Tasseled Cap Research Articles

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.

Wetlands Mapping in North America by Decision Rule Classification Using MODIS and Ancillary Data

An up-to-date wetlands map based on remote sensing data at a continental scale is urgently needed for estimating global environmental change. In this study, a wetlands map of North America was developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data obtained in 2008 and ancillary data. For this purpose, a decision rule classification method was developed relied upon the hierarchical characteristics of land types and prior knowledge about the geographical location of wetlands. Two hierarchical levels of land types were used to extract wetlands. At the first level, non-vegetation land types including water, snow, urban, and bare areas were separately extracted from vegetation land types using threshold methods. At the second level, wetlands were discriminated from non-wetland vegetation land types with the MODIS tasseled cap (brightness, greenness, and wetness) indices using the decision tree method. In addition, elevation data were used to build the elevation mask and a climate map was used to subdivide the study area into five sub-regions. In the quantitative accuracy assessment, user's and producer's accuracies of wetlands for the whole study area were calculated as 80.3% and 83.7%, respectively. In a comparison with two existing global land cover datasets, GLC2000 and IGBP DISCover, our results show significant improvement in extracting coastal and narrow types of wetlands. This study indicates that decision rule classification, integrated with multi-temporal MODIS data and ancillary data, is useful to develop an improved wetlands map at a continental scale.

Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987–2010

Forest management seeks sustainability for a diverse set of goals, including economic objectives, provision of ecosystem services, and provision of a variety of possible land uses. It is important to quantify, map and monitor forest dynamics resulting from natural and anthropogenic processes over time periods appropriate to the temporal scale of change as well as to land management goals and decision making. This paper presents temporal trend analyses of temperate mixed forest dynamics in Gatineau Park, Québec, Canada, using a time series of Landsat 5 TM scenes. Several vegetation indices were first evaluated as indicators of field measured vegetation abundance parameters such as leaf area index, canopy openness, DBH, and basal area. Of these, Tasseled Cap Wetness (TCW) provided the best relationships (e.g., r=0.81 against LAI) and it differentiated between coniferous, mixed and deciduous forests. Thirteen clear sky 5 TM scenes from the growing seasons of 1987 to 2010 were relatively calibrated and assembled into an image time-series. TCW applied to the image time-series followed by Theil–Sen and Contextual Mann–Kendall trend analysis detected subtle and gradual field-verified forest change. Gradual and abrupt forest decline or regrowth periods were identified; over the full period, 641ha (1.8% of the park) exhibited statistically significant growth, and 689ha (1.9%) exhibited decline. Mapping the timing, location, magnitude, and duration of forest change will help inform land management policy and actions within Gatineau Park and such methods may be applied in other similar forests.

Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.

Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations

Research Article| November 01, 2013 Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations Thomas Oommen; Thomas Oommen Department of Geological Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 1Corresponding author: phone: 906-487-2045, fax: 906-487-3371, email: toommen@mtu.edu. Search for other works by this author on: GSW Google Scholar Laurie G. Baise; Laurie G. Baise Department of Civil and Environmental Engineering, Tufts University, Medford MA 02155 Search for other works by this author on: GSW Google Scholar Rudiger Gens; Rudiger Gens Alaska Satellite Facility, Geophysical Institute, University of Alaska–Fairbanks, Fairbanks, AK 99775 Search for other works by this author on: GSW Google Scholar Anupma Prakash; Anupma Prakash Geophysical Institute, University of Alaska–Fairbanks, Fairbanks, AK 99775 Search for other works by this author on: GSW Google Scholar Ravi P. Gupta Ravi P. Gupta Department of Earth Sciences, Indian Institute of Technology, Roorkee 247667, India Search for other works by this author on: GSW Google Scholar Environmental and Engineering Geoscience (2013) 19 (4): 303–318. https://doi.org/10.2113/gseegeosci.19.4.303 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Thomas Oommen, Laurie G. Baise, Rudiger Gens, Anupma Prakash, Ravi P. Gupta; Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations. Environmental and Engineering Geoscience 2013;; 19 (4): 303–318. doi: https://doi.org/10.2113/gseegeosci.19.4.303 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentBy SocietyEnvironmental and Engineering Geoscience Search Advanced Search Abstract Documenting earthquake-induced liquefaction effects is important to validate empirical liquefaction susceptibility models and to enhance our understanding of the liquefaction process. Currently, after an earthquake, field-based mapping of liquefaction can be sporadic and limited due to inaccessibility and lack of resources. Alternatively, researchers have used change detection with remotely sensed pre- and post-earthquake satellite images to map earthquake-induced effects. We hypothesize that as liquefaction occurs in saturated granular soils due to an increase in pore pressure, liquefaction-induced surface changes should be associated with increased moisture, and spectral bands/transformations that are sensitive to soil moisture can be used to identify these areas. We verify our hypothesis using change detection with pre- and post-earthquake thermal and tasseled cap wetness images derived from available Landsat 7 Enhanced Thematic Mapper Plus (ETM+) for the 2001 Bhuj earthquake in India. The tasseled cap wetness image is directly related to the soil moisture content, whereas the thermal image is inversely related to it. The change detection of the tasseled cap transform wetness image helped to delineate earthquake-induced liquefaction areas that corroborated well with previous studies. The extent of liquefaction varied within and between geomorphological units, which we believe can be attributed to differences in the soil moisture retention capacity within and between the geomorphological units. You do not currently have access to this article.

A Land Cover Variation Model of Water Level for the Floodplain of Tonle Sap, Cambodia, Derived From ALOS PALSAR and MODIS Data

The floodplain around Tonle Sap, Cambodia is strongly influenced by seasonal variations in water level. In the wet season, lacustrine landforms and vegetated areas are partly inundated due to increases in the water level. Conversely, they are gradually emerged when the flooding recedes during the dry season. Because floods in Tonle Sap are an annual event, a land cover variation model that takes into account water level is necessary to predict areal changes in each land cover class at the floodplain. To establish this model, we used the Phased Array L-band Synthetic Aperture Radar (PALSAR) backscattering coefficients, normalized difference vegetation index (NDVI) values, and tasseled cap (TC) transformations of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2007 to 2010 to estimate the areal variation of six land cover classes during the annual flood pulse. The radar backscattering coefficients correlated well with NDVI values during the dry season, but the relationship vanished during the wet season. According to our model, a backscattering coefficient change from -8.4 dB to -20.6 dB for lowland shrubs in the flood developing stage corresponded to an areal percentage change of un-flooded lowland shrubs from 16.3% to 0.5% of the total study area. Once the water level increased to the peak of flooding, 46.2% of the lowland shrub area was immersed. Our model also predicted that approximately 41.8% of the total study area was replaced with a water surface at the peak of flooding. When we compared the two results obtained using our model at 6 m above mean sea level (amsl) and using a digital terrain model (DTM) and the land use map, we observed a large difference between the two models in the areal percentage of the corresponding land cover of un-flooded lowland shrubs (-10.3%). Our land cover variation model can be used to predict areal changes in land cover classes during flood development and recession stages, and can also provide insight into flood dynamics, thereby enabling flood management in this region.

Remote Sensing Integration Survey of Typical Land Types and Analysis of its Spectral Properties in Southeast of Yunnan Province

Karst rocky desertification is a major ecological disaster faced by the southeastern region of Yunnan Province, in China. The research of land use status in rocky desertification area is more and more concerned by human. Through remote sensing integrated survey ways to establish the spectral database of six kinds of typical land types, such as water, forest land, paddy field, grassland, dry land and bare land; then, Normalized Difference Vegetation Index (NDVI) processing and Tasseled Cap transformation processing for the spectral data; finally, the spectral characteristics and the variation of typical land types was analyzed and studied. The results indicated that, there are no significant differences of spectral of the six kinds of typical land types, after the processing of NDVI, the water, the forest land and grassland with larger vegetation coverage, and the paddy land, dry land and bare land with smaller vegetation coverage, can be well distinguish, but the distinction between forest land and grassland is bad, the distinction of paddy land, dry land and bare land is also bad; after the Tasseled Cap transformation processing, the dispersion degree of water, forest land, paddy field and grassland is better, the dispersion degree of dry land and bare land is a bit poor, need to be further studied. The remote sensing integrated survey and the analysis of spectral properties are important foundation for the Land Use / Land Cover Change (LUCC) and the dynamic monitoring of rocky desertification in southeastern rocky desertification region of Yunnan province.

A new approach for regional scale interrill and rill erosion intensity mapping using brightness index assessments from medium resolution satellite images

Having accurate soil erosion intensity/type maps using satellite imagery is not generally a difficult task. However, there are still difficulties for the generation of small scale erosion features at regional and national levels. It is even more problematic when high-resolution satellite images cannot be used due to their high cost at a regional level. The principal objective of this study is to investigate the applicability of brightness value to generate accurate interrill and rill erosion intensity maps using medium resolution satellite images at a regional level. In this study, Landsat ETM+ images are used and the Golestan dam watershed with an area of 4511.8km2 located at northeast of Iran is selected as the study area. In order to generate a Homogeneous Land Unit (HLU) map, three ancillary layers including slope, landform, land use and land cover, are overlayed on each other. The HLUs are used in a supportive role for identifying appropriate sampling points across the entire study area, at which the degrees of interrill and rill erosions are measured. The ground-truth erosion information collected at the 1328 locations is divided into training and reference data sets. Using the Tasseled Cap transformation technique, the brightness value of each pixel at the beginning (May), middle (July) and end (September) of growing season is obtained. By subtracting the May brightness value (BM) from the July one (BJ), and the July brightness value from the September one (BS), two new brightness images representing the brightness variations over May–July (BMJ) and July–September (BJS) are created. The two new brightness images are combined to generate a map where its pixels indicate the state (i.e. increase, I, decrease, D, and constant, C) of brightness variation over the two growing seasons. Using the measured interrill and rill erosion information at the training sampling locations, a unique relationship is found between the trend of brightness variation and the erosion intensity. This relationship is validated using the reference data sets. The results show that the proposed method is able to produce an interrill–rill erosion intensity map with an overall field-checked accuracy of 96% at this study location. The main advantages of this method are its high accuracy, its lower demands on time and funds for field work, and the ready availability of required data.

EXTRACTING VEGETATION INFORMATION FROM SATELLITE DATA FOR THE NILWALA CATCHMENT AREA, SOURTHERN SRI LANKA

It is evident that increasing human activities on forest resources can lead to floods, soilerision, loss of bio diversity, and increase in atmospheric gases causing global warming,Sustainable management of forest resources requires accurate data on the current status ofthe environment as well as tools to support the decision making process, Remote Sensingand Geographical Information Systems have increasingly been used as decision supporttools, Particulaly, recent advancements in satclitc remote sensing have facilitatedacquiring valuable data and information in assessing the current status of thc environmentat varying levels of detail. Thc purpose of this study is to assess the potential of satclitcremote sensing in vegetation mapping of the Nilwala catchment area of Southernn SriLanka, The paper presents thc preliminary work amid at extracting vegctation informationfrom Landsat TM and ETM data, The maps produced applying image processingtechniques such as image differencing, Normalised Difference Vegetation Index (NIJVI),ISOCLUSS unsupervised classification and Tasseled Cap Transformation show the extent,quality and change of vegetation in the catchment area, Information derived from satellitedata can further be used in modeling erosion potential in agricultural areas, mapping theflood risk, and land cover and land use change analysis when combine with geographicalinformation,

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Does the spatial arrangement of disturbance within forested watersheds affect loadings of nitrogen to stream waters? A test using Landsat and synoptic stream water data

Remotely sensed maps of forest disturbance provide a powerful tool for predicting spatial and temporal variability in the loading of nitrogen to receiving waters, key data needed for effective watershed management of nutrient pollution. We hypothesize that the spatial arrangement of disturbances within small-forested watersheds can affect N loadings. To test this, we developed schemes for spatially weighting maps of yearly disturbance produced through change analysis of the Landsat Tasseled Cap Disturbance Index (DI), and evaluated the ability of each scheme to predict N concentrations, and subsequently estimated N loads, from forty low-order streams within the Savage River drainage of western Maryland, USA during the 2006–2010 water years, a period encompassing extensive defoliations by gypsy moths (Lymantria dispar). We generated a base scheme of unweighted, watershed averaged change in DI (ΔDI), and five other schemes that weighted ΔDI by either a pixel's flow accumulation value, the distance to the watershed outlet, or proximity to the stream. Over the five years, the flow accumulation scheme tended to perform better than other weighting schemes, and even explained slightly more variability than the base scheme during years of moderate N loads (R2=0.15 vs. 0.03 in 2007 and R2=0.30 vs. 0.18 in 2010). However, this best spatial weighting scheme explained comparable or less variability during the two post-defoliation years with larger N loads (R2=0.43 vs. 0.44 in 2008 and R2=0.31 vs. 0.48 in 2009). Thus, for the purposes of utilizing remote sensing information within watershed management of nutrient pollution, these results suggest that coarse-scale, high temporal frequency data such as MODIS could be well suited for characterizing forest disturbance and predicting the resultant episodic N loads.

The procedure of seeking effective spectral indices for automated interpretation of water bodies (a case study of Belgorod oblast)

A case study has been made of the water bodies in Belgorod oblast in investigating the efficiency of improving spectral conversions of space images for purposes of automated interpretation of water bodies. A comparison is made of the initial image-based mapping accuracy of water bodies with values of 14 spectral indices. It is found that the Tasseled Cap Transformation can be employed in order to investigate in the greatest detail the spectral properties of water surfaces. Changes in the areas of water bodies of Belgorod oblast have been analyzed for the time interval from 1986 to 2011 on the basis of results of automated interpretation of space images.

Multi-scale standardized spectral mixture models

Linear spectral mixture models can be standardized by using endmembers that span the global mixing space. By combining the benefits of location-specific mixture models with standardized spectral indices, standardized mixture models offer consistency, simplicity, inclusivity and applicability. We construct a globally representative mixing space using a spectrally diverse collection of 100 Landsat ETM+(Thematic Mapper & Enhanced Thematic Mapper+) subscenes. Global composites of 100,000,000 Landsat spectra, constructed from both exoatmospheric reflectance and atmospherically corrected surface reflectance, represent the spectral diversity of a wide range of terrestrial environments. Principal Component (PC) Analysis of the global composite shows that 99% of the spectral variance can be represented in a 3-dimensional mixing space of the low order PCs. Within this 3D space 98% of spectra are contained within a tetrahedral hull bounded by a continuous plane of substrates, and well-defined apexes corresponding to vegetation and dark endmembers. Suites of individual substrate, vegetation and dark endmember spectra are used to derive mean endmembers and to quantify the effects of endmember variability on fractions estimated from a standardized Substrate, Vegetation, and Dark (SVD) linear mixture model. Maximum endmember variability introduces less than 0.05 difference in S, V, and D fractions for most SVD models constructed from individual pixel endmember spectra giving less than 0.05 model misfit for more than 97% of pixels in the global composite. The mean SVD endmembers define a standard global mixture model for Landsat spectra. These SVD endmembers can be used to model mixed reflectance spectra from other sensors with similar spectral responses to Landsat ETM+. Comparisons of endmember fractions estimated from coincident acquisitions of Landsat TM and ETM+and WorldView-2 imagery show strong linear scaling for vegetation and dark fractions. Substrate fractions do not scale as linearly for the urban validation sites because the Landsat substrate endmember does not accurately represent the impervious surfaces imaged by WorldView-2. Comparisons of Landsat and WorldView-2 unmixed with the same Visible-Near Infrared (VNIR) endmembers derived from the global Landsat endmembers are also strongly correlated but with reduced bias. This linear scaling suggests that the Landsat global endmembers may provide a basis for standardized mixture models for WorldView-2 and other broadband sensors with spectral response similar to Landsat TM and ETM+. Comparisons of vegetation fractions with vegetation indices for the global composite show strong linear correspondence for Tasseled Cap Greenness and Enhanced Vegetation Index, with some degree of saturation at high fractions for the Soil Adjusted Vegetation Index and a wide range of responses for the Normalized Difference Vegetation Index.

Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.

A Sensor Specified Method Based on Spectral Transformation for Masking Cloud in Landsat Data

Few uniform approaches designed for masking clouds by means of high resolution sensors have been widely developed due to the divergence in spectral channels. Here we propose an algorithm on a basis of two linear constraints among indexes acquired from the normalized Tasseled Cap (TC) transformation. This sensor specific approach requires neither thermal bands nor other reference images. The diversity of cloud type and cloud coverage (1%-20%) is analyzed for validating the present algorithm using nine scenes of the typical Landsat sensors. The first results derived from Landsat MSS and TM images are assessed in comparison with the Maximum Likelihood supervised classification method. Another comparison is then employed in analyzing the agreement between the automatic cloud cover assessment (ACCA) algorithm and our method. Although varying acceptable degrees of discrepancies are occurred among the above three methods in identifying thin clouds and cloud edges, the overall results show a fine agreement in detecting thick clouds and large clouds. In the event of the acquirement of the sensor-based parameters of TC transformation, this method could be developed for flagging clouds on other high resolution sensors, like SPOT, Quick-Bird, HJ, etc.

Object-based locust habitat mapping using high-resolution multispectral satellite data in the southern Aral Sea basin

Three different object-based image classification techniques are applied to high- resolution satellite data for the mapping of the habitats of Asian migratory locust (Locusta migratoria migratoria) in the southern Aral Sea basin, Uzbekistan. A set of panchromatic and multispectral Système Pour l’Observation de la Terre-5 satellite images was spectrally enhanced by normalized difference vegetation index and tasseled cap transformation and segmented into image objects, which were then classified by three different classification approaches: a rule-based hierarchical fuzzy threshold (HFT) classification method was com- pared to a supervised nearest neighbor classifier and classification tree analysis by the quick, unbiased, efficient statistical trees algorithm. Special emphasis was laid on the discrimination of locust feeding and breeding habitats due to the significance of this discrimination for practical locust control. Field data on vegetation and land cover, collected at the time of satellite image acquisition, was used to evaluate classification accuracy. The results show that a robust HFT classifier outperformed the two automated procedures by 13% overall accuracy. The classification method allowed a reliable discrimination of locust feeding and breeding habitats, which is of significant importance for the application of the resulting data for an economically and environmentally sound control of locust pests because exact spatial knowledge on the habitat types allows a more effective surveying and use of pesticides.

Mapping fire extent and burn severity in Alaskan tussock tundra: An analysis of the spectral response of tundra vegetation to wildland fire

Observed warming in the high northern latitudes has led to an increase in fire occurrence across North American tundra. Our ability to effectively monitor ecosystem change and the carbon cycle in this region depends upon the development of robust and reliable methods of extracting information about fire events and the characterization of fire impacts from remotely-sensed data. Previously developed and routinely applied remote sensing methods yield poor results in assessing the extent and severity of burning in tundra. Here we present an analysis of temporal trends in spectral signatures of burned and unburned areas of tussock tundra obtained from Landsat imagery within six fire events of the 2010 fire season in the Noatak Nature Preserve in Alaska. The analysis revealed a large variability in surface reflectance of burned and unburned areas across space and time. We established that spectral signatures of fire scars can be well separated by both single- and multi-date mapping approaches immediately after burning. However, the spectral signature of burned areas in tundra deteriorates rapidly and, in our study, is poorly distinguishable by the end of the first post-fire season. Our results showed that common mapping methods based on the normalized burn ratio were inferior to other spectral indices (Tasseled Cap Greenness and Brightness) and single Landsat bands (near-infrared) in separating burned and unburned areas as well as in mapping burn severity.

Discriminating the occurrence of pitch canker fungus in Pinus radiata trees using QuickBird imagery and artificial neural networks

Pathogenic fungi, such as Fusarium circinatum, present a serious threat to Pinus radiata plantations. The effective management of infected trees is thus paramount. Coupled with advanced techniques, high spatial resolution remote sensing data provides the necessary tools to effectively identify and map infected trees. This paper explores the utility of transformed high spatial resolution QuickBird imagery and artificial neural networks for the detection and mapping of pitch canker disease. Individual tree crowns were delineated using an automated segmentation and classification approach within an object-based image analysis environment. Subsequently, several vegetation indices including the tasseled cap transformation were calculated and incorporated into a neural network model. The feed-forward neural network showed high discriminatory power with an overall accuracy of 82.15% and KHAT of 0.65. The results of this study show great potential for the future application of crown-level mapping of the pitch canker disease at a landscape scale.

Assessment of carbon storage and biomass on minelands reclaimed to grassland environments using Landsat spectral indices

This study investigated carbon (C) storage and biomass in grasslands of West Virginia reclaimed surface minesites. Mine-related disturbance and subsequent reclamation may be an important component of C cycling. Biomass and C storage generally increased for the first five years after reclamation, but then declined, suggesting a nonlinear pattern to vegetation recovery. Three 2007 Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus images were used to assess the potential to predict biomass from raw red and near infrared radiance, the tasseled cap transformation (TC), and four vegetation indices [normalized difference vegetation index, enhanced vegetation index (EVI), triangular vegetation index, and the soil adjusted vegetation index]. TC greenness and EVI were most strongly correlated with biomass and illustrate a modest potential for monitoring vegetation recovery in reclaimed minelands. Additionally, a number of regression models that included age since reclamation and spectral indices were statistically significant suggesting a temporal recovery pattern amongst minesites in this study.

Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery

Bark beetles cause significant tree mortality in coniferous forests across North America. Mapping beetle-caused tree mortality is therefore important for gauging impacts to forest ecosystems and assessing trends. Remote sensing offers the potential for accurate, repeatable estimates of tree mortality in outbreak areas. With the advancement of multi-temporal disturbance detection methods using Landsat data, the capability exists for improvement in mapping methods, yet more information is needed to determine the accuracy of these methods for mapping forest disturbances and to quantify differences between these methods and single-date image classification methods. We compared single-date (using maximum likelihood classification) to multi-date (using time series of spectral indices) classification methods of Landsat imagery and investigated how detection accuracy changed with varying levels of mortality severity. For each method, we evaluated several bands and/or spectral vegetation indices and identified the one that resulted in the highest accuracy. A fine-resolution classified aerial image within the Landsat scene was used as reference data for evaluation and comparison between methods. For the single-date image classification, we achieved a 91.0% (kappa=0.88) overall accuracy with 11.7% omission and 2.3% commission errors for the red stage (tree mortality) class using the tasseled cap transformation indices of brightness, greenness, and wetness. For the multi-date analysis, the Band5/Band4 anomaly produced the highest accuracy among spectral indices and resulted in a 89.6% (kappa=0.86) classification accuracy with 12.6% omission and 7.1% commission errors for the red stage class. We compared accuracies between the best single- and multi-date methods across a range of tree mortality within a pixel. The multi-date method was more accurate at intermediate levels of tree mortality, whereas the single-date method was more accurate at high mortality levels. Our results indicate that Landsat-based mapping of forest disturbances that use either single-date or multi-date methods can result in high classification accuracy.