Accuracy assessment of ICESat-2 ATL 08 terrain estimates:A case study in Spain

The Ice,Cloud and Land Elevation Satellite-2(ICESat-2),a new spaceborne light detection and ranging(LiDAR)system,was successfully launched on September 15,2018.The ICESat-2 data increase the types of spaceborne LiDAR data archive and provide new control point data for large-scale topographic mapping and geodetic surveying.However,the accuracy of the ATL 08 terrain estimates has not been fully evaluated on a large scale and in complex terrain conditions.This article aims to quantitatively assess the accuracy of ICESat-2 ATL 08 terrain estimates.Firstly,the ICESat-2 ATL 08 terrain estimates were compared with the high-precision airborne LiDAR digital terrain model(DTM),and impacts of acquisition time,vegetation cover type,terrain slope,and season change on the terrain estimation accuracy were analyzed.We get the following conclusions from the analysis:1)the mean and RMSE of the terrain estimates of day acquisitions are 0.22 m and 0.59 m higher than that of night acquisitions;2)the accuracy of the ATL 08 terrain estimates acquired in vegetated areas is lower than those in non-vegetated areas;3)the accuracy of the ATL 08 terrain estimates is inversely proportional to the slope,and the elevation error increases significantly when the terrain slope is larger than 30°;4)in the non-vegetation covered area,the accuracy of the ATL 08 terrain estimates of summer and winter acquisitions has no obvious discrepancy,but in vegetated area,the accuracy of winter acquisitions is significantly better than that of summer acquisitions.This research provides references for the selection and application of ICESat-2 data.

Accuracy assessment of global ocean tide models in the South China Sea using satellite altimeter and tide gauge data

In this study,to meet the need for accurate tidal prediction,the accuracy of global ocean tide models was assessed in the South China Sea(0°–26°N,99°–121°E).Seven tide models,namely,DTU10,EOT11 a,FES2014,GOT4.8,HAMTIDE12,OSU12 and TPXO8,were considered.The accuracy of eight major tidal constituents(i.e.,Q1,O1,P1,K1,N2,M2,S2 and K2)were assessed for the shallow water and coastal areas based on the tidal constants derived from multi-mission satellite altimetry(TOPEX and Jason series)and tide gauge observations.The root mean square values of each constituent between satellite-derived tidal constants and tide models were found in the range of 0.72–1.90 cm in the deep ocean(depth>200 m)and 1.18–5.63 cm in shallow water area(depth<200 m).Large inter-model discrepancies were noted in the Strait of Malacca and the Taiwan Strait,which could be attributable to the complicated hydrodynamic systems and the paucity of high-quality satellite altimetry data.In coastal regions,an accuracy performance was investigated using tidal results from 37 tide gauge stations.The root sum square values were in the range of 9.35–19.11 cm,with the FES2014 model exhibiting slightly superior performance.

Using of Synthetic Images on Accuracy Assessment of Change Detection

Land cover change detection is the major goal in multitemporal remote sensing studies. It is well known that remotely-sensed images of the same area acquired on different dates tend to be affected by radiometric differences and registration problems. These influences are considered as noise in the process and may induce the user to both： signalling false changes and masking real surface changes. The difference image produced by subtracting two co-registered images is a standard initial step in change detection algorithms. This image naturally appears to be noisier than the original ones and has at least two populations： （1） the noise-like and （2） the real changes. The problem that arises is how to discriminate them. There are several approaches to perform change detection reported in the literature and some studies have employed synthetic images. By using synthetic images, the accuracy assessment of specific algorithm can be done more accurately. The question at this point is： what is the acceptable noise level to be added on the synthetic images to simulate a real problem？ This paper attempts to answer this question by suggesting values of SNR （signal-to-noise ratio） obtained from experiments performed on TM-Landsat-5 and CCD-CBERS-2B images.

Accuracy assessment of global vertical displacement loading tide models for the equatorial and Indian Ocean

The three-dimensional displacements caused by ocean loading effects are significant enough to impact spatial geodetic measurements on sub-daily or longer timescales,particularly in the vertical direction.Currently,most tide models incorporate the distribution of vertical displacement loading tides;however,their accuracy has not been assessed for the equatorial and Indian Ocean regions.Global Positioning System(GPS)observations provide high-precision data on sea-level changes,enabling the assessment of the accuracy and reliability of vertical displacement tide models.However,because the tidal period of the K_(2) constituent is almost identical to the orbital period of GPS constellations,the estimation of the K_(2) tidal constituent from GPS observations is not satisfactory.In this study,the principle of smoothness is employed to correct the systematic error in K_(2) estimates in GPS observations through quadratic fitting.Using the adjusted harmonic constants from 31 GPS stations for the equatorial and Indian Ocean,the accuracy of eight major constituents from five global vertical displacement tide models(FES2014,EOT11a,GOT4.10c,GOT4.8,and NAO.99b)is evaluated for the equatorial and Indian Ocean.The results indicate that the EOT11a and FES2014 models exhibit higher accuracy in the vertical displacement tide models for the equatorial and Indian Ocean,with root sum squares errors of 2.29 mm and 2.34 mm,res-pectively.Furthermore,a brief analysis of the vertical displacement tide distribution characteristics of the eight major constituents for the equatorial and Indian Ocean was conducted using the EOT11a model.

Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome,Italy

Google Earth(GE)has recently become the focus of increasing interest and popularity among available online virtual globes used in scientific research projects,due to the free and easily accessed satellite imagery provided with global coverage.Nevertheless,the uses of this service raises several research questions on the quality and uncertainty of spatial data(e.g.positional accuracy,precision,consistency),with implications for potential uses like data collection and validation.This paper aims to analyze the horizontal accuracy of very high resolution(VHR)GE images in the city of Rome(Italy)for the years 2007,2011,and 2013.The evaluation was conducted by using both Global Positioning System ground truth data and cadastral photogrammetric vertex as independent check points.The validation process includes the comparison of histograms,graph plots,tests of normality,azimuthal direction errors,and the calculation of standard statistical parameters.The results show that GE VHR imageries of Rome have an overall positional accuracy close to 1 m,sufficient for deriving ground truth samples,measurements,and large-scale planimetric maps.

Coseismic surface deformation of the 2014 Napa earthquake mapped by Sentinel-1A SAR and accuracy assessment with COSMO-SkyMed and GPS data as cross validation

The new land observation satellite Sentinel-1A was launched on 25 April 2014 with a C-band synthetic aperture radar(SAR)sensor,which has the significant enhancements in terms of revisit period and high resolution.The Mw 6.1 Napa,California earthquake occurring on 24 August 2014,almost 4 months after the launch,is the first moderate earthquake imaged by the Sentinel-1A.This provides an opportunity to map the coseismic deformation of the event and evaluate the potential of Sentinel-1A SAR for earthquake study.Two techniques including the interferometric SAR(InSAR)and pixel offset-tracking(PO)are,respectively,employed to map the surface deformation along the radar line of sight(LOS),azimuth and slant-range directions.The cross comparison between Sentinel-1A InSAR LOS deformation and GPS observations indicates good agreement with an accuracy of∼2.6 mm.We further estimate the earthquake source model with the external COSMO-SkyMed InSAR and GPS data as constraints,and forward calculate the surface deformation as cross validation with the Sentinel-1A observations.The comparison between the observed and modeled deformation shows that the Sentinel-1A measurement accuracy can achieve 1.6 cm for InSAR technique along LOS direction,and 6.3 and 6.7 cm for PO along azimuth and range directions,respectively.

Land use and cover change and influencing factor analysis in the Shiyang River Basin,China

Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.

Performance validation of High Mountain Asia 8-meter Digital Elevation Model using ICESat-2 geolocated photons

High Mountain Asia(HMA),recognized as a third pole,needs regular and intense studies as it is susceptible to climate change.An accurate and high-resolution Digital Elevation Model(DEM)for this region enables us to analyze it in a 3D environment and understand its intricate role as the Water Tower of Asia.The science teams of NASA realized an 8-m DEM using satellite stereo imagery for HMA,termed HMA 8-m DEM.In this research,we assessed the vertical accuracy of HMA 8-m DEM using reference elevations from ICESat-2 geolocated photons at three test sites of varied topography and land covers.Inferences were made from statistical quantifiers and elevation profiles.For the world’s highest mountain,Mount Everest,and its surroundings,Root Mean Squared Error(RMSE)and Mean Absolute Error(MAE)resulted in 1.94 m and 1.66 m,respectively;however,a uniform positive bias observed in the elevation profiles indicates the seasonal snow cover change will dent the accurate estimation of the elevation in this sort of test sites.The second test site containing gentle slopes with forest patches has exhibited the Digital Surface Model(DSM)features with RMSE and MAE of 0.58 m and 0.52 m,respectively.The third test site,situated in the Zanda County of the Qinghai-Tibet,is a relatively flat terrain bed,mostly bare earth with sudden river cuts,and has minimal errors with RMSE and MAE of 0.32 m and 0.29 m,respectively,and with a negligible bias.Additionally,in one more test site,the feasibility of detecting the glacial lakes was tested,which resulted in exhibiting a flat surface over the surface of the lakes,indicating the potential of HMA 8-m DEM for deriving the hydrological parameters.The results accrued in this investigation confirm that the HMA 8-m DEM has the best vertical accuracy and should be of high use for analyzing natural hazards and monitoring glacier surfaces.

Assessment of degraded mattoral land using remote sensing im-agery in Guadalteba Area,Spain

Natural land cover information is important for analysing and understanding of the current terrestrial situation, especially in the study area that is facing the environmental deteriorating increasingly. The study combined the remote sensing Aster data and ground truth to improve 2001 land cover map of Guadalteba area in Spain, and increased the accuracy from 47% to 70%. The general land cover map produced about the Guadalteba study area outlines the distribution of the vegetation type and the current natural land cover in the area. Based on this improved general land cover map, the natural cover map gave an indication of the present location of nature and agriculture areas. The shrub land degradation map identified location of various shrub/matorral areas and different levels of degradation. The further analysis and discussion were done. The output maps indicated that much of the natural cover mostly dominated by formations of shrubs has been changed to agriculture and other land uses. It is observed that shrubland covers a small percentage, approximately 9% of the study area, due to land degradation in most parts caused by human interfere. Keywords Accuracy assessment - Aster - Land cover map - Matorral degradation map - Remote Sensing CLC number S757.3 Document code A Foundation item: This paper was partly sponsored by NFP (Netherlands Feliowship Program) and National Strategic Project “Environmentally Sound Forest Management Techniques and Models in Natural Forest in Northeast China” (2001BA510B0702) respectively.Biography: XING Yan-qiu (1970-), female, Lecturer, in College of Engi neering and technology Northeast Forestry University. Harbin 150040. P. R. ChinaResponsible editor: Song Funan

Comparison of Accuracy of Two Global DEMs, and the Extracted DEM from the Topographic Map of the Tafilah Governorate

SRTM DEM （shuttle radar topographic mission digital elevation model） and ASTEMR DEM （advanced space-borne thermal emission and reflection radiometer digital elevation model） are now freely available in two resolutions： 90 m and 30 m. DEM is a computerized representation of the Earth＇s relief, and is used in many applications such as hydrology, climatology, geomorphology and ecology. There are some important differences in assessing the accuracy of digital elevation models for specific applications. The purpose of this study is to investigate the errors in digital elevation models obtained globally from DEMs 90 m, 30 m ground resolution, and the extracted DEM from a topographic map by digitized contour lines of 1：50,000 scale in a rugged mountainous region and a heterogeneous landscape, using KS （kernel smoothing） interpolation method. For validation purposes, datasets of 250-GCPs （ground control points） distributed over the study area were used to evaluate and compare the accuracy of the two global Dems and the DEM extracted from TM. The main results showed that the 30 m resolution global （DEM） is the most accurate one among the three. According to the validation results, SRTM with maximum （＋） and minimum （-） error elevation is -10_9. M, with mean deviation of KS is -0.00238667, and RMS （root-mean-square） error of 0.98, and currently the most accurate model is 30 m DEM of the study area with no significant differences compared with DEM 90 m.

Investigations of 3D Surface Roughness Characteristic＇s Accuracy

The existing surface roughness standards comprise only two dimensions. However, the real roughness of the surface is 3D （three-dimensional）. Roughness parameters of the 3D surface are also important in analyzing the mechanics of contact surfaces. Problems of mechanics of contact surfaces are related to accuracy of 3D surface roughness characteristic. One of the most important factors for 3D characteristics determination is the number of data points per x and y axes. With number of data points we understand its number in cut-off length. Number of data points have substantial influence on the accuracy of measurement results, measuring time and size of output data file （especially along the y-axis direction, where number of data points are number of parallel profiles）. Number of data points must be optimal. Small number of data points lead to incorrect results and increase distribution amplitude, but too large number of data points do not enlarge range of fundamental information, but substantially increase measuring time. Therefore, we must find optimal number of data points per each surface processing method.

THE METHODS OF EXTRACTING WATER INFORMATION FROM SPOT IMAGE

Some techniques and methods for deriving water information from SPOT-4(XI) image were investigated and discussed in this paper. An algorithm of decision tree (DT) classification which includes several classifiers based on the spectral responding characteristics of water bodies and other objects, was developed and put forward to delineate water bodies. Another algorithm of decision tree classification based on both spectral characteristics and auxiliary information of DEM and slope (DTDS) was also designed for water bodies extraction. In addition, supervised classification method of maximum likelyhood classification (MLC), and unsupervised method of interactive self organizing dada analysis technique (ISODATA) were used to extract waterbodies for comparison purpose. An index was designed and used to assess the accuracy of different methods adopted in the research. Results have shown that water extraction accuracy was variable with respect to the various techniques applied. It was low using ISODATA, very high using DT algorithm and much higher using both DTDS and MLC.

Facets of Uncertainty in Digital Elevation and Slope Modeling

This paper investigates the differences that result from applying different approaches to uncertainty modeling and reports an experimental examining error estimation and propagation in elevation and slope, with the latter derived from the former. It is confirmed that significant differences exist between uncertainty descriptors, and propagation of uncertainty to end products is immensely affected by the specification of source uncertainty.

Two-sided Long Baseline Radargrammetry from Ascending Descending Orbits with Application to Mapping Post-seismic Topography in the West Sichuan Foreland Basin

One-sided ascending or descending Synthetic Aperture Radar（SAR） stereoradargrammetry has limited accuracy of topographic mapping due to the short spatial baseline（-100 km） and small intersection angle. In order to improve the performance and reliability of generating digital elevation model（DEM） from spaceborne SAR radargrammetry, an exploration of two-sided stereoradargrammetry from the combination of ascending and descending orbits with geometric configuration of long spatial baseline（-1000 km） was conducted in this study. The slant-range geometry between SAR sensors to the earth surface and the Doppler positioning equations were employed to establish the stereoscopic intersection model. The measurement uncertainty of two-sided radargrammetric elevation was estimated on the basis of radar parallax of homogeneous points between input SAR images. Two stereo-pairs of ALOS/PALSAR（Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar） acquisitions with the orbital separation almost 1080 km over the west Sichuan foreland basin with rolling topography in southwestern China were employed in the study to obtain the up-to-date terrain data after the 2008 Wenchuan earthquake that hit this area. Thequantitative accuracy assessment of two-sided radargrammetric DEM was performed with reference to field GPS observations. The experimental results show that the elevation accuracy reaches 5.5 m without ground control points（GCPs） used, and the accuracy is further improved to 1.5 m with only one GPS GCP used as the least constraint. The theoretical analysis and testing results demonstrate that the twosided long baseline SAR radargrammetry from the ascending and descending orbits can be a very promising technical alternative for large-area and high accuracy topographic mapping.

Change Detection of Land Use and Land Cover over a Period of 20 Years in Papua New Guinea

People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps changing rapidly due to burgeoning population. A digital change detection analysis is performed with the help of Geographic Information System (GIS) on the Remote Sensing data spanning over last 20 years, complemented by in-situ data and ground truth information. This current research briefly endeavours to find out the nature of change happening in the major three coastal cities of Papua New Guinea (PNG), namely Alotau, capital of Milnebay province;Lae, capital of Morobe province and Port Moresby, capital of Papua New Guinea. Changes in land use and land cover that took place over 20 years have been recorded using Landsat 5 thematic mapper (TM) data of 1992 and Landsat 8 operational land imager (OLI) data. Land use and land cover maps of 1992, and 2013/14, and change detection matrix of 1992-2013/14 are derived. Results show an immensely sprawling urban landscape, evincing about five times growth during 1992 to 2014. At the same time “natural forests” dwindled by 444.96 hectares in Alotau, 6977.25 hectares in Lae and “mangrove” and “grass/shrub land” decreased by 127.78 and 4859.39 hectares respectively around Port Moresby. The above changes owe to ever increasing population pressure, land tenure shift, agriculture and industrial development.

Monitoring Land Cover Change Using Remote Sensing (RS) and Geographical Information System (GIS): A Case of Golden Pride and Geita Gold Mines, Tanzania

This study monitored land cover change in the mining sites of Golden Pride Gold Mine (GPGM) and Geita Gold Mine (GGM), Tanzania. The satellite data for land cover classification for the years 1997, 2010 and 2017 were obtained from the United States Geologic Survey Departments (USGS) online database and were analyzed using Arc GIS 10 software. Supervised classification composed of seven classes namely forest, bushland, agriculture, water, bare soil, urban area and grassland, was designed for this study, in order to classify Landsat images into thematic maps. In addition, future land cover changes for the year 2027 were simulated using a Cellular Automata (CA)-Markov model after validating the model using the Land Cover for the year 2017. The results from the LULC analysis showed that forest was the most dominant land cover type in 1997 at GPGM and GGM covering 510 ha (52.1%) and 9833 ha (49.7%) respectively. In 2017, the forest area decreased and the bushland replaced forest to be the most dominant land cover type covering 219 ha (22.4%) for GPGM and 8878 ha (44.9%) for GGM. Based on the CA-Markov model, a predicted land cover map for 2027 was dominated by forest covering 340 ha (34.7%) and 8639 ha (43.7%) for GPGM and GGM respectively. An overall accuracy and kappa coefficient for GPGM were 74.7% and 70.2% respectively and for GGM were 71.4% and 66.1% respectively. Thus, land cover changes resulting from mining activities involve reduction of forest land hence endangers biodiversity. GIS and remote sensing technologies are potential to detect the trend of changes and predict future land cover. The findings are crucial as it provides basis for land use planning and intensifies monitoring programs in the mining areas of Tanzania.

The urgent need to develop a new grassland map in China:based on the consistency and accuracy of ten land cover products

Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years.Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets(GLC2000, GlobCover, CCI-LC,MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×10^(4)to 332.46×10^(4)km^(2), with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of eastcentral Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products,with an overall accuracy(OA) reaching 77.51% and a kappa coefficient of 0.51;CLCD is slightly less accurate than MCD12Q1(OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China’s grasslands and call for researchers and the government to actively produce a new generation of grassland maps.

Reliability and consistency assessment of land cover products atmacro and local scales in typical cities

Urban areas have higher heterogeneity compared to natural areas,it is crucial to assessfine-resolution land cover products and discover how they differ in urban areas so that they can be efficiently used for various application scenarios.In this study,five typical cities in China were chosen as study areas to evaluate four commonly used 30 m land cover products:GLC_FCS30-2020,FROM-GLC30-2017,Globeland30-2020,and CLCD-2019.We analyzed the reliability of these four products using validation samples as well as by examining their area and spatial pattern consistency.Given the limitations of traditional accuracy assessments at the macro level,we added a local area evaluation to further examine the classification details in these products.The macro results indicated that four land cover products within urban areas have a similar overall accuracy,surpassing 76%,but there was a low consistency among them,ranging from 42.21%to 61.13%.The local accuracy assessment illustrated that GLC_FCS30-2020 and FROM-GLC30-2017 performed well in reflecting the intricate details of the city,however,the four products exhibited varying degrees of misclassifications and omissions.These phenomena suggest that more sophisticated algorithms are needed to consider urban particularities sincefine-resolution land cover products may fail to capture complex urban details.

SAR Image Coregistration Using Fringe Definition Detection

In order to overcome the limitation of cross correlation coregistration method for Synthetic Aperture Radar （SAR） interferometric pairs with low coherence, a new image coregistration algorithm based on Fringe Definition Detection （FDD） is presented in this paper. The Fourier transformation was utilized to obtain spectrum characteristics of interferometric fringes. The ratio between spectrum mean and peak was proposed as the evaluation index for identifying homologous pixels from interferometric images. The satellites ERS-1/2 C-band SAR acquisitions covering the Yangtze River plain delta, eastern China and ALOS/PALSAR L-band images over the Longmen Shan mountainous area, southwestern China were respectively employed in the experiment to validate the proposed coregistration method. The testing results suggested that the derived Digital Elevation Model （DEM） from FDD method had good agreement with that from the cross correlation method as well as the reference DEM at high coherence area. However, The FDD method achieved a totally improved topographic mapping accuracy by 24 percent in comparison to the cross correlation method. The FDD method also showed better robustness and achieved relatively higher performance for SAR image eoregistration in mountainous areas with low coherence.

Classification of the mining land cover based on density segmentation

The Datun mining area is the test area in this paper,and the purpose is to obtain the mining land cover classification map in 2003.The data source used in this paper is the Landsat enhanced thematic mapper plus(ETM)remote sensing data.By obtaining the normalized difference vegetation index(NDVI)of the study area,the land cover classification information is extracted using the density segmentation method.Because the results cannot distinguish the construction land and wetland,this paper obtains the humidity information of multi-spectral data through the tasseled cap transformation.From the density segmentation image of humidity information,the construction and wetland types can be clearly distinguished.Finally,combining the two classification maps,the visual interpretation using Landsat ETM fusion image with 15-m resolution helps to get the final classification results.After classification accuracy assessment,the overall accuracy calculated from the classification confusion matrix is 86%.This result can be applied in actual project.