Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

Fusion of Radar and Optical Data for Land Cover Mapping

Abstract

This study examined the complementarity of radar and optical data for feature identification. Spaceborne radar and Landsat Thematic Mapper (TM ) multispectral data sets were assessed independently and in combination to classify a site near Wad Medani, Sudan. Radar processing procedures included speckle reduction, texture extraction and post‐processing smoothing. Relative accuracy of the resultant classifications was established by comparison to ground truth information derived from field visitation. Neither speckle filtering nor post‐classification smoothing were improvements over the poor results obtained with the unfiltered, original radar data. Texture measures were significant improvements over the original data (20 percent overall accuracy increase) and several, but not all, individual classes had excellent results. Landsat TM had good overall results (80 percent correct) but considerable spectral confusion between urban and bare soil. Combination of radar with Landsat TM greatly improved results, achieving near perfect classification of all individual classes. The systematic strategy of this study, determination of the best individual method before introducing the next procedure, was effective in managing a complex set of analysis possibilities.     

Relative performance of landsat MSS,TM and IRS LISS-I data for acreage estimation of Rabi sorghum

Acreage estimation of Rabi sorghum crop in Ahmadnagar, Pune and Solapur districts of central Maharashtra has been attempted using synchronously acquired Landsat MSS and TM data of 1987–88 season and IRS LISS-I data of 1988–89 season; in conjuction with near-synchronous ground truth data. The remote-sensing-based acreage estimations for the districts were compared with the respective estimates by Bureau of Economics and Statistics (BES). As the acreages were underestimated with the classification of standard four-band MSS data, the atmospheric correction of fourband MSS data and normalised differencing (ND) of the atmospheric-corrected MSS data were attempted. The main observations are: (1) the use of Landsat MSS data results in underestimation of sorghum acreage in comparison with BES estimation, (2) the atmospheric correction and ND transformation of MSS data are necessary for bringing acreage estimates in agreement with BES estimates, (3) Mid-IR data in band 1.55 to 1.75 μm are useful in improving the separability of land-use classes, and (4) remote sensing data with radiometric sensitivity comparable to LISS-I or Landsat TM and Signal-to-Noise ratios comparable to LISS-I data are suitable for accurate acreage estimation of sorghum.     

Tea acreage estimation and condition assessment using satellite data in nilgiri district,tamilnadu

In the present study an attempt has been made to estimate acreage and condition of tea plantations by using satellite based digital remotely sensed data in visible, near infra-red and middle infra-red spectral regions, in the Nilgiri district of Tamilnadu state. Landsat MSS and TM data, acquired on Dec. 26, 1990 were used in the analysis, Different spectral band combinations, Landsat MSS (1234), TM (1234), TM (2345) and TM (123457) were used for identification of tea plantations. District-boundary-overlaying approach with complete enumeration of digital data was used for estimation of tea acreages. Condition assessment of tea plantations is based on the Greenness Index. Use of Landsat MSS data resulted in an underestimation of area under tea whereas the acreages estimated by using TM spectral band combinations 1234 and 2345 compared closely with the estimates of Department of Horticulture (DOH). The distribution pattern of various condition classes of tea plantations compared well with the prevailing ground conditions as observed during post-classification field survey in September 1992 in the district.     

The Utility of Very High Spatial Resolution Images to Identify Urban Objects

Abstract

Landsat MSS, TM and SPOT XS imageries were used in conjunction with unsupervised, supervised and hybrid classilication techniques to classify land cover types in semi‐arid savannas of Mathison Pastoral Station in the Katherine region of northern Australia. Accuracy assessment was based on field data from 246 ground survey sites over a 745‐km² study area. Of 14 land cover classes identified by traditional mapping means, all combinations of imageries and classification techniques differentiated at least seven land cover types. The overall accuracy for these classifications ranged between 43% and 67%. SPOT XS image delivered the best accuracy followed by TM and MSS; unsupervised classification performed better than supervised and hybrid methods. User's and producer's accuracy of individual land units ranged from 0% to 100%. Riparian woodlands, woodland on limestone slopes, shrubland on clay plains, woodland on limestone plains and shadows were the best‐mapped classes. The land units that were associated with undulating hills were not mapped accurately. However, incorporation of a digital elevation model (DEM) in a GIS improved the overall accuracy. The user's and producer's accuracy of dominant land cover types were also enhanced. The classification results and the efficacy of the techniques at Mathison were similar to those found for a nearby semi‐arid area (Kidman Springs) about 200 km from Mathison. However, the overall accuracy was lower at Mathison than at Kidman Springs. Spectral classification masks were developed from the SPOT XS and TM imageries at Kidman Springs, and were applied to classify SPOT XS and TM imageries at Mathison. Initial results showed that the classification mask could be successfully extrapolated to map dominant land cover types but only with moderate accuracy (50%).     

Examining the Phenological Cycle of Barley (Hordeum vulgare) Using Satellite and in situ Spectroradiometer Measurements for the Detection of Buried Archaeological Remains

The detection of buried archaeological remains using satellite remote sensing is still an open question in archaeological research. This research investigates how the phenological stages of crops can be used support the detection of buried archaeological remains. Ground remote sensing data using the GER-1500 spectroradiometer were obtained from two sites. One site was the Neolithic settlements in central Greece and the other was in Alampra village in Cyprus. For the latter, an archaeological environment was simulated and ground spectroradiometric measurements were systematically acquired over the different phases of the phenological cycle of barley crops. The acquired in situ reflectance measurements have been converted to "in-band" reflectance values of the Landsat TM/ETM+ using the satellite relative spectral responses filters (RSR). Based on the proposed methodology, 97 Landsat MSS, TM, and ETM+ satellite images were acquired (covering a period from 1983 to 2011), for the Thessalian (Greek) site. It has been found that phenological-cycle observations can provide valuable information for identifying buried archaeological remains. Such observations may be used in cases where the spatial resolution of satellite imagery is not high and therefore cannot help support the detection of archaeological remains using standard interpretation techniques.     

Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats,Zambia

Abstract

The paper describes the use of Principal Component Analysis (PCA) of remote sensing images as a method of change detection for the Kafue Flats, an inland wetland system in southern Zambia. The wetland is under human and natural pressures but is also an important wildlife habitat. A combination of Landsat MSS and TM images were used. The images used were from 24 September 1984 (MSS), 3 September 1988 (MSS), 12 September 1991 (TM) and 20 September 1994 (TM). They were geometrically co‐registered and, in the process, the 80m resolution MSS images were resampled to 30m using nearest neighbour resampling. Preliminary PCA revealed that for the MSS images most of the data variance was in near infrared reflectance while for the TM images it was in mid and thermal infrared bands. Holding sensor type constant, separate inter‐band correlation analysis for each image could indicate whether the wetland was drier or wetter on one date versus another. The 1994 image was made the reference image and equivalent green, red and near infrared bands from the other images were radiometrically normalised with those on the reference image. All the bands, three from each date, were then merged into a twelve‐band image on which PCA for change detection was undertaken. A colour composite of eigen images from the resulting principal components was used in change detection. Hydrological data, indicating long‐term reduced inflow of water into the wetland due to human regulation, help explain some of the wetland change detected. Compared to a classification comparison approach to change detection for this area, PCA was found to be very useful in indicating where change had occurred, though interpretation of the changes was difficult without reference to the input images. The methodology appears to have potential use in habitat monitoring for this wetland area.     

Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover

Abstract

A methodology is presented for estimating percent coverage of impervious surface (IS) and forest cover (FC) within Landsat thematic mapper (TM) pixels of urban areas. High-resolution multi-spectral images from Quickbird (QB) play a key role in the sub-pixel mapping process by providing information on the spatial distributions of ISs and FCs at 2.4 m ground sampling intervals. Thematic classifications, also derived from the Landsat imagery, have then been employed to define relationships between 30 m Landsat-derived greenness values and percent IS and FC. By also utilizing land cover/land use classification derived from Landsat and defining unique relationships for urban sub-classes (i.e. residential, commercial/industrial, open land), confusion between impervious and fallow agricultural lands has been overcome. Test results are presented for Ottawa-Gatineau, an urban area that encompasses many aspects typical of the North American urban landscape. Multiple QB scenes have been acquired for this urban centre, thereby allowing us to undertake an in-depth study of the error budgets associated with the fractional inference process.     

Hydrologic investigations in the north Chilean Altiplano using landsat ‐ MSS and ‐ TM data

In order to understand Late Glacial high lake levels in the dry Andes of Northern Chile, recent short ‐ to medium‐term fluctuations in the water budget of present lakes and brines (salars) and their relationship with the atmospheric circulation were investigated. A time sequence of four Landsat‐MSS images between November 1983 and August 1984 was analysed in terms of changing water surface and water volume of several lakes and salars. The variations of the open water bodies were interpreted as a result of the spatial pattern of summer and winter precipitation. Furthermore a method to determine water depth and water salinity of the very shallow salars and lakes by correlating field measurements and digital Landsat‐TM data is described. The resulting model to compute water depth was also applied to the MSS‐sequence, showing good results.     

Relative utility of Landsat-TM and MSS data small scale soil mapping

The Landsat MSS and TM data in the form of false colour composite (FCC) prints at 1∶250,000 scale over parts of Mirzapur (U.P.) and Rohtas district of (Bihar) were interpreted monoscopically in concert with the collateral data and limited field check for soilscape boundary delineation. The study has revealed that at the mapping scale, except for improved image contrast and capturing features of relatively smaller dimensions, no additional advantage has been noticed with TM data over MSS data with respect to exhibition of soilscape boundaries. However, the capability of TM data to withstand enlargement upto 1∶50,000 which is not feasible with MSS data is an additional feature from soil mapping viewpoint.     

Age discrimination among eruptives of Menengai Caldera,Kenya, using vegetation parameters from satellite imagery

This paper reports an investigation to determine the degree to which digitally processed Landsat TM imagery can be used to discriminate among vegetated lava flows of different ages in the Menengai Caldera, Kenya. Since Landsat data display vegetation parameters well, and plant communities vary with type and depth of soil development, selective digital processing techniques were applied to take advantage of these characteristics for discriminating relative age differences of the underlying volcanics. A selective series of five images, consisting of a color‐coded Landsat 5 classification and four color composites, were compared with geologic maps. These included a color coded, modified, unsupervised classification and contrast enhanced, color composite images using TM bands 3–2–1, 4–3–2 and 7–5–3, and the first 3 Karhunen‐Loeve transformation axes that had been generated using 7 Landsat TM bands.

The most recent of more than 70 post‐caldera flows within the caldera are trachytes, which are variably covered by shrubs and subsidiary grasses. Soil development evolves as a function of time, and as such, supports a changing plant community. Progressively older flows exhibit the increasing dominance of grasses over bushes. It was found that the Landsat images correlated well with geologic maps, but that the two mapped age classes could be further subdivided on the basis of different vegetation communities. It is concluded that field maps can be modified, and in some cases corrected by use of such imagery, and that digitally enhanced Landsat imagery can be a useful aid to field mapping in similar terrains.     

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products,using Landsat data and C-CAP products

This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.     

Saline degradation of Indian agricultural lands : A case study in Khambhat Taluka,Gujarat state (India), using satellite remote sensing

In certain agricultural fields of Khambhat Taluka in Gujarat State, the salinity has increased considerably rendering the land completely infertile. The occurrence of salinity in this area can be attributed partly to subsurface sea‐water ingress and partly to improper land and water management practices prior to implementation of irrigation. Landsat MSS or TM and IRS IA LISS II data was used to test the feasibility of delineating saline soils by both visual image interpretation and digital analysis. The study of saline soils using multi‐temporal Landsat images of the year 1977, 1983, and 1987, indicated an evident increase in saline areas in past few years. The Soil Brightness Index (SBI) generated from the IRS‐IA data by the application of MSS equivalent coefficients brought out different categories of soil degradation. The supervised classification scheme aided in generating various salinity levels. The analysis of the soil samples of the above area exhibited increasing values of Electrical Conductivity (ECe), and the soluble cations with increasing levels of salinity.     

Comparison of MODIS fPAR Products with Landsat-5 TM-Derived fPAR over Semiarid Rangelands of Idaho

While validation of the MODIS fPAR product is well behind that of the LAI product, it is recently receiving more attention. In this study, MODIS fPAR and Landsat-5 TM-derived fPAR (TM fPAR) were calculated and quantitatively compared using imagery from 2005 to 2008 for the semiarid rangelands of Idaho, USA. fPAR change maps were calculated between active growth and late-summer senescence periods. Accuracy of the MODIS fPAR and TM fPAR were determined indirectly by incorporating field-based measurements of above-ground forage biomass and percent ground cover from a variety of sites (n = 442).     

Multi‐Temporal Study and Detailed Photogeological Mapping of the Armero Debris Flow (Colombia), Using Landsat TM 5 Images

Abstract

On November the 13^th of 1985, the City of Armero (Colombia) was destroyed by debris flows generated by a reactivation of the Nevado del Ruiz Volcano. The flows ocurred in at least three principal pulses, as was observed by the disater's survivors. Landsat TM 5 data processing was carried out in subscenes taken before and after the lahar sedimentation.

False color composites were generated and combined with the geological information available in order to visualize the magnitude of the catastrophe and the flow characteristics. Taking advantage of Landsat TM 5 images with high spectral resolution, a detailed photogeological mapping of the three principal pulses of the debris flows was carried out. Landsat TM 5 proved to be a powerful complementary source of information for hazard assesment of these catastrophic debris flows. The images were used in addition to ground‐based information, and were an easy way to help ordinary people and decision makers understand such hazardous volcanic situations.     

Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data

In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research.     

Assessment of soil degradation Hazards in Jalor and Ahor Tehsil of Jalor district (Western Rajasthan) by remote sensing

In the present study efforts have been made to identify and map areas affected by various land degradation processes with the aid of Landsat TM imagery data of 1988 and ground truth verification. The kind, extent and degree of land degradation have been mapped. In an area of over 4,124 sq. km. 51% was affected by water erosion and 30% area by wind erosion. Nearly 1.14% area is affected by salinity. Degradation due to combined effect of water and wind erosion and water erosion and salinization has affected 8.20% of the study area. 1.53% area is free from any hazard. Remaining 7.85% area comes under hills and rivers. Nearly 44 percent of the affected area is subjected to moderate and severe degradation which can easily be combatted by techniques referred.     

Regression‐Based Estimation of Lodgepole Pine Forest Age from Landsat Thematic Mapper Data

Abstract

A linear regression‐based model for mapping forest age using Landsat Thematic Mapper data is evaluated in the lodgepole pine forests of Yellowstone National Park. Regression models predicting age (R²=0.62) and a logarithmic transformation of age (R² = 0.90) used a combination of visible, near‐infrared, and middle‐infrared TM bands. Forest age maps produced using the regression method match broad‐scale patterns of forest age within the Yellowstone Central Plateau study area. Per‐pixel estimates of forest age, however, may depart substantially from actual forest age, particularly for older stands, and the maps are most appropriate for depicting regional patterns of forest age.     

Vegetation type discrimination on Landsat TM data in heterogeneous forested landscape of Western Ghats—Accuracy evaluation from large scale aerial photo maps

In the present study, forest type classification using Landsat TM False Colour Composite (FCC) bands 2, 3, 4 has been evaluated for mapping highly heterogeneous forest environment of Western Ghats (Kerala). Visual interpretation of Landsat TM FCC has been carried out to identify bioclimatic vegetation types. For accuracy estimation maps prepared from 1∶15,000 scale black-and-white aerial photographs have been used as ground check data. For comparison aerial photomap classes have been aggregated to match with Landsat-TM-derived map. The classification accuracy of ten major bioclimatic and landcover types was estimated using systematic sampling procedure. The overall classification accuracy of the forest types for the study area was 88.33%.     

空间实验室图像的制图能力分析及其地学应用——以西藏日喀则地区和珠峰地区的试验为例

本文论述了空间实验室测量相机(MC)拍摄的彩色红外立体像对在中比例尺地形制图、正射影像地图以及土地资源调查中的应用可能性,给出1:10万和1:20万比例尺的制图例证,表明平面位置和高程精度能满足1:10万比例尺地形图规范要求;1:10万比例尺影像图可以满足专题制图精度要求。 应用数字图像处理技术,将二种来源(MC,MSS)的遥感数据做数字匹配,并进行信息提取、组合、增强和分类等处理,其结果与原始图像比较表明有独特的优越性,与MSS原始数据相比,图像的空间分辨率明显提高,各种类型界线更分明,可分辨类目增多;而与MC原始图像相比,其光谱特性获得改善且能保持原有的空间分辨率。土地利用类型解释精度与MSS和MC原始图像比较分别提高7—20％左右。