Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images

Abstract We developed a new algorithm called MFmask (Mountainous Fmask) for automated cloud and cloud shadow detection for Landsats 4–8 images acquired in mountainous areas. The MFmask algorithm, built upon the success of the Fmask algorithm (Zhu and Woodcock, 2012; Zhu et al., 2015), is designed for cloud and cloud shadow detection in mountainous areas, where the Fmask algorithm is not performing well. The inputs of the MFmask algorithm include Landsat Top of Atmosphere (TOA) reflectance, Brightness Temperature (BT), and Digital Elevation Models (DEMs). Compared to Fmask, MFmask can separate water and land pixels better in mountainous areas with the aid of DEMs. Moreover, MFmask produces better cloud detection results than Fmask in mountainous areas after BT is linearly normalized by DEMs. To provide more accurate cloud shadow detection in mountainous areas, MFmask uses a double-projection approach to better predict cloud shadow shape on slope side. Additionally, MFmask applies a topographic correction to remove terrain shadows and estimates cloud base height with neighboring clouds. Both will reduce the possibility of cloud and cloud shadow mismatch and increase cloud shadow detection accuracy for places with large topographic gradient. To test the performance of the proposed MFmask algorithm, a total of 67 Landsat images acquired in mountainous areas from different parts of the world were selected for assessing the accuracy of cloud detection, in which 15 of them were used for assessing the accuracy of cloud shadow detection. Compared with Fmask, MFmask can provide substantial improvements in cloud and cloud shadow detection accuracies for places with large topographic gradient and also work well for relatively flat terrain.

[1]  J. Cihlar,et al.  An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images , 2002 .

[2]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[3]  M. Wulder,et al.  Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data , 2011 .

[4]  Xing Li,et al.  A Bayesian Network-Based Method to Alleviate the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy Water Content Retrieval , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[6]  S. Goward,et al.  Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) Algorithm , 2006 .

[7]  A. Fisher,et al.  Comparing Landsat water index methods for automated water classification in eastern Australia , 2016 .

[8]  Gérard Dedieu,et al.  A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images , 2010 .

[9]  Kun Yang,et al.  Temperature lapse rate in complex mountain terrain on the southern slope of the central Himalayas , 2013, Theoretical and Applied Climatology.

[10]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[11]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[12]  E Brown de Colstoun,et al.  National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier , 2003 .

[13]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[14]  G. Asner,et al.  Cloud cover in Landsat observations of the Brazilian Amazon , 2001 .

[15]  Heather Reese,et al.  C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter , 2011 .

[16]  Bin Wang,et al.  Automated Detection and Removal of Clouds and Their Shadows from Landsat TM Images , 1999 .

[17]  Justin Braaten,et al.  Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems , 2015 .

[18]  David J. Selkowitz,et al.  An Automated Approach for Mapping Persistent Ice and Snow Cover over High Latitude Regions , 2015, Remote. Sens..

[19]  Richard R. Irish,et al.  Landsat 7 automatic cloud cover assessment , 2000, SPIE Defense + Commercial Sensing.

[20]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[21]  John L. Dwyer,et al.  Development of the Landsat Data Continuity Mission Cloud-Cover Assessment Algorithms , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Joanne C. White,et al.  An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites , 2015 .

[23]  Hao Wang,et al.  Water body mapping method with HJ-1A/B satellite imagery , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[24]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[25]  P. Teillet,et al.  On the Slope-Aspect Correction of Multispectral Scanner Data , 1982 .

[26]  Bruce A. Wielicki,et al.  Cumulus cloud base height estimation from high spatial resolution Landsat data: a Hough transform approach , 1992, IEEE Trans. Geosci. Remote. Sens..

[27]  Akira Iwasaki,et al.  Characteristics of ASTER GDEM version 2 , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[28]  Chengquan Huang,et al.  Automated masking of cloud and cloud shadow for forest change analysis using Landsat images , 2010 .

[29]  Collin G. Homer,et al.  Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA , 2013 .

[30]  Jane Southworth,et al.  Modeling Spatially and Temporally Complex Land-Cover Change: The Case of Western Honduras* , 2004, The Professional Geographer.

[31]  Michael J. Wilson,et al.  Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands , 2011, IEEE Geoscience and Remote Sensing Letters.

[32]  Wei Deng,et al.  Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions , 2012 .

[33]  Dean L. Urban,et al.  Spatial estimation of air temperature differences for landscape-scale studies in montane environments , 2003 .

[34]  Michael J. Wilson,et al.  Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data Continuity Mission , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Zhe Zhu,et al.  Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .

[36]  Lloyd L. Coulter,et al.  Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery , 2016 .

[37]  Binbin He,et al.  Retrieval of leaf area index in alpine wetlands using a two-layer canopy reflectance model , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[38]  Warren B. Cohen,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation , 2010 .

[39]  M. Bauer,et al.  Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing , 2005 .

[40]  Hyeungu Choi,et al.  Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision , 2004 .

[41]  W. Featherstone,et al.  Comparison and validation of the recent freely available ASTER-GDEM ver1, SRTM ver4.1 and GEODATA DEM-9S ver3 digital elevation models over Australia , 2010 .

[42]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[43]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[44]  Xiao Zheng,et al.  Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests , 2015, Remote. Sens..

[45]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[46]  Christopher E. Holden,et al.  Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014) , 2016 .

[47]  C. Verpoorter,et al.  Automated mapping of water bodies using Landsat multispectral data , 2012 .