Digital change detection of forest conversion of a dry tropical Indian forest region

Monitoring of the changes in the forest cover has been quite important because of its significant impact on climatic change. The following study was undertaken to monitor the conversion of vegetation cover to categories of different canopy cover in an area that has undergone very rapid industrialization in the recent past. 1982 and 1989 same season MSS data was registered and the atmospheric haze was reduced using a regression method. Conversion of the vegetation categories to the other categories was detected from the colour-sliced PC2 image of the subtracted bands.     

Retrieval of forest canopy attributes based on a geometric-optical model using airborne LiDAR and optical remote-sensing data

In the retrieval of forest canopy attributes using a geometric-optical model, the spectral scene reflectance of each component should be known as prior knowledge. Generally, these reflectances were acquired by a foregone survey using an analytical spectral device. This article purposed to retrieve the forest structure parameters using light detection and ranging (LiDAR) data, and used a linear spectrum decomposition model to determine the reflectances of the spectral scene components, which are regarded as prior knowledge in the retrieval of forest canopy cover and effective plant area index (PAI_e) using a simplified Li–Strahler geometric-optical model based on a Satellites Pour l'Observation de la Terre 5 (SPOT-5) high-resolution geometry (HRG) image. The airborne LiDAR data are first used to retrieve the forest structure parameters and then the proportion of the SPOT pixel not covered by crown or shadow K_g of each pixel in the sample was calculated, which was used to extract the reflectances of the spectral scene components by a linear spectrum decomposition model. Finally, the forest canopy cover and PAI_e are retrieved by the geometric-optical model. As the acquired time of SPOT-5 image and measured data has a discrepancy of about 2 months, the retrieved result of forest canopy cover needs a further validation. The relatively high value of R ² between the retrieval result of PAI_e and the measurements indicates the efficiency of our methods.     

OBCD-HH: an object-based change detection approach using multi-feature non-seed-based region growing segmentation

There is an increasing need to get updated information regarding the changes on earth’s surface. The information obtained can be used in a wide range of applications including disaster management, land-use investigation etc. The high-resolution remote sensing images obtained from satellites provide us with an opportunity to detect changes on earth’s surface between various time intervals. In this paper, an unsupervised object-based change detection (OBCD) method is proposed to detect changes in high resolution bi-temporal satellite images. To detect changes, a novel multi-feature non-seed-based region growing (MF-NSRG) algorithm is proposed for image segmentation based on heterogeneity minimization that uses textural heterogeneity along with spectral and spatial heterogeneity during region growing. The performance of MF-NSRG algorithm is further improved by using Harris Hawk, a recently proposed metaheuristic algorithm, which is used to obtain optimal values of segmentation parameters. Finally, the feature maps extracted from the pre-change and post-change segmented images are analysed using histogram trend similarity (HTS) approach to detect changes. The proposed approach is known as object-based change detection using Harris Hawk (OBCD-HH). The proposed OBCD-HH approach is applied on two datasets: xBD and Onera Satellite Change Detection (OSCD) dataset. Its performance is compared with existing state-of-the-art algorithms and results show the superiority of the proposed approach.

     

Wetland change mapping for the U.S. mid-Atlantic region using an outlier detection technique

Although the impacts of wetland loss are often felt at regional scales, effective planning and management require a comparative assessment of local needs, costs, and benefits. Satellite remote sensing can provide spatially explicit, synoptic land cover change information to support such an assessment. However, a common challenge in conventional remote sensing change detection is the difficulty of obtaining phenologically and radiometrically comparable data from the start and end of the time period of interest. An alternative approach is to use a prior land cover classification as a surrogate for historic satellite data and to examine the self-consistency of class spectral reflectances in recent imagery. We produced a 30-meter resolution wetland change probability map for the U.S. mid-Atlantic region by applying an outlier detection technique to a base classification provided by the National Wetlands Inventory (NWI). Outlier-resistant measures – the median and median absolute deviation – were used to represent spectral reflectance characteristics of wetland class populations, and formed the basis for the calculation of a pixel change likelihood index. The individual scene index values were merged into a consistent region-wide map and converted to pixel change probability using a logistic regression calibrated through interpretation of historic and recent aerial photography. The accuracy of a regional change/no-change map produced from the change probabilities was estimated at 89.6%, with a Kappa of 0.779. The change probabilities identify areas for closer inspection of change cause, impact, and mitigation potential. With additional work to resolve confusion resulting from natural spatial heterogeneity and variations in land use, automated updating of NWI maps and estimates of areal rates of wetland change may be possible. We also discuss extensions of the technique to address specific applications such as monitoring marsh degradation due to sea level rise and mapping of invasive species.     

Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series

Monitoring land cover over large areas on a yearly basis is challenging. The spatial and temporal consistency of an object-based change detection algorithm was tested through a multi-year application on the forest of Borneo, using SPOT-VEGETATION time series from 2000 to 2008. Continuous change thresholds allowed the tuning of the algorithm according to specific requirements in terms of omission and commission errors. The accuracy of the method was assessed using the ROC (relative operating characteristics) curves, which were found useful to evaluate the performance of the method independently of the selected threshold and to support the selection of an optimal threshold. The forest area that annually changed between 2000 and 2008 was detected and a cumulative change map was produced. The resulting change rates and the distribution of the forest change patterns were in agreement with other sources of information. These results demonstrated the very promising temporal consistency of the proposed approach. Further work aims at testing it at larger scales.     

An automated binary change detection model using a calibration approach

An automated binary change detection model using a threshold-based calibration approach was introduced in the study. The burdensome processes required in binary change detection, including calibration, calculation of accuracy, extraction of optimum threshold(s), generation of a binary change mask, and removal of “salt-and-pepper” noise were integrated and automated in the model. For practical purpose, the model was implemented as a dynamic linked library in ESRI ArcMap 9.1 using Visual Basic. This study demonstrated the model with a variety of single and multiple variables (layers) extracted from multiple-date QuickBird imagery for three study sites in Las Vegas, NV and two study sites in Tucson, AZ. The use of multiple variables in binary change detection resulted in significantly better performance than single variables.     

Early structural change detection as an optimal stopping problem: solution theorem and its proof using reduction to absurdity

The change point detection (CPD) problem in a time series is when it is found that the structure of the data being generated has changed at some time and for some reason. We have formulated structural change detection in a time series as an optimal stopping problem using the concept of dynamic programming (DP), and we present the optimal solution and its correctness by numerical calculations. In this article, we present the solution theorem and its proof using reduction to absurdity.     

Extended SPRT for structural change detection of time series based on a multiple regression model

It is important to detect a structural change in a time series quickly as a trigger to remodeling the forecasting model. The well-known Chow test has been used as the standard method for detecting change, especially in economics. However, we have proposed the application of the sequential probability ratio test (SPRT) for detecting the change in single-regression modeled time-series data. In this article, we show experimental results using SPRT and the Chow test when applied to time-series data that are based on multiple regression models. We also clarify the effectiveness of SPRT compared with the Chow test in its ability to detect change early and correctly, and its computational complexity. Moreover, we extend the definition of the point at which structural change is detected with the SPRT method, and show an improvement in the accuracy of change detection.     

Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model

A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multi-angle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and a hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semi-arid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li-Ross BRDF model; diffuse brightness (ρ0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li-Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km² area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r² = 0.78, RMSE = 0.013, N = 3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r² = 0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets.     

Visual tracking using structural local DCT sparse appearance model with occlusion detection

Multimedia Tools and Applications - In this paper, a structural local DCT sparse appearance model with occlusion detection is proposed for visual tracking in a particle filter framework. The energy...     

Trajectory-based change detection for automated characterization of forest disturbance dynamics

Satellite sensors are well suited to monitoring changes on the Earth's surface through provision of consistent and repeatable measurements at a spatial scale appropriate for many processes causing change on the land surface. Here, we describe and test a new conceptual approach to change detection of forests using a dense temporal stack of Landsat Thematic Mapper (TM) imagery. The central premise of the method is the recognition that many phenomena associated with changes in land cover have distinctive temporal progressions both before and after the change event, and that these lead to characteristic temporal signatures in spectral space. Rather than search for single change events between two dates of imagery, we instead search for these idealized signatures in the entire temporal trajectory of spectral values. This trajectory-based change detection is automated, requires no screening of non-forest area, and requires no metric-specific threshold development. Moreover, the method simultaneously provides estimates of discontinuous phenomena (disturbance date and intensity) as well as continuous phenomena (post-disturbance regeneration). We applied the method to a stack of 18 Landsat TM images for the 20-year period from 1984 to 2004. When compared with direct interpreter delineation of disturbance events, the automated method accurately labeled year of disturbance with 90% overall accuracy in clear-cuts and with 77% accuracy in partial-cuts (thinnings). The primary source of error in the method was misregistration of images in the stack, suggesting that higher accuracies are possible with better registration.     

Studying the change in fAPAR after forest fires in Siberia using MODIS

Disturbance events such as fire have major effects on forest dynamics, succession and the carbon cycle in the boreal biome. This paper focuses on establishing whether characteristic spatio‐temporal patterns of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) occur in the initial two years after a fire event in Siberian boreal forests. Time‐series of MODIS fAPAR were used to study post‐fire dynamics during the year of the fire and the following two years. Three forest types (evergreen needle‐leaf, deciduous needle‐leaf and deciduous broadleaf) grouped into three latitudinal regions, ranging from 51° N to 65° N, were studied by analysing a sample of 14 burned areas. For each of the burned areas an adjacent unburned control plot was selected with the aim of separating inter‐annual variations caused by climate from changes in fAPAR behaviour due to a burn. The results suggest that (i) the forest types exhibit characteristic fAPAR change trajectories shortly after the fire, (ii) the differences in the fAPAR trajectories are related to the forest type, (iii) fAPAR changes are not significantly different among the latitudinal regions, and (iv) the limited temporal variability observed among the 3 years of observations indicates that fAPAR varies very little in the initial years after a fire event.     

Multi-scale chemo-mechanical analysis of the slip surface of landslides in the Three Gorges, China

Multi-scale chemo-mechanical effects and microscopic failure modes are explored in the evolution of strength change of slip surface. Direct shear equipments, scanning electro-microscope and X-ray diffraction are used to trace the change in strength of remodeled soils of slip surfaces in the Three Gorges area. Results show that there is a release of alkali metals and concentration of clay minerals on the surface. During the tests, potassium ions were released, the cementation was reduced, and the ratio of in...     

A multi-group analysis of structural invariance: an illustration using the technology acceptance model

Selecting the appropriate mix of functional and/or interface characteristics to achieve user acceptance has proven to be a more challenging and difficult decision than expected. While numerous studies have shown that the technology acceptance model (TAM) is useful for predicting acceptance, estimates of its structural weights are not consistent across studies. Using initial exposure data from 742 users of office suite applications (i.e., spreadsheet, database, word processing, and graphics), our research illustrated the use of multi-group analysis of structural invariance (MASI) to test differences in structural weights across population subgroups for latent variables in TAM. We argue that, for large sample studies containing latent variables, MASI may be a more appropriate test of differences for structural weights/regression coefficients than analysis of covariance. The managerial implications of the results in setting functionality and interface goals and allocating resources to continued development efforts are discussed.     

Detection of forest change in the Green Mountains of Vermont using Multispectral Scanner data

Abstract

This study evaluates the potential of Landsat Multispectral Scanner (MSS) data to monitor long term changes in high-elevation coniferous forests. August 1973 and 1984 MSS data from the Green Mountains of Vermont were used. Following co-registration and standardization of data sets, colour density slices of 0.75 μm and 0·95 μm band difference images were produced. False colour composites using either (a) the 1973 0·65 μm and 0·75 μm band in conjunction with the 0.75 μm band difference data set or (b) the 1973 0·65 μm and 0·95 μm band in conjunction with the 0.95 μm band difference data set, were also produced. These images indicated that major reflectance decreases occurred for the near-infrared bands between 1973 and 1984 for much of the high-elevation forests. This includes the west-facing slope of Camels Hump, an area for which long term records documenting decreases in basal area and inferred biomass are available. It is believed that the reflectance decreases noted for the high-elevation coniferous forests are related to the forest decline process and are associated with reduction in green leaf biomass and/or the increased levels of mortality and higher amounts of dead branches accompanying this reduction in biomass.     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

Feature detection using the general linear model

The detection of simple features using position-invariant filters constructed by the use of the matched filter theorem is extended to the general case of features with more than one free parameter.     

A forest inventory using LANDSAT imagery in the Mao-shan area of China

This paper presents a forest inventory study of the Mao-shan area, a region which is partly representative of the forest types of southern China. In the study, the effectiveness of various feature extraction techniques was investigated, new classification algorithms were developed and supervised classification schemes were implemented and assessed. A scheme involving two-dimensional spectrum decomposition classification, manual editing and Bayes classification is proposed. Its application gives results which show promising potential for forest inventory in southern China using computer processing of LANDSAT imagery.     

A novel approach based on structural information for change detection in SAR images

Structural information, extracted by simulating the human visual system (HVS), is independent of viewing conditions and individual observers. Structural similarity (SSIM), a measure of similarity between two images, has been widely used in image quality assessment. Given the fact that the change detection techniques identify the changed area by the similarity of multi-temporal images, SSIM has significant prospect in change detection of synthetic aperture radar (SAR) images. However, the experimental results show that SSIM performs worse in change detection of multi-temporal SAR images. In this study, we first propose an advanced SSIM (ASSIM) based on a two-step assumption of extracting structural information and a visual attention measure (VAM) model. Then, we propose a novel approach based on ASSIM for change detection in SAR images. SSIM, ASSIM, and state-of-the-art methods are tested on two datasets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can acquire a better difference image than SSIM and other state-of-the-art methods, and improve the accuracy of change detection in SAR images effectively.     

Queries on change in an extended relational model

A data model that allows for the storage of detailed change history in so-called backlog relations is described. Its extended relational algebra, in conjunction with the extended data structures, provides a powerful tool for the retrieval of patterns and exceptions in change history. An operator, Σ, based on the notion of compact active domain is introduced. It groups data not in predefined groups but in groups that fit the data. This operator further expands the retrieval capabilities of the algebra. The expressive power of the algebra is demonstrated by examples, some of which show how patterns and exceptions in change history can be detected. Sample applications of this work are statistical and scientific databases, monitoring (of databases, manufacturing plants, power plants, etc.), CAD, and CASE