A Statistical Framework for Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS Data

Tang

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2016

ABSTRACT:Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., zvalue or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

Tang

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2016

“…An alternative is using MODIS products that have finer temporal resolutions. For example, Anees and Aryal [60] developed a near real-time detection framework for occurrence of beetle infestation in pine forests using the time series of eight-day 500 m spatial resolution MODIS data collected over five years. In this framework, each of seven vegetation indices was fit by an underlying triply modulated cosine model to derive a stationary vegetation index time series.…”

Section: Methods Comparison By Estimation Accuracymentioning

confidence: 99%

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

et al. 2019

China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.

IEEE Geosci. Remote Sensing Lett.

“…It is worth to mention that SAR change detection based on image stack at regular intervals of time has been experimented. The systems like MODIS can provide high temporal resolution data [4]. The applications can be found in e.g.…”

Section: Introductionmentioning

confidence: 99%

Wavelength-Resolution SAR Incoherent Change Detection Based on Image Stack

2017

Abstract-The paper presents a wavelength-resolution SAR incoherent change detection method based on image stack, i.e. there are more than one reference or/and surveillance image. Considering image stack in statistical hypothesis test for change detection is expected to result into a simple mathematical expression for implementation and provide better change detection results. As presented in this paper, a statistical hypothesis test is developed on bivariate Gaussian distribution for an image stack of two reference and one surveillance images. The requirement for the image stack is three images associated with three measurements with no change between two of them. A detection method with simple processing scheme is proposed. The method is experimented with 24 CARABAS data sets. The results indicate that high average detection probability, e.g. 96%, with very low false alarm rate, e.g. only 0.19 per square kilometer, is obtained with the proposal.