Use of cartographic data and Landsat TM images to determine land use change in the vicinity of Mexico City

Evaluation of change in land use is important for planning further development in populated areas. Here we attempt to determine the growth of urban areas in the vicinity of Mexico City, using a 1993 Landsat Thematic Mapper (TM) image and cartographic data contained in maps published by the Instituto Nacional de Estadistica Geografia e Informatica (INEGI 1975, 1983). The area occupied by urban areas in 1975 and 1983 was quantified using raster images generated by scanning the maps. Supervised classification processes were applied to a 1993 Landsat TM image in bands 1, 2, 3, 4, 5 and 7, of the area of Chalco. The image was pre-processed and then processed to enhance the spectral response of the surface materials. The different land cover types that characterise distinct land uses in the study area were identified in the image and an overall classification accuracy of 82% was estimated using aerial photographs from the Chalco area. The resulting evaluation of the land use changes in the Chalco urban area was plotted, and a growth greater than 14% per year was estimated.     

Monitoring the effects of Dracunculiasis remediation on agricultural productivity using satellite data

Dracunculiasis has been shown to be a major cause of agricultural work loss in many parts of West Africa. Monitoring the magnitude of agricultural loss caused by this parasite has been a problem due to the non-market nature of the agricultural output, which is often used for subsistence or barter. To test the hypothesis that ‘ Optimization of water and sanitation interventions in areas with high Dracunculiasis prevalence have a measurable socio-economic impact’ a temporal analysis of Landsat Thematic Mapper (TM) satellite data was conducted. A region in which intervention had not taken place (the control) and a region in which it had were compared to determine the change in agricultural activity. Paired comparisons were made of the difference between two Landsat TM data sets, a 31 January 1986 scene ( pre-intervention) and a 5 January 1991 scene (post-intervention) using the digital number ( DN) values in bands 5, and 7, and the Normalized Difference Vegetation Index (NDVI) calculated from bands 3 and 4. The results of the paired comparison indicated that agricultural activity increased in regions where intervention had taken place. The analysis also indicated that the Harmattan winds may have effected the utility of the NDVI because of its differential effect on atmospheric scattering in the visible and infrared parts of the spectrum.     

Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: Comparison of various indices through correlation analysis

In this paper, we assess the capability of Landsat Thematic Mapper (TM) for oakwood crown closure estimation in Tulare County, California. Measurements made from orthorectified aerial photographs for the same area were used as a reference. The linear relationship between crown closure and digital values of each band of the TM image was examined. TM Band 3 had the highest correlation ( @ = m 0.828; R 2 = 0.687) with crown closure measurements. The simple ratio (SR) and the normalized difference vegetation index (NDVI) were generated for correlation analysis and only NDVI showed better correlation ( A = 0.836; R 2 = 0.699) than use of single bands. An additional index (NIR N - R N )/(NIR N + R N ), called NDVIN, was experimented, NDVISQ ( N = 2) and NDVICUB ( N = 3) showed some improvements over SR and NDVI ( A = 0.855; R 2 = 0.732 for N = 3). Through multiple regression with all six bands, we found that there was a considerable amount of improvement in variability explanation over any individual band or index tested ( R 2 = 0.803). NIR, red and blue bands were able to adequately model crown closure as using all the six TM bands ( R 2 = 0.802). Principal component analysis (PCA) and Kauth-Thomas (K-T) transform were applied to reduce multi-collinearity among bands. The third principal component and greenness in K-T transform showed similar effects to those of NDVI. Transformation of digital numbers (DNs) to radiances kept the results of single band and multiple band estimation the same, and did not improve the index estimation very much. A simple radiometric correction of the TM image improved results for the NDVI ( A = 0.840; R 2 = 0.705) and NDVISQ estimation ( A = 0.861; R 2 = 0.741), but worsened estimation results of single band and multiple bands.     

Remote sensing and field data integration in the definition of hydrothermally altered areas in vegetated terrain,central Brazil

A Landsat Thematic Mapper (TM) natural colour composite allowed the discrimination of areas of hydrothermally altered materials, even where vegetation (mainly herbaceous plants) covered portions of the terrain. Field spectra data showed that broad iron-oxide absorption features in TM 1 and TM 2 bands enabled the spectral discrimination between areas of hydrothermally altered materials and areas of soils derived from biotite-granites. In order to improve the definition of the target areas, the TM images were merged with a digitized aerial photograph through IHS technique. The resulting high resolution hybrid images were segmented using a region growing method, which generated images partitioned into a number of homogeneous regions. The segmented images were classified using an unsupervised clustering region classifier algorithm. The result, compared with field observations, demonstrated that the method eliminated the subjectivity of the visual image interpretation and increased the accuracy in the delineation of the hydrothermally altered areas.     

Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images

Routine acquisition of Landsat 5 Thematic Mapper (TM) data was discontinued recently and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) has an ongoing problem with the scan line corrector (SLC), thereby creating spatial gaps when covering images obtained during the process. Since temporal and spatial discontinuities of Landsat data are now imminent, it is therefore important to investigate other potential satellite data that can be used to replace Landsat data. We thus cross-compared two near-simultaneous images obtained from Landsat 5 TM and the Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS), both captured on 29 May 2007 over Los Angeles, CA. TM and AWiFS reflectances were compared for the green, red, near-infrared (NIR), and shortwave infrared (SWIR) bands, as well as the normalized difference vegetation index (NDVI) based on manually selected polygons in homogeneous areas. All R ² values of linear regressions were found to be higher than 0.99. The temporally invariant cluster (TIC) method was used to calculate the NDVI correlation between the TM and AWiFS images. The NDVI regression line derived from selected polygons passed through several invariant cluster centres of the TIC density maps and demonstrated that both the scene-dependent polygon regression method and TIC method can generate accurate radiometric normalization. A scene-independent normalization method was also used to normalize the AWiFS data. Image agreement assessment demonstrated that the scene-dependent normalization using homogeneous polygons provided slightly higher accuracy values than those obtained by the scene-independent method. Finally, the non-normalized and relatively normalized ‘Landsat-like’ AWiFS 2007 images were integrated into 1984 to 2010 Landsat time-series stacks (LTSS) for disturbance detection using the Vegetation Change Tracker (VCT) model. Both scene-dependent and scene-independent normalized AWiFS data sets could generate disturbance maps similar to what were generated using the LTSS data set, and their kappa coefficients were higher than 0.97. These results indicate that AWiFS can be used instead of Landsat data to detect multitemporal disturbance in the event of Landsat data discontinuity.     

Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX

A sequence of five high-resolution satellite-based land surface temperature (T_s) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.     

1979~2010年乌海市煤矿开采对生态环境影响的遥感监测

以1979年以来多期陆地卫星Landsat MSS/TM遥感数据为基础,利用煤矿遥感影像各波段反射率远低于其他地物的典型光谱特征,提取了乌海市煤矿开采区时空分布数据集,并结合SPOT-VEGETATION NDVI数据,对乌海市煤矿开采区的变化过程以及生态环境影响进行了动态监测。结果表明:30 a来乌海市煤矿开采区扩张明显,煤矿开采区面积由1979年的2.69 km²增加到2010年的109.34 km²,净增加106.65 km²。煤矿开采过程中侵占了大量耕地、林地和草地,导致1998~2001年乌海市整体生态环境急剧恶化,全市年最大NDVI由1998年的0.2043下降到2001年的0.1231,2000年以后,乌海市煤矿开采区面积虽然仍呈增长趋势,但全市植被覆盖状况有所好转,尤其是2005~2010年植被覆盖状况明显好转,全市年最大NDVI由2005年的0.1417上升到2010年的0.2028。     

Location of kimberlites using Landsat Thematic Mapper images and aerial photographs: the Redondao diatreme,Brazil

Abstract

Landsat Thematic Mapper images and aerial photographs were used in the detection of kimberlile-derived materials in the Redondao test site. In this area kimberlite-derived soils show a flora constituted mainly by grasses and shrubs, which differ from the surrounding savanna-park (cerrado) vegetation cover. Band-ratio images were able to distinguish kimberlite-derived materials by enhancing areas with different vegetation covers. However, the coarse spatial resolution of Landsat-TM images compared with the spatial variability of the study area, and the removal of topographic shadowing effects on ratio images blurred several landscape features. To increase discrimination, Landsat Thematic Mapper ratio images were merged with digitized aerial photographs through intensity, hue and saturation (IHS) colour transforms. The resulting merged colour composite highlighted the spatial and spectral features of the study area permitting an accurate definition of the kimberlite-derived materials within the Redondao diatreme.     

Oakwood crown closure estimation by unmixing Landsat TM data

Using an unconstrained least squares solution (LSS) method and an artificial neural network (ANN) algorithm, we estimated oakwood crown closure from a Landsat Thematic Mapper (TM) image of Tulare County, California, USA. Fractions of endmembers (oak crown (f1), grass (f2) and soil (f3)) from mixed pixels were derived from aerial photographs (scale 1?:?40?000) scanned at 1?m ground resolution for training and testing the LSS and ANN algorithms. The aerial photographs were orthorectified using a digital photogrammetric software package with ground control points collected through a differential global positioning system (GPS). The TM image was georeferenced with respect to the corresponding orthorectified aerial photographs. The training and test samples were randomly selected from the TM image and their corresponding fractions of endmembers were derived from the orthophoto. A fourth endmember, shade (f4), was directly extracted from the TM image. Experimental results indicate that the ANN has performed better than the unconstrained LSS. To extract oakwood crown closure in mixed pixels, better results were obtained without using a shade endmember.     

Influence of fire severity on plant regeneration by means of remote sensing imagery

In this paper we analyse the interactions between fire severity (plant damage) and plant regeneration after fire by means of remote sensing imagery and a field fire severity map. A severity map was constructed over a large fire (2692 ha) occurring in July 1994 in the Barcelona province (north-east of Spain). Seven severity classes were assigned to the apparent plant damage as a function of burning intensity. Several Landsat TM and MSS images from dates immediately before and after the fire were employed to monitor plant regeneration processes as well as to evaluate the relationship with fire severity observed in situ . Plant regeneration was monitored using NDVI measurements (average class values standardized with neighbour unburned control plots). Pre-fire NDVI measurements were extracted for every plant cover category (7), field fire severity class (7), and spatial cross-tabulation of both layers (33) and compared to post-fire values. NDVI decline due to fire was positively correlated with field fire severity class. Results show different patterns of recovery for each dominant species, severity class and combination of both factors. For all cases a significant negative correlation was found between damage and regeneration ability. This work leads to a better understanding of the influence of severity, a major fire regime parameter on plant regeneration, and may aid to manage restoration on areas burned under different fire severity levels.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Monitoring change in the extent of salt-affected soils in northern India

A large area of barren salt-affected soils has been reclaimed in recent years in the Ganges Plains or Uttar Pradesh, India, Increased canal irrigation in the area, on the other hand, is also leading to salinization of new areas. A study was conducted using aerial photographs and Landsat Thematic Mapper (TM) data to monitor change in the status of salt-affected soils in the Kanpur district of Uttar Pradesh. Old survey maps prepared using ground methods in 1956, showing large salt-affected soil blocks of more than 80 ha, were used as a basis for comparison. These maps were compared with the maps prepared using aerial photographs of 1972 and Landsat TM images of 1986. Aerial photographs on a 1:40 000 scale and standard Landsat TM false colour composite (FCC) image on 1:50000 scale provided a minimum delineation of 2 ha size, which was considered sufficient for change detection in the present case. The average increase in cultivation due to reclamation within the salt-affected soil blocks during 1956-86 was found to be 22 per cent. This increase was also corroborated by the increase in the rice area during the above period in the district since these soils are used mainly for rice cultivation. During 1972-86, an increase in the extent of salt-affected soils on the periphery of large blocks was also observed, which was limited to 3 per cent.     

NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa

Crop Normalized Difference Vegetation Index (NDVI) time profiles and crop acreage estimates were derived from the application of linear mixture modelling to Advanced Very High Resolution Radiometer (AVHRR) data over a test area in the southern part of the Pampa region, Argentina. Bands 1 and 2 from seven AVHRR scenes (June to January 1991) were combined to produce fraction images of winter crops, summer crops and pastures. A Landsat Thematic Mapper (TM) scene of the region was classified and superimposed to the AVHRR Local Area Coverage (LAC) data by means of a correlation technique. Each class signature was extracted by regressing the AVHRR response on the cover types proportions, estimated from Landsat-TM data, over sets of calibration windows. The crop NDVI profiles were hence derived from the class signatures in bands 1 and 2. These profiles appeared consistent with the cover types, but variability depending on the set of windows was noted. The assessment of the class signatures was indirectly accomplished through the subpixel classifications of the AVHRR data, performed using the different sets of class spectra. Although some discrepancies between AVHRR and Landsat–TM estimates were observed at the individual window level, the classification results compared quite well on a regional scale with Landsat–TM estimates: crop acreage was estimated to an overall accuracy ranging from 89 to 95 per cent according to the spectra used in the classification. Definitely, the proposed methodology should permit a better exploitation of the temporal resolution of AVHRR data in both the areas of yield prediction and vegetation classification. Furthermore, the perational application of such a methodology for crop monitoring will undoubtedlybe facilitated with the coming sensor systems such as the ModerateResolution Imaging Spectroradiometer (MODIS), the SPOT Vegetation Monitoring Instrument or the ‘Satelite Argentino Cientifico’ (SAC–C).     

Scene selection and the use of NASA's global orthorectified Landsat dataset for land cover and land use change monitoring

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA-AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift-corrected NOAA-AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA-AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.     

Scene selection and the use of NASA's global orthorectified Landsat dataset for land cover and land use change monitoring

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA‐AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift‐corrected NOAA‐AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA‐AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.     

Crop type identification by integration of high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data

Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results.     

基于AVHRR和TM数据的时间序列较高分辨率NDVI数据集重构方法

由于技术条件的限制,一个传感器很难同时具有高空间分辨率和高时间分辨率。然而,在高分辨率尺度上监测地表景观季节性变化的能力是全球的迫切需要,融合周期短、覆盖范围广与分辨率高、周期长的遥感数据是一种较好的方法。基于AVHRR时间分辨率高和TM空间分辨率高及其数据积累时间长的特点,选择若尔盖高原为研究区域,在改进ESTARFM方法的基础上,对TM NDVI和AVHRR NDVI进行融合,构建高时空分辨率的NDVI数据集。研究结果表明:该方法能有机结合AVHRR NDVI的时间变化信息与TM NDVI的空间差异信息,有效实现高时空分辨率NDVI数据集的重构,3景预测高分辨率NDVI与MODIS NDVI产品相关系数分别达到了0.89、0.91和0.85。该方法能够在时间上保留高时间分辨率数据的时间变化信息,同时在空间上反映高空间分辨率数据的空间差异信息,从而为有效构建相对高分辨率时间序列NDVI数据集提供了可能的方法。     

NDVI time series for monitoring RUSLE cover management factor in a tropical watershed

Land cover, an important factor for monitoring changes in land use and erosion risk, has been widely monitored and evaluated by vegetation indices. However, a study that associates normalized difference vegetation index (NDVI) time series to climate parameters to determine soil cover has yet to be conducted in the Atlantic Rainforest of Brazil, where anthropogenic activities have been carried out for centuries. The objective of this paper is to evaluate soil cover in a Brazilian Atlantic rainforest watershed using NDVI time series from Thematic Mapper (TM) Landsat 5 imagery from 1986 to 2009, and to introduce a new method for calculating the cover management factor (C-factor) of the Revised Universal Soil Loss Equation (RUSLE) model. Twenty-two TM Landsat 5 images were corrected for atmospheric effects using the 6S model, georeferenced using control points collected in the field and imported to a GIS database. Contour lines and elevation points were extracted from a 1:50,000-scale topographic map and used to construct a digital elevation model that defined watershed boundaries. NDVI and RUSLE C-factor values derived from this model were calculated within watershed limits with 1 km buffers. Rainfall data from a local weather station were used to verify NDVI and C-factor patterns in response to seasonal rainfall variations. Our proposed method produced realistic values for RUSLE C-factor using rescaled NDVIs, which highly correlated with other methods, and were applicable to tropical areas exhibiting high rainfall intensity. C-factor values were used to classify soil cover into different classes, which varied throughout the time-series period, and indicated that values attributed to each land cover cannot be fixed. Depending on seasonal rainfall distribution, low precipitation rates in the rainy season significantly affect the C-factor in the following year. In conclusion, NDVI time series obtained from satellite images, such as from Landsat 5, are useful for estimating the cover management factor and monitoring watershed erosion. These estimates may replace table values developed for specific land covers, thereby avoiding the cumbersome field measurements of these factors. The method proposed is recommended for estimating the RUSLE C-factor in tropical areas with high rainfall intensity.     

Remote sensing of contrasting tillage practices in the Texas Panhandle

Tillage information is crucial in environmental modelling as it has a direct impact on water holding capacity, evapotranspiration, carbon sequestration and water quality. In this study, a set of Landsat Thematic Mapper (TM)‐based linear logistic models were developed for mapping tillage practices and verified with an independent dataset. For data collection purposes, 35 and 41 commercial fields were randomly selected in Moore and Ochiltree counties, respectively, in the Texas Panhandle. Tillage survey was planned and conducted to coincide with Landsat 5 satellite overpasses during the 2005 planting season and two TM scenes were acquired. Using the Moore County dataset, seven logistic regression models were developed and these were evaluated with the data collected from Ochiltree County. The overall classification accuracy of the models varied from 86% to 91% with the Moore County dataset. These models were evaluated against independent Ochiltree County dataset and resulted in somewhat less accurate (classification accuracy of 67–85%) but still useful results. Analysis of these results indicates that logistic regression models that have indices derived from the combination of TM band 5 with bands 4 or 6 may provide consistent and acceptably accurate results when they are applied in the same geographic region.     

Assessment of grassland degradation near Lake Qinghai,West China,using Landsat TM and in situ reflectance spectra data

The severity of grassland degradation near Lake Qinghai, West China was assessed from a Landsat Thematic Mapper (TM) image in conjunction with in situ samples of per cent grass cover and proportion (by weight) of unpalatable grasses (PUG) collected over 1?m² sampling plots. Spectral reflectance at each sampling plot was measured with a spectrometer and its location determined with a Global Positioning System (GPS) receiver. After radiometric calibration, the TM image was geometrically rectified. Ten vegetation indices were derived from TM bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding most closely to those of TM3 and TM4. Regression analyses showed that NDVI and SAVI are the most reliable indicators of grass cover and PUG, respectively. Significant relationships between TM bands-derived indices and in situ sampled grass parameters were established only after the former had been calibrated with in situ reflectance spectra data. Through the established regression models the TM image was converted into maps of grass cover parameters. These maps were merged to form a degradation map at an accuracy of 91.7%. It was concluded that TM imagery, in conjunction with in situ grass samples and reflectance spectra data, enabled the efficient and accurate assessment of grassland degradation inside the study area.