Time Series Of Landsat Imagery Research Articles

Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2=0.88, RMSE=2.39m and bias=−0.16 for canopy height; R2=0.72, RMSE=0.068% and bias=−0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm.

Mapping and assessing typhoon-induced forest disturbance in Changbai Mountain National Nature Reserve using time series Landsat imagery

Monitoring forest disturbances is important for understanding changes in ecosystems. The 1986 Typhoon Vera was a serious disturbance that severely impacted the forest ecosystems of Changbai Mountain National Nature Reserve. Although the typhoon disturbance occurred more than two decades ago, the effects of the typhoon still remain within the study area. Few studies have focused on mapping and assessing disturbances across broad spatial and temporal scales. For this study, we first generated a map of forest composition prior to the typhoon disturbance, which served as a baseline data for the extraction of disturbed area. Then, the Disturbance Index (DI) method was tested for mapping the extent and magnitude of disturbance in the study area by applying a Tasseled Cap transformation to the Landsat imagery. The Landsat-based DI method estimated that an area of 13,764.78 ha of forest was disturbed by the typhoon. Based on visual assessments, these results correspond closely with the reference map derived from ground surveys. These results also revealed the influence of local topographic features on the distribution of windthrow areas. Windthrow areas were more pronounced in areas with elevations ranging from 1,000 to 2,000 m, slopes of less than 10 degrees, and southwestern to northwestern aspects. In addition, the relatively long (25 years) post-typhoon recovery period assessed by this study provided a more comprehensive analysis of the dynamics of forest recovery processes over time. Windthrow areas did not recover immediately after the typhoon, likely due to forest management practices enacted at the time. So far, forest recovery has proceeded more rapidly at elevations below 1,400 m, particularly on western slopes within the study area. Finally, a time series of DI values within the study period suggests a secondary disturbance may have occurred between 2000 and 2001.

Monitoring Linear Disturbance Footprint Based on Dense Time Series Landsat Imagery

Mapping linear disturbances, including pipelines, roads, and seismic lines created by resource exploration, traditionally relies on very high-resolution remote sensing data, which usually limits results to small operational areas. With increased availability of low-cost medium-resolution satellite data, complete information of linear disturbances may be monitored and reconstructed from processing time series images from more than 30 years archival data. In this study, we propose a novel approach to incorporate spectral, spatial, and temporal information for mapping and characterizing linear disturbances based on time series Landsat imagery. The mapping process involves 4 steps: line detection based on a multiscale directional template, line updating based on reappearance frequency, line connection using the Hough transform, and linear disturbance characterization. The proposed method was tested and evaluated over 4 sites in Alberta, Canada, with various linear densities for detecting and reconstructing linear disturbances from 1984–2013 using time series Landsat imagery. The results obtained by processing time series Landsat imagery have shown improved accuracy in detecting linear disturbances over that from single or multiple Landsat images. It is concluded that the strategy of integrating information from time series imagery has the potential to lead to improved operational mapping of linear disturbances.

Landsat imagery-based visualization of the geomorphological development at the terminus of a dryland river system

In this remote sensing-based study, we present the analysis of the geomorphological development at the low-gradient terminus of the modern Río Colorado dryland river system in the endorheic Altiplano Basin (Bolivia). Changes in the river morphology occur after short periods of catastrophic peak discharge which result in the expansion of existing crevasse splays, formation of new crevasse splays and in river path avulsion. Episodic peak discharge events in the study area were pinpointed and quantified by combining daily precipitation records from gauging stations in the vicinity with catchment area analysis from ASTER global DEM (GDEM) remote sensing data. A time series of Landsat imagery for the period 1975–2001 was then used to analyze the river morphology changes after major peak discharge events. Compensational stacking of crevasse splays in combination with river avulsion produced a thin but aerially extensive connected sand sheet at the terminus of the fluvial system.

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000ha in the central Oregon Cascades, USA. Mapped explanatory data included tasseled-cap indices and disturbance history metrics (year, magnitude, and duration of disturbance) from LTS imagery, lidar-derived vegetation metrics, climate, topography, and soil parent material. Vegetation data from USDA Forest Service forest inventory plots was summarized at two plot sizes (plot and subplot) and geographically located with two levels of accuracy (standard and improved). Maps of vegetation composition and structure were developed with the Gradient Nearest Neighbor (GNN) method of NN imputation using different combinations of explanatory variables, plot spatial resolution, and plot positional accuracy. Lidar vegetation indices greatly improved predictions of live tree structure, moderately improved predictions of snag density and down wood volume, but did not consistently improve species predictions. LTS disturbance metrics improved predictions of forest structure, but not to the degree of lidar indices, while also improving predictions of many species. Absence of disturbance attribution (i.e. disturbance type such as fire or timber harvest) in LTS disturbance metrics may have limited our ability to predict forest structure. Absence of corrected lidar intensity values may also have lowered accuracy of snag and species predictions. However, LTS disturbance attribution and lidar corrected intensity values may not be able to overcome fundamental limitations of remote sensing for predicting snags and down wood that are obscured by the forest canopy. Improved GPS plot locations had little influence on map accuracy, and we suggest under what conditions improved GPS plot locations may or may not improve the accuracy of predictive maps that link remote sensing with forest inventory plots. Subplot NN imputation maps had much lower accuracy compared to maps generated using response variables from larger whole plots. No single map had optimal results for every mapped variable, suggesting map users and developers need to prioritize what forest vegetation attributes are most important for any given map application.

Interpretation of forest disturbance using a time series of Landsat imagery and canopy structure from airborne lidar

In this study we examined forest disturbance, largely via forest harvest, over three decades in a coastal temperate forest on Vancouver Island, British Columbia, Canada. We analysed how disturbance history relates to current canopy structural conditions by interpreting the relationship between light detection and ranging (lidar) derived canopy structure and forest disturbance trajectories derived from Landsat images to assess if a particular stand structural condition is to result based on disturbance histories. The lidar data were obtained in 2004, and are used to relate forest structural conditions at the end of the Landsat time series (1972–2004), essentially providing for a measure of resultant structure emerging from the spectral trends captured. Correlation analysis was applied between lidar-derived canopy structure (canopy cover and height) and Landsat spectral indices, such as the Tasseled Cap Angle (TCA), which showed a strong correlation coefficient (r = 0.86) with canopy cover. TCA was then used to characterize change in forest disturbance through the full temporal depth of the available Landsat image time series using a trajectory-based characterization method. Approximately 71.5% of the study area was found to correspond to “stable and undisturbed forest”. Four disturbance classes (areas characterized by disturbance, disturbance followed by revegetation, ongoing revegetation, and revegetation to stable state) accounted for approximately 10.2%, 5.3%, 2.2%, and 10.5% of the study area, respectively. We evaluated the forest structural and spectral separability between the disturbance classes. In terms of structural variability the mean airborne lidar-derived canopy cover showed clear differentiation between disturbance classes. Spectral mixture analysis (SMA) was used to extract the spectral characteristics for each disturbance class. The SMA-derived fractions were then used to analyse the class separability between the Landsat trajectory derived disturbance classes. The fraction images provided clear distinction between disturbance classes in abundances between sunlit canopy, non-photosynthetic vegetation, shade, and exposed soil. The extracted spectral indices and SMA fractions within the Landsat trajectory derived disturbance classes were used to assess if terminal forest structural conditions can be related to a complex suite of stand development trajectories and processes. The Landsat spectral indices and SMA fractions were separately modeled to estimate lidar-derived mean canopy cover and height data within each disturbance class using multiple regression. The results indicate canopy cover and height regression models developed using spectral indices provided a relatively better estimation than those using SMA endmember fractions. Compared with the relatively regular structure of fully grown undisturbed (stable) forests, the forest disturbance classes typically exhibited complex irregular structure, making it more difficult to accurately estimate their canopy cover and height. As a result, all models developed for the stable forest class performed better than those developed for other forest disturbance classes. Modeling canopy cover and height from Landsat temporal spectral indices resulted in modeled agreement to lidar measures of R2 0.82 (RMSE 0.09) and R2 0.67 (RMSE 3.21), respectively. Our results also indicate moderately accurate predictions of lidar-derived canopy height can be obtained using the Landsat-level disturbance class endmember fractions with R2 0.60 and RMSE 4.19. This study demonstrates the potential of using the over four decade record of Landsat observations (since 1972) to estimate forest canopy cover and height using prestratification of the data based on disturbance trajectories.

Scale considerations for integrating forest inventory plot data and satellite image data for regional forest mapping

The integration of satellite image data with forest inventory plot data is a popular approach for mapping forest vegetation over large regions. Several methodological choices regarding spatial scale, mostly related to spatial resolution or grain, can profoundly influence forest maps developed from plot and imagery data. Yet often the consequences of scaling choices are not explicitly addressed. Our objective was to quantify the effects of several scale-related methods on map accuracy for multiple forest attributes, using a variety of diagnostics that address different map characteristics, to help guide map developers and users. We conducted nearest-neighbor imputation over a large region in the Pacific Northwest, USA, to investigate effects of imputation grain (single pixel or kernel); inclusion of heterogeneous plots; accuracy assessment grain and extent; and value of k (k=1 and k=5), where k is the number of nearest-neighbor plots. Spatial predictors were from rasters describing climate and topography and a time-series of Landsat imagery. Reference data were from regional forest inventory plots measured over two decades. All analyses were conducted at a spatial resolution of 30m×30m. Effects of imputation grain and heterogeneous plots on map accuracy were small. Excluding heterogeneous plots slightly improved map accuracy and did not lessen the systematic agreement (ACSYS) between our maps and observed plot data. Accuracy assessment grain strongly influenced map accuracy: maps assessed with a multi-pixel block were much more accurate than when assessed with a single pixel for almost all map diagnostics, but this was an artifact of methods rather than reflecting real differences among maps. Unsystematic agreement (ACUNS) between our maps and plots, or random error, improved notably with increasing accuracy assessment extent for all scaling methods, indicating that reliability of most map applications can be improved through coarsening the map grain. Value of k strongly influenced map diagnostics. The k=5 maps were better than k=1 maps for local-scale accuracy, but at the cost of reduced ACSYS, and loss of variability and poor areal representation of forest conditions over the study region. The k=1 maps produced notably better predictions of the least abundant forest conditions (early-successional, late-successional, and broadleaf). None of the scaling methods were optimal for all map diagnostics. Nevertheless, given a variety of diagnostics associated with a range of scaling options, map developers and map users can make informed choices about methods and resulting maps that best meet their particular objectives, and we present some general guidelines in this regard. Most of our findings are applicable to mapping with Landsat data in other forested regions with similar forest inventory data, and to other methods for spatial prediction.

Robust prediction of time-integrated NDVI

The time-integrated normalized difference vegetation index (iNDVI) provides key remote-sensing-derived information on the interactions between vegetation growth, climatic and soil conditions, and land use. Using a time-series of Landsat imagery obtained for Queensland, Australia, it has been demonstrated how robust geostatistics can be used to predict iNDVI. This approach is novel because it explicitly quantifies the uncertainty of prediction and uses Winsorizing, a data-censoring method, to minimize the distorting effects of outliers. Robust prediction of iNDVI, as opposed to non-robust prediction, was justifiable in 79% of the study area, highlighting the need for methods that deal with outliers in time-series analysis of remotely sensed imagery. There was a strong coarse-scale association between Queensland’s bioregions and iNDVI, and also between bioregion and the rain-induced difference in iNDVI through time (effects that were significant at p < 0.001 in both cases). At a finer spatial scale, prediction of iNDVI also appeared to be a promising way to distinguish long-term cropping land from adjacent long-term grazing land (effect significant at p < 0.001). The method is tied to a set of assumptions concerning image radiometry, cloud detection, variogram estimation, and variable additivity. The first two are fundamental remote-sensing issues that can be improved with additional labour; the last two can be improved statistically but would greatly increase the processing time per pixel. Robust geostatistical analysis of time-series has immediate relevance to gap-filling of SLC-off Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery, and for generating novel covariates for digital soil mapping.

Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM/ETM + time series

The 20+ year collection and moderate resolution of Landsat Thematic Mapper (TM) imagery and Enhanced Thematic Mapper (ETM+) imagery provide a crucial data source for analysing land surface change over time for a range of applications. In Queensland, Australia, a number of government policies, natural resource management programmes and research activities are reliant on large-area, multi-temporal land cover monitoring applications based on Landsat satellite imagery. However, clouds and associated cloud shadows frequently obstruct the view of the land surface. The restriction of analyses to cloud-free imagery will reduce the opportunities to sample the land surface and limit the analysis of trends in reflectance over time. This study presents a new automated method to screen cloud and cloud shadow and is intended for application to entire time series of Landsat imagery rather than single images processed in near- real time. The method uses a hierarchical approach and takes advantage of spectral, temporal, and contextual information. Outliers are located relative to the time series of land surface reflectance by smoothing time series information using minimum and median filters which are then used in multi-temporal image differencing. Seeded region grow and morphological dilation (pixel buffering) filters are then applied to map a larger spatial extent of the cloud/cloud shadow. Spectral and contextual rules were developed empirically using calibration and validation data derived from six Landsat WRS Path/Rows (number of images=60) with varying climatic and land surface characteristics across the state of Queensland, Australia. The validation demonstrates that cloud contaminated pixels were accurately classified with producer's, user's and overall accuracies of 98, 87 and 97%, respectively. The ability to detect cloud shadow was less accurate, in comparison, with producer's, user's and overall accuracies of 90, 62 and 97%, respectively. The pixel buffer was found to be the largest source of commission error for cloud and cloud shadow in the final classifications. However, for many applications removing additional cloud/shadow at the expense of higher commission errors may be desirable. The performance of the method was also compared with the published Fmask (Function of mask) method. This demonstrated a moderate improvement in the detection of cloud (producer's accuracies: time series 98% and Fmask 90%; and an equivalent user's accuracy of 87%), and a significant improvement in the detection of cloud shadow (producer's accuracies: time series 90% and Fmask 78%; user's accuracies: time series 62% and Fmask 50%). Importantly, the results indicate that this automated method is robust and that temporal information can improve the detection of cloud and cloud shadow, although shadow detection above cropping areas is limited. The calibration/validation of the method has been restricted to Queensland, Australia. With further development there is potential for this method or one using a similar framework to have wider application in other landscapes.

Forest cover trends from time series Landsat data for the Australian continent

In perennial and natural vegetation systems, monitoring changes in vegetation over time is of fundamental interest for identifying and quantifying impacts of management and natural processes. Subtle changes in vegetation cover can be identified by calculating the trends of a vegetation density index over time. In this paper, we apply such an index-trends approach, which has been developed and applied to time series Landsat imagery in rangeland and woodland environments, to continental-scale monitoring of disturbances within forested regions of Australia. This paper describes the operational methods used for the generation of National Forest Trend (NFT) information, which is a time-series summary providing visual indication of within-forest vegetation changes (disturbance and recovery) over time at 25m resolution. This result is based on a national archive of calibrated Landsat TM/ETM+ data from 1989 to 2006 produced for Australia's National Carbon Accounting System (NCAS). The NCAS was designed in 1999 initially to provide consistent fine-scale classifications for monitoring forest cover extent and changes (i.e. land use change) over the Australian continent using time series Landsat imagery. NFT information identifies more subtle changes within forested areas and provides a capacity to identify processes affecting forests which are of primary interest to ecologists and land managers. The NFT product relies on the identification of an appropriate Landsat-based vegetation cover index (defined as a linear combination of spectral image bands) that is sensitive to changes in forest density. The time series of index values at a location, derived from calibrated imagery, represents a consistent surrogate to track density changes. To produce the trends summary information, statistical summaries of the index response over time (such as slope and quadratic curvature) are calculated. These calculated index responses of woody vegetation cover are then displayed as maps where the different colours indicate the approximate timing, direction (decline or increase), magnitude and spatial extent of the changes in vegetation cover. These trend images provide a self-contained and easily interpretable summary of vegetation change at scales that are relevant for natural resource management (NRM) and environmental reporting.

INVESTIGATING THE DYNAMICS OF WANDOO CROWN DECLINE WITH TIME SERIES LANDSAT IMAGERY

Abstract. In the forests of south west Western Australia dramatic declines in tree health have been observed in recent years. The species Eucalyptus wando has exhibited loss of crown foliage in increasing stages of severity; this condition is referred to as wandoo crown decline and can lead to death. Determining the extent and timing of these declines is difficult on the ground due to the large distribution of E. wandoo and the observation of tree declines at a range of locations and dates over the last 40 years. Understanding the distribution, severity and timing of these declines is essential to the identification of the causes of these impacts. Investigation of time series Landsat imagery can inform on locations of crown foliage loss and the time periods these losses occurred in. Applying a vegetation index to a 20 year sequence of imagery enabled periods of decline to be identified. Employment of trend analysis of four date time series demonstrated the dynamics of wandoo crown cover. Comparisons of periods of decline with the variations of cover density over the entire image sequence facilitated the identification of possible locations and timing of wandoo crown decline impacts. Changes in crown cover observed by the imagery analysis were compared to field surveys.

HYDROPERIOD CLASSIFICATION OF CERVANTES COOLIMBA COASTAL WETLANDS USING LANDSAT TIME SERIES IMAGERY

Abstract. Geomorphic classification of wetlands relies on information regarding landform and hydroperiod. Additional attributes of wetland size, vegetation cover and salinity can be collected for lower order classification. Hydroperiod is important in determining features that characterize the ecological function of the wetland. This study examines how Landsat time series imagery was used to describe the hydroperiod of wetlands along the coastal zone between Cervantes to Coolimba in Western Australia. Inundation extent maps were derived using 17, Landsat Band 5 images captured between 1988 and 2011. The number of times a wetland basin was more than 10 % inundated was then calculated. This inundation frequency dataset was then the basis for hydroperiod classification. Wetlands were classified as being permanently inundated if they had more than 10% inundation in 12 or more of the 17 images available (approximately 70%). A Landsat image captured 2 weeks prior to field work was used to determine Band 5 thresholds to identify areas of inundation and assess classification accuracy. Field measurements of surface water cover, depth, secchi depth, vegetation cover and salinity were taken across 16 plots located close to inundation boundaries. The accuracy of the inundation maps was highly dependent on the degree of vegetation cover. Mapping accuracy was heavily influenced by vegetation cover and achieved an overall accuracy of 87.5%. The resulting hydroperiod datasets provides an accurate record of inundation frequency which can be used to aid classification of wetlands and also allows changes to inundation frequency over time to be assessed.

Forest cover and deforestation patterns in the Northern Andes (Lake Maracaibo Basin): A synoptic assessment using MODIS and Landsat imagery

This work synthesizes results from the application of land cover classification techniques and probability sampling of satellite imagery for estimating forest extent and deforestation in Lake Maracaibo Basin (Venezuela and Colombia). A forest map was produced using a semi-automated supervised classification routine on MODIS 8-day 500-m imagery acquired in January 2010. Results show that forests occupy 29,710 km2 which represents 38% of the basin's total terrestrial landmass. From this extent, 61% belongs to Venezuela and 39% falls within the Colombian region. Findings indicate a drastic decrease in forest cover as a result of anthropogenic agricultural and urban expansion, especially when compared to its potential extent within the ‘Maracaibo dry forests’ and the ‘Venezuelan Andean montane forests’ ecoregions. Using time series of Landsat imagery, deforestation rates for the 1985–2010 time period were calculated. The analysis was performed on 24 samples blocks of 10 × 10 km2 randomly allocated within previously defined change probability strata. The general spatial distribution of deforestation rates was predicted by a simple regression model between sample blocks and prior change probabilities at the basin scale. Our results indicate that deforestation was low (<0.5%/y) in 85% of the basin, with highly focalized deforestation fronts (intermediate-to-high rates, <2.5%/y) in three regions: a) the Motatán river sub-basin in the Eastern Cordillera, b) the lower slopes of the Catatumbo river sub-basin and c) the submontane regions of the Apón and Santa Ana river sub-basins. The results of this paper lead the way for understanding current patterns in socioeconomic drivers of forest clearing in Lake Maracaibo Basin. The study also demonstrates the feasibility of using alternatives methods to the time-consuming and financially unsustainable methods traditionally used at national and sub-national scale in Venezuela and other Latin American countries.

Characterizing 25 years of change in the area, distribution, and carbon stock of Mediterranean pines in Central Spain

Mediterranean pines are subject to continuous change under the influence of natural and human factors. Remotely sensed data provide a means to characterize these changes over large areas. In this study we used a time series of Landsat imagery to capture 25 years (1984–2009) of change in the pine-dominated forests of the Central Range in Spain. Object-based image analysis methods were used to identify landscape-level changes in the area and the distribution of forests. We also propose that in the absence of disturbance, biomass accrual is occurring (or depletion in cases where removal is evident) and may be related to changes to the carbon stock; we describe the detected spectral changes in terms of biomass changes as the carbon stocking process. The primary inputs for the identification of changes in the area and distribution of pine stands were Landsat bands 3, 4 and 5 and the Tasseled Cap Angle (TCA) – a metric derived from the greenness and brightness components of the Tasseled Cap Transformation (TCT). In the identification of carbon stocking processes the temporal derivative of the TCA, the Process Indicator (PI), was used to inform on the rate and directionality of the change present. Our results show that the total area of pine forest has increased by 40%, from 1211 km2 to 1698 km2, during this period, with a variable rate of change. The distribution of pine-dominated forest has changed as well: there is an area of 765 km2 permanently covered with pines and 945 km2 found to be temporarily occupied. Following the logic of carbon stocking processes, our findings show that at the end of the analysis period, 20% of the potential pine area is increasing its carbon stock and 40% of this area is experiencing a decrease.

Spatial and Temporal Evolution of an Avulsion on the Taquari River Distributive Fluvial System from Satellite Image Analysis

Avulsion processes have been observed and analyzed using field based studies, however, they have rarely been analyzed through use of satellite imagery even though such an approach allows documentation of spatial and temporal variations of the resulting deposits. In order to document changes that are occurring at the Caronal avulsion on the Taquari River, Brazil, a time series of LANDSAT imagery from 1985 to present (2010) was compiled. As of July 2010, the imagery shows that the avulsion is not yet complete, with discharge being routed to both the parent channel and the avulsion channel in the floodbasin. The avulsion was initiated as flow was diverted from the parent channel to the floodbasin through two small crevasses, allowing the development and evolution of splays, with a single splay stabilizing only since 1999. The avulsion area is marked by the growth of new splays, the formation of an avulsion belt, and progradation of avulsion deposits into the floodbasin. The parent channel downstream of the avulsion site has experienced decreasing width through time, associated with the higher volumes of water and sediment being captured by the avulsion channel. Because this avulsion is not yet complete, we expect more morphological changes to occur as the avulsion belt evolves into the primary channel. Rapid sedimentation caused by this avulsion and the 24-year time series of imagery that captured this change allowed us to construct a 3D block representing the evolution of depositional environments associated with avulsion, using time in the vertical dimension. Thus we could represent the evolution of geomorphological patterns, including floodbasin, proximal levee, distal levee, and channel deposits, and project their distributions through time. This block of evolving sedimentary settings can be used as a guide for future sedimentologic studies by offering visual clues regarding the way which depositional systems track avulsion-initiated changes.

Monitoring trends in the extent of major floods in the lower reach of Songkhram River Basin, Northeastern Thailand

This study analyzes trends in the extent of major floods in the lower reach of Songkhram River Basin, one of the most important areas for aquatic biological production in the Lower Mekong River Basin. We first classified the time series Landsat imagery acquired at a 2-year interval between 2000 and 2006 using the unsupervised classification method. We then analyzed the extent of major floods through the image matrix analysis on seasonal land cover map pairs. Additionally, we estimated flood volumes and discharge rates for each time series. We finally analyzed the extent of land uses that were affected by major flood events. Accuracy assessment showed that the extent of major floods was accurately mapped. Analysis of trends of major floods revealed that there was a considerable variation in the extent through 2006, with the flood decreasing since 2002. Analysis of flood risk areas based on the 2000–2004 data showed that about 3.04% of the study areas was at high risk of being flooded. Between 2000 and 2006, about 5.5% of the study area that are classified as agriculture and built-up land uses was affected by major floods. Our estimates on flood volumes and discharge rates are consistent with the data from other studies. Overall findings suggest that accurate mapping of major floods and flood risk areas using space and time dependent data can be important for developing protocols for flash flood early warning and flood risk management and mitigation. Output GIS maps and data combined with crude assumptions about water flow will serve as the baseline data to estimate the amount of water that flows in and out of the basin. Further research should focus on integration of social science research to evaluate the socio-economic impacts of major floods and identifying coping strategies of affected communities.

Parcel-based classification of agricultural crops via multitemporal Landsat imagery for monitoring habitat availability of western burrowing owls in the Imperial Valley agro-ecosystem

Agricultural production has a large impact upon the sustainability of wildlife populations. Certain species decline in the presence of intensive agriculture, while others thrive. Thus, quantifying changes in habitat within agricultural systems is paramount to understanding the underlying ecological mechanisms influencing species' range shifts and may facilitate the development of management strategies for sensitive wildlife species that depend on agricultural systems. Remotely sensed data can provide an efficient means to assess and monitor agricultural crop dynamics across large spatial extents. The objective of this study was to classify field-level agricultural crop types via a time series of Landsat imagery across the 3 620 000 ha agro-ecosystem in the Imperial Valley in California. The primary impetus was to generate data to characterize short-term changes in habitat for the western burrowing owl (Athene cunicularia), a species of concern that has an affinity for agricultural systems. The parcel-based classification method attained overall accuracies of 84% and 69% for level II (3 crop groups) and level III (31 crop types) agricultural crops, respectively. Given the large number of crop categories classified, these accuracies were quite high, especially when compared with existing crop type classifications across the region (e.g., the National Cropland Data Layer). These results suggest that the crop type classifications presented herein could potentially be used to evaluate owl demographic and space use parameters in light of shifting crop patterns across the study area.

Validation of North American Forest Disturbance dynamics derived from Landsat time series stacks

The North American Forest Dynamics (NAFD) study is a core project of the North American Carbon Program (NACP). The NAFD project is evaluating forest disturbance patterns and rates of disturbance by integrating U.S. Department of Agriculture (USDA) Forest Service Inventory and Analysis (FIA) field observations with temporally dense time series Landsat imagery. In Phase I of NAFD forest disturbance history was derived for 23 U.S. sample locations over the time period 1984 to 2005 from biennial Landsat time series stacks (LTSS). This study evaluates the accuracy of these Phase I NAFD disturbance history maps for 6 selected sample locations. We evaluate the disturbance maps using 2 reference datasets: 1) a design-based approach incorporating visual analysis of the LTSS in tandem with high resolution imagery and 2) the USDA FIA field observations. Overall accuracy for the NAFD disturbance product assessed at the individual time step level range from 77% to 86%. We examine the success rates of the mapping approach for capturing different types of disturbance and find that 82% of stand clearing events were detected. When we aggregate the data into change and no change categories the accuracy of stand clearing disturbance samples improved to over 92%. The majority of error in the disturbance maps was due to misclassification of partial disturbance as unchanged forest. We analyze the resulting errors of commission and omission as related to both reference datasets for each LTSS and present examples to illustrate the strengths and weaknesses of Phase I NAFD approach. In addition, we discuss the map biases observed in this work and what this may imply for estimating national forest disturbance rates with this approach.

Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation

In this technical note we present a new technique using mixed linear models for characterizing a mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation from multiyear satellite imagery. The main benefit of our approach is an ability to determine the statistical significance of each annual spectral change. Knowledge of the annual spectral change characteristics can then be used to statistically determine if a disturbance event has occurred, the timing of a given disturbance event, as well as to provide information for clustering fitted multitemporal reflectance curves (i.e. spectral trajectories) with a common shape. The spatial clustering of spectral trajectories provides insights into the nature of the disturbance and recovery imposed by infestation over a 14-year period.

Quantification of the Ecological Resilience of Drylands Using Digital Remote Sensing

Drylands cover 41% of the terrestrial surface and support > 36% of the world's population. However, the magnitude of dryland degradation is unknown at regional and global spatial scales and at 15-30-yr temporal scales. Historical archives of > 30 yr of Landsat satellite imagery exist and allow local to global monitoring and assessment of a landscape's natural resources in response to climatic events and human activities. Vegetation indices (VIs), i.e., proxies of vegetation characteristics such as phytomass, can be derived from the spectral properties of Landsat imagery. A dynamical systems analysis method called mean-variance analysis can be used to describe and quantify dynamic regimes of VI response to disturbance using characteristics of ecological resilience, particularly amplitude and malleability, from a change detection perspective. Amplitude is the magnitude of response of a VI to a disturbance; malleability is the degree of recovery of a resource after a disturbance. Spatially aggregate and spatially explicit (image) differencing are methods whereby a VI image or statistic from one time period is subtracted from a VI image or statistic from another time period. To illustrate this method, we used a time series of Landsat imagery from 1972 to 1987 to measure the response of vegetation communities that are managed by subsistence agropastoral communities to the severe 1982-1984 El Nino-induced drought on the Bolivian Altiplano. We found that the entire landscape had decreased vegetation cover, increased variance (diagnostic of a regime shift), and thus, increased susceptibility to soil erosion during the drought. The wet meadow vegetation cover class had the lowest amplitude and thus the most resilience relative to other vegetation cover classes. This response identified the wet meadow as a key resource, as well as a harbinger of climate change for agropastoral communities in areas where drought is an endemic stressor.