Cloud and cloud shadow masking for Sentinel-2 using multitemporal images in global area

ABSTRACT

Sentinel-2 data provided the opportunity for complementary data to existing missions including Landsat and SPOT. In this study, multitemporal cloud masking (MCM) used to detect cloud and cloud shadow masking for Landsat 8 was improved to detect cloud and cloud shadow for Sentinel-2 data. This improvement takes advantages of the spectral similarity between Landsat 8 and Sentinel-2. To assess the reliability of the new MCM algorithm, several data selected from different environments such as sub-tropical South, tropical, and sub-tropical North were evaluated. Moreover, these data have heterogeneous land cover and variety of cloud types. In visual assessment, the algorithm can detect cloud and cloud shadow accurately. In the statistical assessment, the user’s and producer’s accuracies of sample in sub-tropical environments of cloud masking was 99% and 96%, respectively, and cloud shadow masking was 99% and 98%, respectively. In addition, the user’s and producer’s accuracies of sample in tropical environments of cloud masking was 100% and 95%, respectively, and cloud shadow masking was 100% and 92%, respectively. Compared to Fmask, MCM has higher accuracies in most of the results of cloud and cloud shadow masking in both sub-tropical and tropical environments. The results showed that the improved-MCM algorithm can detect cloud and cloud shadow for Sentinel-2 data accurately in all scenarios and the accuracies are significantly high.     

Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA

A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.     

Object-based cloud and cloud shadow detection in Landsat imagery

A new method called Fmask (Function of mask) for cloud and cloud shadow detection in Landsat imagery is provided. Landsat Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) are used as inputs. Fmask first uses rules based on cloud physical properties to separate Potential Cloud Pixels (PCPs) and clear-sky pixels. Next, a normalized temperature probability, spectral variability probability, and brightness probability are combined to produce a probability mask for clouds over land and water separately. Then, the PCPs and the cloud probability mask are used together to derive the potential cloud layer. The darkening effect of the cloud shadows in the Near Infrared (NIR) Band is used to generate a potential shadow layer by applying the flood-fill transformation. Subsequently, 3D cloud objects are determined via segmentation of the potential cloud layer and assumption of a constant temperature lapse rate within each cloud object. The view angle of the satellite sensor and the illuminating angle are used to predict possible cloud shadow locations and select the one that has the maximum similarity with the potential cloud shadow mask. If the scene has snow, a snow mask is also produced. For a globally distributed set of reference data, the average Fmask overall cloud accuracy is as high as 96.4%. The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images.     

A change detection method using spatial-temporal-spectral information from Landsat images

ABSTRACT

Remote sensing data and techniques are reliable tools for monitoring land cover and land-use change. For time-series change detection algorithms, detecting the breakpoints accurately is the key element. However, the current state-of-art algorithms are vulnerable to cloud/cloud shadow or noises in the time-series imagery. The objective of this study is to develop a new method to detect land cover change using Landsat imagery by integrating temporal, spectral and spatial information to increase the accuracy of breakpoints detection. In the temporal dimension, the time-series model is decomposed into seasonality and trend. Due to different land cover types corresponding to different seasonal characteristics, breakpoints exist only in the seasonal component. In the spectral dimension, two-step judgement is applied. The first judgement detects a change when the seasonal breakpoint positions are the same in different spectral bands. The second judgement involves detecting a changed pixel when the classification result indicates different types on either side of the breakpoint. In the spatial dimension, neighbour information is utilized to control the false-positive rate. Experimental results using all available Landsat images acquired between 2001 and 2006 in Kansas City, US, illustrate the effectiveness and stability of the proposed approach. All pixels were used for assessing the classification and change detection accuracy compared with National Land Cover Database products. The overall accuracy of classification into eight categories was about 81% and the accuracy of change detection was 88%. Maps of timing of breaks and change times are also provided in this article.     

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000-2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.     

Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected. Several supervised classification approaches were tested: spectral classification of single Landsat data, temporal classification of normalized difference vegetation index time series from Landsat images, and two-step classification integrating both these approaches. The accuracy of the land-cover maps obtained ranges between 80% and 90% overall for the two-step classification approach. Comparison of the maps between the two years indicates a stable semi-arid region, where some change in hot spots exists despite a generally constant level of rainfall in the area during this period. In particular, the Dallol Bosso fossil valley highlights an increase in cultivated land, while a decrease in herbaceous vegetation was observed outside the valley where rangeland is the predominant natural landscape.     

Ten years of forest cover change in the Sierra Nevada detected using Landsat satellite image analysis

The Sierra Nevada of California is a region where large wildfires have been suppressed for over a century. A detailed geographic record of recent vegetation regrowth and disturbance patterns in forests of the Sierra Nevada remains a gap that can be filled with remote-sensing data. Landsat Thematic Mapper imagery was analysed to detect 10 years of recent changes (between 2000 and 2009) in forest vegetation cover for areas burned by wildfires between years of 1995 and 1999 in the region. Results confirmed the prevalence of regrowing forest vegetation during the period 2000 and 2009 over 17% of the combined burned areas. Classification of these regrowing forest vegetation areas by the Landsat normalized burn ratio (NBR) showed that there was a marked increase in average disturbance index (ΔDI) values in the transitions from low to moderate to high burn severity classes. Within the five combined wildfire perimeters, 45% of the high burn severity area delineated by the RdNBR analysis was covered by regrowing forest detected between 2000 and 2009. In contrast, a notable fraction (12%) of the entire 5 km (unburned) buffer area outside the 1995–1999 fires perimeters showed decline in forest cover, and not nearly as many regrowing forest areas, covering only 3% of all the 1995–1999 buffer areas combined. Based on comparison of these results to ground-based survey data and high-resolution aerial images, the Landsat difference index (ΔDI) methodology can fulfil much of the need for consistent, low-cost monitoring of changes due to climate and biological factors in western forest regrowth following stand-replacing disturbances.     

Predicting forest successional stages using multitemporal Landsat imagery with forest inventory and analysis data

Forest succession is an important ecological process that has profound biophysical, biological and biogeochemical implications in terrestrial ecosystems. Therefore, information on forest successional stages over an extensive forested landscape is crucial for us to understand ecosystem processes, such as carbon assimilation and energy interception. This study explored the potential of using Forest Inventory and Analysis (FIA) plot data to extract forest successional stage information from remotely sensed imagery with three widely used predictive models, linear regression (LR), decision trees (DTs) and neural networks (NNs). The predictive results in this study agree with previous findings that multitemporal Landsat Thematic Mapper (TM) imagery can improve the accuracy of forest successional stage prediction compared to models using a single image. Because of the overlap of spectral signatures of forests in different successional stages, it is difficult to accurately separate forest successional stages into more than three broad age classes (young, mature and old) with reasonable accuracy based on the age information of FIA plots and the spectral data of the plots from Landsat TM imagery. Given the mixed spectral response of forest age classes, new approaches need to be explored to improve the prediction of forest successional stages using FIA data.     

Procedures for change detection using Landsat digital data

Resource management agencies are interested in knowing when and how satellite data can be effectively used to monitor environmental change and what information can be expected from remote sensing techniques. In this paper the procedures that might be followed in applying the complementary methods of band ratioing and post-classification change detection to monitor a large remote area are outlined. Examples from research in the Peace-Athabasca Delta are used to illustrate the procedures and expected results.     

Contextual classification of Landsat TM images to forest inventory cover types

In the context of Landsat TM images forest stands are a cluster of homogeneous pixels. Contextual classification of forest cover types exploits relationships between neighbouring pixels in the pursuit of an increase in classification accuracy. Results with six contextual classifiers from two sites in Canada were compared to results with a maximum likelihood (ML) classifier. The comparisons were done at three levels of spectral class separation. Training and validation data were obtained from single-stage cluster sampling of 2?km×2?km primary sampling units (PSU) located on a 20?km×20?km grid. A strong relationship between contextual and ML classification accuracy was explored with logistic regression analysis. Effects of contextual classification were predicted for given levels of ML accuracy. Estimates of the spatial autocorrelation of reflectance values within a PSU were deemed consistent with a first-order autoregressive process. Iterative Conditional Modes (ICM) was the best contextual method; it improved the overall accuracy by four to six percentage points (statistically significant) when ML accuracy was between 50% and 80%. A relaxed ICM and a smoothing algorithm were second and third best. Contextual classification is most promising when an ML accuracy is around 70%. ICM results were sensitive to the level of spatial autocorrelation of ML classification errors and to the homogeneity of a PSU.     

Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images

The homogeneity of time series of satellite images is crucial when studying abrupt or gradual changes in vegetation cover via remote sensing data. Various sources of noise affect the information received by satellites, making it difficult to differentiate the surface signal from noise and complicates attempts to obtain homogeneous time series. We compare different procedures developed to create homogeneous time series of Landsat images, including sensor calibration, atmospheric and topographic correction, and radiometric normalization. Two seasonal time series of Landsat images were created for the middle Ebro Valley (NE Spain) covering the period 1984–2007. Different processing steps were tested and the best option selected according to quantitative statistics obtained from invariant areas, simultaneous medium-resolution images, and field measurements. The optimum procedure includes cross-calibration between Landsat sensors, atmospheric correction using complex radiative transfer models, a non-lambertian topographic correction, and a relative radiometric normalization using an automatic procedure. Finally, three case studies are presented to illustrate the role of the different radiometric correction procedures when analyzing and explaining gradual and abrupt temporal changes in vegetation cover, as well as temporal variability. We have shown that to analyze different vegetation processes with Landsat data, it is necessary to accurately ensure the homogeneity of the multitemporal datasets by means of complex radiometric correction procedures. Failure to follow such a procedure may mean that the analyzed processes are non-recognizable and that the obtained results are invalid.     

MODIS图像云检测业务化算法研究

本文针对3种不同空间分辨率的MODIS数据(250m,500m,1km),提出相应的业务化云检测算法,采用多阈值判别云像元,算法简单有效,仅以图像作为输入,不需要其它辅助数据的支持。以我国中低纬度地区为实验区,分别选取了春夏秋冬4幅MODIS图像,根据图像选择了合理的云判别阈值,取得了较好的云检测结果,与美国宇航局(NASA)提供的MODIS云产品相比,云的空间分布基本一致,且很好地更正了MODIS云产品中被误判为云区的河流、湖水、岸边的区域,从而可以获得更高的云检测精度和提高云覆盖判别的效率,为建立业务化运行的遥感图像云剔除预处理流程奠定基础。     

Estimating east Mediterranean forest parameters using Landsat ETM

The conservation of Jordan's Mediterranean forest requires the use of remote sensing. Among the most important parameters needed are the crown-cover percentage (C) and above-ground biomass (A). This study aims to: (1) identify the best predictor(s) of C using Landsat Enhanced Thematic Mapper (ETM) bands and the derived transformed normalized difference vegetation index (TNDVI); (2) determine if C is a good predictor of A, volume (V), Shannon diversity index (S) and basal area (B); and (3) generate maps of all these parameters. A Landsat ETM image, aerial photographs and ground surveys are used to model C using multiple regression. C is then modelled to A, V, S and B using linear regression. The relationship between C and Landsat ETM bands (1 and 7) plus the TNDVI is significantly high (coefficient of determination R ² = 0.8) and is used to produce the C map. The generated C map is used to predict A (R ² = 0.56), V (R ² = 0.58), S (R ² = 0.50) and B (R ² = 0.43). Cross validation for the predicted C map (cross-validation error = 5.3%) and for the predicted forest-parameter maps (cross-validation error = 13.7%–19.9%) shows acceptable error levels. Results indicate that Jordan's east Mediterranean forest parameters can be mapped and monitored for biomass accumulation and carbon dioxide (CO₂) flux using Landsat ETM images.     

Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon

Deforestation is the replacement of forest by other land use while degradation is a reduction of long-term canopy cover and/or forest stock. Forest degradation in the Brazilian Amazon is mainly due to selective logging of intact/un-managed forests and to uncontrolled fires. The deforestation contribution to carbon emission is already known but determining the contribution of forest degradation remains a challenge. Discrimination of logging from fires, both of which produce different levels of forest damage, is important for the UNFCCC (United Nations Framework Convention on Climate Change) REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program. This work presents a semi-automated procedure for monitoring deforestation and forest degradation in the Brazilian Amazon using fraction images derived from Linear Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper (TM) scene (path/row 226/068) covering part of Mato Grosso State in the Brazilian Amazon, was selected to develop the proposed method. First, the approach consisted of mapping deforested areas and mapping forest degraded by fires using image segmentation. Next, degraded areas due to selective logging activities were mapped using a pixel-based classifier. The results showed that the vegetation, soil, and shade fraction images allowed deforested areas to be mapped and monitored and to separate degraded forest areas caused by selective logging and by fires. The comparison of Landsat Operational Land Imager (OLI) and RapidEye results for the year 2013 showed an overall accuracy of 94%. We concluded that spatial resolution plays an important role for mapping selective logging features due to their characteristics. Therefore, when compared to Landsat data, the current availability of higher spatial and temporal resolution data, such as provided by Sentinel-2, is expected to improve the assessment of deforestation and forest degradation, especially caused by selective logging. This will facilitate the implementation of actions for forest protection.     

Automated analysis of DNA hybridization images for high-throughput genomics

The design and implementation of a computer vision system called DNAScan for the automated analysis of DNA hybridization images is presented. The hybridization of a DNA clone with a radioactively tagged probe manifests itself as a spot on the hybridization membrane. The imaging of the hybridization membranes and the automated analysis of the resulting images are imperative for high-throughput genomics experiments. A recursive segmentation procedure is designed and implemented to extract spotlike features in the hybridization images in the presence of a highly inhomogeneous background. Positive hybridization signals (hits) are extracted from the spotlike features using grouping and decomposition algorithms based on computational geometry. A mathematical model for the positive hybridization patterns and a Bayesian pattern classifier based on shape-based moments are proposed and implemented to distinguish between the clone-probe hybridization signals. Experimental results on real hybridization membrane images are presented.Received: 25 June 2002, Accepted: 11 November 2003, Published online: 17 February 2004 Correspondence to: Suchendra M. Bhandarkar     

采用遥感影像的林地建模研究

提出了一种全新的林地建模方法,对生长着树木的林地获取地面序列影像,利用影像分割实现树木信息的自动识别,通过基于地面区域约束的点云提取算法获得地面高精度密集点云,最后应用三次Beta样条曲线插值模型实现了林地等高线的建模。该方法将摄影测量技术应用于林地建模,解决了无法对有树木遮挡的地面进行建模的难题,提高了林地建模的效率。     

A new Landsat 8 cloud discrimination algorithm using thresholding tests

ABSTRACT

In this work, we propose a Cloud Discrimination Algorithm for Landsat 8 (CDAL8) to improve a high-frequency automatic land change detection system developed at the National Institute of Advanced Industrial Science and Technology (AIST), Japan for large-scale satellite image analysis. Although the land change detection system can process several kinds of satellite remote sensing data, improvements are needed to enable practical applications using Landsat 8 data. Cloud discrimination is a necessary pre-processing step for land cover change detection. Currently, most of the prediction errors on land change detection are caused by the false cloud discrimination results as a pre-processing step. Therefore, we introduce an improved cloud discrimination algorithm (CDAL8) in this study to improve the overall performance of our land change detection system. The algorithm was developed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm and Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). CDAL8 is distinct in that it switches judgment tests and their thresholds using a threshold brightness temperature and uses separate features in cloud judgment and clear-sky judgment. To evaluate the accuracy of the proposed algorithm, we compared it with the Automated Cloud-Cover Assessment algorithm (ACCA) and Function of Mask (Fmask) version 3.3 using US Geological Survey Landsat 8 cloud cover assessment validation data, which contain 96 cloud masks. Our proposed cloud discrimination algorithm (CDAL8) have promising results with an accuracy of 88.1%, which was greater than that of the ACCA (82.5%) and Fmask (84.6%). Furthermore, we also confirmed that the average accuracy of CDAL8 was approximately 91.2% when low solar elevation scenes were removed.     

Issues related to delineation of forest boundaries on Landsat Thematic Mapper winter images

This paper describes an approach for estimating the effect of factors influencing the determination of forest boundaries on medium-resolution satellite images. Forest edges were delineated on a Landsat Thematic Mapper (TM) image made in late winter under plain snow cover conditions. The study investigated the best Landsat TM spectral band and threshold value for the detection of forest edges in winter images. It was hypothesized that shadows cast by trees on forest edges on the bright snow of the surrounding open area make north- or north-west-facing forest edges less sharp than edges facing in other directions. If this holds true for medium-resolution Landsat TM satellite images, forest area change studies should carefully consider images taken under different atmospheric and solar elevation conditions in order to distinguish real changes at forest edges from those stemming from different conditions of solar illumination. The results of the study show that there is no significant shadow effect, as the reflectance contrast at forest edges exposed in different azimuthal directions does not differ on Landsat TM winter images under plain snow cover conditions. Landsat TM bands 2-4 are all equally good for the detection of the forest edge location at an average value of reflectance. These results are valid for forest edges that have remained stable for several decades.     

Volcanic ash cloud detection from remote sensing images using principal component analysis

Thermal infrared remote sensing can quickly and accurately detect the volcanic ash cloud. However, remote sensing data have pretty strong inter-band correlation and data redundancy, both of which have decreased to a certain degree the detecting accuracy of volcanic ash cloud. Principal component analysis (PCA) can compress a large number of complex information into a few principal components and overcome the correlation and redundancy. Taking the Eyjafjallajokull volcanic ash cloud formed on April 19, 2010 for example, in this paper, the PCA is used to detect the volcanic ash cloud based on moderate resolution imaging spectroradiometer (MODIS) remote sensing image. The results show that: the PCA can successfully acquire the volcanic ash cloud from MODIS image; the detected volcanic ash cloud has a good consistency with the spatial distribution, SO₂ concentration and volcanic absorbing aerosol index (AAI).