistent specifications and quality across years. Inconsistencies included interannual climate variation, phenology, and presence of algae, as well as differences in pixel size and image brightness. Our findings indicate that remote sensing is useful for postrestoration monitoring of tidal marsh ecosystems.

Fires constitute one major ecological disturbance which influences the natural cycle of vegetation succession and the structure and function of ecosystems. There is no single natural scale at which ecological phenomena are completely understood and thus the capacity to handle scale is beneficial to methodological frameworks for analyzing and monitoring ecosystems. Although satellite imagery has been widely applied for the assessment of fire related topics, there are few studies that consider fire at several spatial scales simultaneously. This research explores the relationships between fire occurrence and several families of environmental factors at different spatial observation scales by means of classification and regression tree models. Predictors accounting for vegetation status (estimated by spectral indices derived from Landsat imagery), fire history, topography, accessibility and vegetation types were included in the models of fire occurrence probability. We defined four scales of analysis by identifying four meaningful thresholds related to fire sizes in the study site. Sampling methodology was based on random points and the power-law distribution describing the local fire regime. The observation scale drastically affected tree size, and therefore the achieved level of detail, and the most explanatory variables in the trees. As a general trend, trees considering all the variables showed a spectral index ruling the most explicative split. According to the comparison of the four pre-determined analysis scales, we propose the existence of three eventual organization levels: landscape patch or ecosystem level, local level and the basic level, the most heterogeneous and complex scale. Rules with three levels of complexity and applicability for management were defined in the tree models: (i) the repeated critical thresholds (predictor values across which fire characteristics change rapidly), (ii) the meaningful final probability classes and (iii) the trees themselves.

Three sets of coast live oak (Quercus agrifolia) leaf samples were collected on three dates: 20 April 2002, 23 July 2002 and 11 September 2001, respectively, for Sudden Oak Death (SOD) monitoring. A total of 330 reflectance spectra (covering 350–2500 nm) were measured in the laboratory with a spectrometer FieldSpec®Pro FR. In this study, the spectroscopic determination of two health levels of the coast live oak leaves was conducted with three sets of spectra. We used two classification algorithms, penalized discriminant analysis (PDA) and cross correlogram spectral matching (CCSM), to discriminate between healthy and infected leaves. PDA is a penalized version of Fisher's linear discriminant analysis (LDA) and can considerably improve upon LDA when it is used for the classification of hyperspectral data. CCSM is practised by calculating the cross correlation at different match positions between a test spectrum and a reference spectrum and is also suitable for processing hyperspectral data. Experimental re...

A Compact Airborne Spectrographic Imager-2 (CASI) dataset was used for detecting mortality and vegetation stress associated with a new forest disease. We first developed a multilevel classification scheme to improve classification accuracy. Then, the CASI raw data were transformed to reflectance and corrected for topography, and a principal component (PC) transformation of all 48 bands and the visible bands and NIR bands were separately conducted to extract features from the CASI data. Finally, we classified the calibrated and corrected CASI imagery using a maximum likelihood classifier and tested the relative accuracies of classification across the scheme. The multilevel scheme consists of four levels (Levels 0 to 3). Level 0 covered the entire study area, classifying eight classes (oak trees, California bay trees, shrub areas, grasses, dead trees, dry areas, wet areas, and water). At Level 1, the vegetated and non-vegetated areas were separated. The vegetated and nonvegetated areas were further subdivided into four vegetated (oak trees, California bay trees, shrub areas, grasses) and four non-vegetated (dead trees, dry areas, wet areas, and water) classes at Level 2. Level 3 identified stressed and non-stressed oak trees (two classes). The ten classes classified at different levels are defined as final classes in this study. The experimental results indicated that classification accuracy generally increased as the detailed classification level increased. When the CASI topographically corrected reflectance data were processed into ten PCs (five PCs from the visible region and five PCs from NIR bands), the classification accuracy for Level 2 vegetated classes (non-vegetated classes) increased to 80.15 percent (94.10 percent) from 78.07 percent (92.66 percent) at Level 0. The accuracy of separating stressed from non-stressed oak trees at Level 3 was 75.55 percent. When classified as a part of Level 0, the stressed and non-stressed were almost inseparable. Furthermore, we found that PCs derived from visible and NIR bands separately yielded more accurate results than the PCs from all 48 CASI bands.

© 2016, Copyright © CASI. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites. Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l’analyse écologique, et la modélisation hydrologique. Les approches d’imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu’un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cette étude présente une stratégie qui utilise à la fois l’imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétation à grandes échelles spatiales. Notre approche comprenait l’utilisation d’un algorithme du critère d’information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d’étude dans les Sierra Nevada, en Californie, ainsi qu’une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d’étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d’étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de la végétation ou de composition des espèces de la végétation. La précision globale et le coefficient kappa des résultats de la cartographie de la végétation sont de plus de 78% et 0,64 pour les deux sites d’étude.

© 2015. Tidal wetlands are highly productive and act as critical habitat for a wide variety of plants, fish, shellfish, and other wildlife. These ecotones between aquatic and terrestrial environments also provide protection from storm damage, run-off filtering, and recharge of aquifers. Many wetlands along coasts have been exposed to stress-inducing alterations globally, including dredge and fill operations, hydrologic modifications, pollutants, impoundments, fragmentation by roads/ditches, and sea level rise. For wetland protection and sensible coastal development, there is a need to monitor these ecosystems at global and regional scales. Recent advances in satellite sensor design and data analysis are providing practical methods for monitoring natural and man-made changes in wetlands. However, available satellite remote sensors have been limited to mapping primarily wetland location and extent. This paper describes how the HyspIRI hyperspectral and thermal infrared sensors can be used to study and map key ecological properties, such as species composition, biomass, hydrology, and evapotranspiration of tidal salt and brackish marshes and mangroves, and perhaps other major wetland types, including freshwater marshes and wooded/shrub wetlands.

In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated through the Fractal Net Evolution Approach (FNEA) segmentation using eCognition software. For each object, 52 features were calculated including spectral features, textures, topographic features, and geometric features. After statistically ranking the importance of these features with the classification and regression tree algorithm (CART), the most effective features for classification were used to classify the vegetation. Due to the uneven sample size for each class, we chose a non-parametric (nearest neighbor) classifier. We built a hierarchical classification scheme and selected features for each of the broadest categories to carry out the detailed classification, which significantly improved the accuracy. Pixel-based maximum likelihood classification (MLC) with comparable features was used as a benchmark in evaluating our approach. The objectbased classification approach overcame the problem of saltand-pepper effects found in classification results from traditional pixel-based approaches. The method takes advantage of the rich amount of local spatial information present in the irregularly shaped objects in an image. This classification approach was successfully tested at Point Reyes National Seashore in Northern California to create a comprehensive vegetation inventory. Computer-assisted classification of high spatial resolution remotely sensed imagery has good potential to substitute or augment the present ground-based inventory of National Park lands.

Sudden Oak Death is a new and virulent disease affecting hardwood forests in coastal California. The spatial–temporal dynamics of oak mortality at the landscape scale are crucial indicators of disease progression. Modeling disease spread requires accurate mapping of the dynamic pattern of oak mortality in time through multi-temporal image analysis. Traditional mapping approaches using per-pixel, single-date image classifications have not generated consistently satisfactory results. Incorporation of spatial–temporal contextual information can improve these results. In this paper, we propose a spatial–temporally explicit algorithm to classify individual images using the spectral and spatial–temporal information derived from multiple co-registered images. This algorithm is initialized by a spectral classification using Support Vector Machines (SVM) for each individual image. Then, a Markov Random Fields (MRF) model accounting for ecological compatibility is used to model the spatial–temporal contextual prior probabilities of images. Finally, an iterative algorithm, Iterative Conditional Mode (ICM), is used to update the classification based on the combination of the initial SVM spectral classifications and MRF spatial–temporal contextual model. The algorithm was applied to two-year (2000, 2001) ADAR (Airborne Data Acquisition and Registration) images, from which three classes (bare, dead, forest) are detected. The results showed that the proposed algorithm achieved significantly better results (Year 2000: Kappa = 0.92; Year 2001: Kappa = 0.91), compared to traditional pixel-based single-date approaches (Year 2000: Kappa = 0.67; Year 2001: Kappa = 0.66). The improvement from the contributions of spatial–temporal contextual information indicated the importance of spatial–temporal modeling in multi-temporal remote sensing in general and forest disease modeling in particular.

A stratified method of separating dead tree crowns from the forest mosaic, especially bare soil, is proposed. Both spectral and spatial information extracted from 1 m high-resolution digital airborne imagery was used. First, spectrally homogeneous objects were recognized based on a Normalized Difference Vegetation Index (NDVI) image. From this image, large patches of bare soil and small patches of noise were excluded by object-size thresholding. Next, an incomplete bare soil image was generated from thresholding the red band using a histogram-based method. A region-based subtraction algorithm was developed and performed on the homogeneous object image and the bare soil image to remove most small patches of bare soil. Finally, medium-sized patches of bare soil were deleted using the assumption that bare soil areas are spatially much closer to larger, pure bare soil areas. The method proved to be effective. The classification accuracy of dead tree crowns was increased from 21% to 75%. Index Terms-Sudden Oak Death, hardwood forest, remote sensing, NDVI, object size, homogeneous object, region-based subtraction, high-resolution imagery.

In Pacific Coast salt marshes, only color and color IR aerial photography provide the spatial, spectral, and temporal resolution required to conduct long-term historic time series analysis of wetland change at the plant community level. We used historic aerial photographs with manual and automated image classification techniques to discern decadal-scale changes to salt marshes in Elkhorn Slough, California caused by off-farm sedimentation from 1971 to 2001. Change detection identified a process of plant succession that led to arroyo willow encroachment into pickleweed marsh. Changes observed were considered within the context of additional land use changes over a greater regional and time scale.

We investigated the ability of high spatial-resolution 4-band imagery (Airborne Digital Acquisition and Registration - ADAR) to discern moisture stress in trees affected by Sudden Oak Death (SOD). We wanted to test if the imagery could be used to distinguish between green oak trees with advanced SOD trunk symptoms, and green oaks with no SOD trunk symptoms. ADAR imagery of China Camp State Park in Marin County, California was flown in spring 2000 and 2001. Training samples from the field consisting of the locations green healthy oaks and green symptomatic oaks were used to derive spectral signatures for the two classes. Both hierarchical unsupervised classification (HUC) and maximum likelihood classification (MLC) were used to classify the imagery. Accuracy assessment and other spectral measurements were performed to analyze the separability of the two signatures. Poor overall accuracy 55.17% was obtained by the HUC method. A better overall accuracy 74.19% was obtained by MLC method, but the low transformed divergence (1448) indicated poor separability of the training samples. The poor accuracy results can be explained by the fact that ADAR image has relatively broad spectral bands that combine narrow moisture- stress-sensitive regions with broader stress-insensitive regions; such combination could decrease the capability of ADAR to detect moisture stress. In addition, healthy oaks in the area display a marked variability in canopy condition, making it difficult to separate healthy trees from those experiencing some stress. In conclusion, this research indicated the inability to automate mapping of moisture stress in oaks using ADAR imagery, and limited success in using methods that require extensive field data.

We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance, ladder fuels are difficult to quantify. One common approach is to calculate canopy base height, but this has many potential sources of error. LiDAR may be a way forward to better characterize ladder fuels, but has only been used to address this question peripherally and in only a few instances. After establishing that landscape fuel treatments reduced canopy and ladder fuels at our site, we tested which LiDAR-derived metrics best differentiated treated from untreated areas. The percent cover between 2 and 4 m had the most explanatory power to distinguish treated from untreated pixels across a range of spatial scales. When compared to independent plot-based measures of ladder fuel classes, this metric differentiated between high and low levels of ladder fuels. These findings point to several immediate applications for land managers and suggest new avenues of study that could lead to possible improvements in the way that we model wildfire behavior across forested landscapes in the US.

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Nearly half of the world’s natural wetlands have been destroyed or degraded, and in recent years, there have been significant endeavors to restore wetland habitat throughout the world. Detailed mapping of restoring wetlands can offer valuable information about changes in vegetation and geomorphology, which can inform the restoration process and ultimately help to improve chances of successful restoration. We performed an object-based image analysis using color infrared aerial photography, which maps specific wetland functions at multiple scales. The combined results of our work highlight important trends and management implications for monitoring wetland restoration using remote sensing, and will further enable restoration ecologists to use remote sensing for tidal marsh monitoring. Restoration objectives, ecosystem function, and scale can be integrated into monitoring techniques using remote sensing for improved restoration monitoring.

Modern agriculture is influenced by both the pressure for increased productivity and increased stresses caused by plant pests. Geographical Information Systems and Global Positioning Systems are currently being used for variable rate application of pesticides, herbicide and fertilizers in Precision Agriculture applications, but the comparatively lesser-used tools of Remote Sensing and Spatial Analyses can be of additional value in integrated pest management practices. The tools provide valuable information in an integrated pest management context, allowing for a complete understanding (via remote mapping or spatial modeling) of the spatial complexity of the abiotic and biotic characteristics of a field and its crops, and providing information about pest populations that are present, or likely to occur. This chapter details some of the advances in Remote Sensing and Spatial Analysis as applied to integrated agricultural plant pest management, and outlines some of the remaining challenges that farmers have to face in their adoption.