Landsat Thematic Mapper Data Research Articles

Retrieval and application of leaf area index over China using HJ-1 data

ABSTRACTAlthough leaf area index (LAI) is one of the essential parameters employed to monitor global vegetation, no global LAI products which use a fine spatial resolution yet exist. To remedy this, we herein outline an adapted LAI retrieval algorithm which employs HuanJing-1 charge-coupled device (HJ-1 CCD) data, which was originally determined using Landsat thematic mapper (TM) data. Validation of this adapted LAI retrieval algorithm via field measurements demonstrates that errors for ∼72% of the sample sites were within a range of 0.2 for the Bashang Grassland in Hebei Province, and within 1.0 for the Taihe region in Jiangxi Province. In addition, the correlation coefficients (R) of HJ-1 LAI and ground-measured LAI data were similar to those of MODIS LAI and ground-measured LAI datasets for both regions. These results demonstrate the potential for transposing a mature Landsat LAI-retrieval algorithm to HJ-1 data. Furthermore, we generated 30 m HJ-1 LAI datasets for China for the summer of 2012. An analytical comparison between retrieved HJ-1 LAI and MODIS LAI data showed the coherence of the LAI distribution. Finally, a specific post-forest fire area was mapped using HJ-1 LAI datasets.

A modified GAC model for extracting waterline from remotely sensed imagery

ABSTRACTAt present many methods are available for extracting waterline automatically from remotely sensed imagery. They are commonly limited by their inability to accurately detect waterline when the land–sea interface is not so distinctive on the imagery. This study aims to develop a new model of extraction in which both the spectral features of water and the spatial feature of waterline itself are incorporated into the level set framework. This model consists of three terms. The first term, which is derived from the spectral features, drives the curve evolution. The second term, curvature flow, guarantees the smoothness of the curve in the process of evolution. The third term, the boundary attraction force, attracts the evolving curve to the genuine position. Experimental results with Landsat Thematic Mapper (TM) data sets validate the effectiveness of the proposed model. It can improve the accuracy of extracted waterline and effectively prevent the omission of weak boundaries from the detected results that are common in both geodesic active contour (GAC) and distance-regularized level set evolution (DRLSE) results.

Can subsurface soil salinity be predicted from surface spectral information? – From the perspective of structural equation modelling

An efficient and reliable method for detecting soil salinity in deep saline soils using remote sensing is required to better manage soil salinization. In this study, a laboratory evaporation experiment was conducted to determine how subsurface soil salinity can be determined using soil surface spectra. Ten soil columns were observed, and measurements were taken at specific time intervals to monitor the variations in soil salinity and moisture content at depths of 5 cm and 15 cm relative to soil surface spectra during the evaporation process. The structural equation modelling (SEM) method was used to analyse the relationships between the spectral reflectance data and the salt content in the subsurface soil (15 cm). The results showed significant correlations (standard path coefficient = −0.37, t = −6.00) between the spectral reflectance of the soil surface and the subsurface soil salinity and between different types of multispectral data, such as the Landsat Thematic Mapper (TM) data, and the subsurface soil salinity. Overall, the results implied that surface soil spectral information can be used to capture some subsurface soil properties and that remote sensing images could provide an alternative method for monitoring changes in deeper soils.

Evaluation of multisource data for glacier terrain mapping: a neural net approach

Spectrally similar nature of land covers in a glacierized terrain hampers their automated mapping from multispectral satellite data, which may be overcome by using multisource data. In the present study, an artificial neural network (ANN)-based information extraction approach was applied for mapping the Kolahoi glacier and adjoining areas, using Landsat TM (Thematic Mapper) data and several ancillary layers such as image transformations and topographic attributes. Results reveal that ANN (highest overall accuracy (OA): 83.74%) outperforms maximum likelihood classifier (highest OA: 66.90%) and the incorporation of ancillary data into the classification process significantly enhances the mapping accuracy (>9%), particularly the addition of Near Infrared Red/Short Wave Infrared (NIR/SWIR) data to the spectral data. A nine-band combination dataset (spectral data, slope, Red/NIR and decorrelation stretch) was found to be the best multisource dataset. Results of the Z-tests (at 95% confidence level) also corroborate and statistically validate the above findings.

Simulating the effects of land use changes on soil erosion using RUSLE model

The aim of this paper is to evaluate the impacts of land use change on soil loss. Soil loss was quantified using the revised universal soil loss equation model in Darabkola catchment. Land use maps of 1992, 1998 and 2012 were derived from Landsat Thematic Mapper data. The mean annual soil loss was therefore determined for these years. The results showed open-canopy forest area decreased by 36% between 1992 and 1998. Likewise, the decreasing trend of forest lands which are near to residential areas has continued from 1795 ha in 1998 to 1765 ha in 2012. Also the results indicate that the maximum annual soil loss ranged from 5.06, 6.19 and 15.23 ton h−1 y−1 in 1992, 1998 and 2012, respectively. Also, by assuming that all watershed conditions and land uses be constant in the future, then the area of close- and open-canopy forest and dry agricultural lands will be 23.23, 2.88 and 29.89 ha in 2040, respectively.

Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data

Mapping cropland distribution over large areas has attracted great attention in recent years, however, traditional pixel-based classification approaches produce high uncertainty in cropland area statistics. This study proposes a new approach to map fractional cropland distribution in Mato Grosso, Brazil using time series MODIS enhanced vegetation index (EVI) and Landsat Thematic Mapper (TM) data. The major steps include: (1) remove noise and clouds/shadows contamination using the Savizky–Gloay filter and temporal resampling algorithm based on the time series MODIS EVI data; (2) identify the best periods to extract croplands through crop phenology analysis; (3) develop a seasonal dynamic index (SDI) from the time series MODIS EVI data based on three key stages: sowing, growing, and harvest; and (4) develop a regression model to estimate cropland fraction based on the relationship between SDI and Landsat-derived fractional cropland data. The root mean squared error of 0.14 was obtained based on the analysis of randomly selected 500 sample plots. This research shows that the proposed approach is promising for rapidly mapping fractional cropland distribution in Mato Grosso, Brazil.

Remote sensing of the impacts of construction in coastal waters on suspended particulate matter concentration – the case of the Yangtze River delta, China

This study investigates the capability of high-spatial resolution Landsat Thematic Mapper (TM) data to sense and document suspended particulate matter concentration (SPMC) variability resulting from the influence of large structures in coastal waters. Two bridges, located in the coastal waters of the Yangtze River delta, are used as examples. A new SPMC inverse model, relating SPMC to TM optical properties through linear regression in the red and near-infrared bands, is developed. In total, 780 samples and 30 transects taken between 2006 and 2011 were used to compare and contrast SPMC at locations upstream and downstream of the bridges. The comparisons show: (i) within a distance of 0.3 km downstream from the bridges, SPMC mostly increased by 3–60% (8.40–176.29 mg l–1); (ii) when SPMC values upstream were low (<300 mg l–1), the increase in SPMC extended to 3.0–6.5 km downstream; (iii) under conditions of high turbidity (>400 mg l–1) upstream, decreases in SPMC were observed in 0.3–6.5 km downstream. The bridges influenced SPMC by blocking the transport of upstream suspended particulate matter (deposition) and through stirring of the sediments near the base of their piers (resuspension). The results can be generalized to other offshore engineering structures.

The application of ensemble techniques for land-cover classification in arid lands

Robust classification approaches are required for accurate classification of complex land-use/land-cover categories of desert landscapes using remotely sensed data. Machine-learning ensemble classifiers have proved to be powerful for the classification of remotely sensed data. However, they have not been evaluated for classifying land-cover categories in desert regions. In this study, the performance of two machine-learning ensemble classifiers – random forests (RF) and boosted artificial neural networks – is explored in the context of classification of land use/land cover of desert landscapes. The evaluation is based on the accuracy of classification of remotely sensed data, with and without integration of ancillary data. Landsat-5 Thematic Mapper data captured for a desert landscape in the north-western coastal desert of Egypt are used with ancillary variables derived from a digital terrain model to classify 13 different land-use/land-cover categories. Results show that the two ensemble methods produce accurate land-cover classifications, with and without integrating spectral data with ancillary data. In general, the overall accuracy exceeded 85% and the kappa coefficient (κ) attained values over 0.83. The integration of ancillary data improved the performance of the boosted artificial neural networks by approximately 5% and the random forests by 9%. The latter showed overall higher accuracy; however, boosted artificial neural networks showed better generalization ability and lower overfitting tendencies. The results reveal the merit of applying ensemble methods to integrated spectral and ancillary data of similar desert landscapes for achieving high classification accuracies.

CHROMITITE PROSPECTING USING LANDSAT TM AND ASTER REMOTE SENSING DATA

Abstract. Studying the ophiolite complexes using multispectral remote sensing satellite data are interesting because of high diversity of minerals and the source of podiform chromitites. This research developed an approach to discriminate lithological units and detecting host rock of chromitite bodies within ophiolitic complexes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. Three main ophiolite complexes located in south of Iran have been selected for the study. Spectral transform techniques, including minimum noise fraction (MNF) and specialized band ratio were employed to detect different rock units and the identification of high-potential areas of chromite ore deposits within ophiolitic complexes. A specialized band ratio (4/1, 4/5, 4/7) of ASTER, MNF components and Spectral Angle Mapper (SAM) on ASTER and Landsat TM data were used to distinguish ophiolitic rock units. Results show that the specialized band ratio was able to identify different rock units and serpentinized dunite as host rock of chromitites within ophiolitic complexes, appropriately. MNF components of ASTER and Landsat TM data were suitable to distinguish ophiolitic rock complexes at a regional scale. The integration of SAM and Feature Level Fusion (FLF) used in this investigation discriminated the ophiolitic rock units and prepared detailed geological map for the study area. Accordingly, high potential areas (serpentinite dunite) were identified in the study area for chromite exploration targets.The approach used in this research offers the image processing techniques as a robust, reliable, fast and cost-effective method for detecting serpentinized dunite as host rock of chromitite bodies within vast ophiolite complexes using ASTER and Landsat TM satellite data.

Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models

This article focuses on retrieving the multi-scale crown closure (CC) of Moso bamboo forest using Système Pour l’Observation de la Terre (SPOT5) and Landsat Thematic Mapper (TM) satellite remotely sensed imagery based on the geometric-optical model and the artificial neural network (ANN) model. CC at local scale was first retrieved using the Li-Strahler geometric-optical model (LSGM) and images from an unmanned aerial vehicle (UAV). Then, multi-scale CC was retrieved using the Erf-BP model (a kind of back-propagation (BP) feed-forward neural network, which takes a Gaussian error function (Erf) as an activation function of the hidden layer) based on a combination of SPOT5 and Landsat TM images. The results show that by combining multi-source remotely sensed data, the CC of Moso bamboo forest can be retrieved at the local region, township area, and county scale with high accuracy using the Erf-BP model. Estimated values have a linear relationship with the observed values at a significance level of 0.05. The highest accuracy of the retrieval of CC (referred to as LSGM-UAV-CC) was observed at the local region based on LSGM and UAV, with the coefficient of determination (R2) of 0.63, followed by that at the township area with an R2 of 0.0.55 based on LSGM-UAV-CC and SPOT5 data using the Erf-BP model (Erf-BP-SPOT5-CC), and that at the county scale with an R2 of 0.54 based on Erf-BP-SPOT5-CC and Landsat TM data using the Erf-BP model (Erf-BP-TM-CC).

Integration of ASTER and landsat TM remote sensing data for chromite prospecting and lithological mapping in Neyriz ophiolite zone, south Iran

AbstractChromite deposits in Iran are located in the ophiolite complexes, which have mostly podiform types and irregular in their settings. Exploration for podiform chromite deposits associated with ophiolite complexes has been a challenge for the prospectors due to tectonic disturbance and their distribution patterns. Most of Iranian ophiolitic zones are located in mountainous and inaccessible regions. Remote sensing approach could be applicable tool for choromite prospecting in Iranian ophiolitic zones with intensely rugged topography, where systematic sampling and conventional geological mapping are limited. In this study, Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for chromite prospecting and lithological mapping in the Neyriz ophiolitic zone in the south of Iran. Image transformation techniques, namely decorrelation stretch, band ratio and principal component analysis (PCA) were applied to Landsat TM and ASTER data sets for lithological mapping at regional scale. The RGB decorrelated image of Landsat TM spectral bands 7, 5, and 4, and the principal components PC1, PC2 and PC3 image of ASTER SWIR spectral bands efficiently showed the occurrence of major lithological units in the study area at regional scale. The band ratios of 5/3, 5/1, 7/5 applied on ASTER VNIR‐SWIR bands were very useful for discriminating most of rock units in the study area and delineation of the transition zone and mantle harzburgite in the Neyriz ophiolitic complex. Spectral Angle Mapper (SAM) technique was implemented to ASTER VNIR‐SWIR spectral bands for detecting minerals of rock units and especially delineation of the transition zone and mantle harzburgite as potential zones with high chromite mineralization in the Neyriz ophiolitic complex. The integration of information extracted from the image processing algorithms used in this study mapped most of lithological units of the Neyriz ophiolitic complex and identified potential areas of high chromite mineralization (transition zone and mantle harzburgite) for chromite prospecting targets in the future. Furthermore, image processing results were verified by comprehensive fieldwork and laboratory analysis in the study area. Accordingly, result of this investigation indicate that the integration of information extracted from the image processing algorithms using Landsat TM and ASTER data sets could be broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such Iranian ophiolitic zones.

Urban land use/land cover changes in the Tema metropolitan area (1990–2010)

Land use and land cover changes are local and place specific, occurring incrementally in ways that often escape our attention. This study sought to detect changes in land cover in the Tema Metropolis of Ghana from 1990 to 2010. Multispectral Landsat Thematic Mapper data sets of 1990, 2000 and 2007 were acquired, pre-processed and enhanced. Unsupervised classification of the images was performed and six land cover classes (water, wetlands, closed vegetation, open vegetation, cropped lands, and built-up) were derived. The post-classification change detection technique was performed to derive the changes in land cover and their corresponding change matrices. Between 1990 and 2010, built-up areas expanded steadily to become the most prevalent land cover type in the metropolis, reducing vegetation cover dramatically. High population growth with its attendant rise in the demand for housing, and increasing commercial activities, were found to have influenced land cover changes over the period.

Phenological Response of an Arizona Dryland Forest to Short-Term Climatic Extremes

Baseline information about dryland forest phenology is necessary to accurately anticipate future ecosystem shifts. The overarching goal of our study was to investigate the variability of vegetation phenology across a dryland forest landscape in response to climate alterations. We analyzed the influence of site characteristics and climatic conditions on the phenological patterns of an Arizona, USA, ponderosa pine (Pinus ponderosa) forest during a five-year period (2005 to 2009) that encompassed extreme wet and dry precipitation regimes. We assembled 80 synthetic Landsat images by applying the spatial and temporal adaptive reflectance fusion method (STARFM) to 500 m MODIS and 30 m Landsat-5 Thematic Mapper (TM) data. We tested relationships between site characteristics and the timing of peak Normalized Difference Vegetation Index (NDVI) to assess the effect of climatic stress on the green-up of individual pixels during or after the summer monsoon. Our results show that drought-induced stress led to a fragmented phenological response that was highly dependent on microsite parameters, as both the spatial autocorrelation of peak timing and the number of significant site variables increased during the drought year. Pixels at lower elevations and with higher proportions of herbaceous vegetation were more likely to exhibit dynamic responses to changes in precipitation conditions. Our study demonstrates the complexity of responses within dryland forest ecosystems and highlights the need for standardized monitoring of phenology trends in these areas. The spatial and temporal variability of phenological signals may provide a quantitative solution to the problem of how to evaluate dryland land surface trends across time.

Monitoring land cover changes in Isfahan Province, Iran using Landsat satellite data.

Changes in land cover and land use reveal the effects of natural and human processes on the Earth's surface. These changes are predicted to exert the greatest environmental impacts in the upcoming decades. The purpose of the present study was to monitor land cover changes using Multispectral Scanner Sensor (MSS) and multitemporal Landsat Thematic Mapper (TM) data from the counties of Isfahan Province, Iran, during 1975, 1990, and 2010. The maximum likelihood supervised classification method was applied to map land cover. Postclassification change detection technique was also used to produce change images through cross-tabulation. Classification results were improved using ancillary data, visual interpretation, and local knowledge about the area. The overall accuracy of land cover change maps ranged from 88 to 90.6%. Kappa coefficients associated with the classification were 0.81 for 1975, 0.84 for 1990, and 0.85 for 2010 images. This study monitored changes related to conversion of agricultural land to impervious surfaces, undeveloped land to agricultural land, agricultural land to impervious surfaces, and undeveloped land to impervious surfaces. The analyses of land cover changes during the study period revealed the significant development of impervious surfaces in counties of Isfahan Province as a result of population growth, traffic conditions, and industrialization. The image classification indicated that agricultural lands increased from 2520.96 km(2) in 1975 to 4103.85 km(2) in 2010. These land cover changes were evaluated in different counties of Isfahan Province.

Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia

This paper explores the suitability of remotely sensed data obtained from Landsat imaging sensors, in combination with ancillary forest inventory data, for the ecological monitoring and management of a production mangrove forest in Malaysia. An assessment is presented on the capabilities and limitations of utilizing Landsat Thematic Mapper data for mapping mangrove species and stand age structure in the Matang Mangrove Forest Reserve. The cloud-free subset satellite images used in this study were acquired in May 1988, February 1995, December 1999, March 2001, December 2009 and November 2013. The December 1999 Landsat 7 ETM+ image was used for the error and accuracy assessment because it coincided with the year the reference map was produced. The image classification process included both pixel-based and object-based approaches conducted on a combination of software which included ENVI, ArcGIS 10.1 and eCognition. Results revealed that the pixel-based approach was not effective in delineating species and stand age structure due to the presence of the ‘salt and pepper effect’ in the image output. The object-based approach accurately delineated between commercial and non-commercial mangrove species. It also produced a reliable and accurate stand-age structure map based on three designated classes: clear-felled areas; young mangrove forest (less than 13 years old); and mature mangrove forest (13 years or older). The analysis highlights that the mapping and analysis of mangrove species and stand age structure with Landsat Thematic Mapper image datasets can play a useful role in achieving cost-efficient and ecologically sustainable outcomes in a production mangrove forest.

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition.

Effects of land use transitions on ecosystem services value:A case study of Hunan province

With the rapid development of Chinese economy, many negative effects of land use transitions under the pressure of rapid urbanization on local ecological system and environment have occurred. This paper examines the dynamic patterns of land use transitions in Hunan province, using high-resolution Landsat TM(Thematic Mapper) data in 1995, 2000, 2005 and2010, and socio-economic data from both research institutes and government departments, and assesses the changes of ecosystem services value(ESV) by drawing a connection between the observed land use dynamics and the evaluation of ESV, based on the latest research of Costanza et al. and some revisions adapted to the situation of China. The outcomes indicated that, during the period from 1995 to 2010, the land use changed significantly, characterized by the increase of construction land and the decrease of cultivated land and grassland; ESV of the study area decreased by 0.42%, from 880.78 to 877.09 billion RMB, due to the losses of large quantities of ecological land to construction land. Then, some of the major implications for improving the land use of Hunan province were discussed. The authors argue that it is fundamental to meet the demand of construction land for socio- economic development,meanwhile, to protect regional ecosystem services function and maintain its stability; only in this way Hunan province may realize the sustainable use of ecological resources in the process of rapid urbanization, as well as the integration of economic, social and ecological benefits.

Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation

This paper investigates the retrieval of forest crown closure (CC) from the Landsat Thematic Mapper (TM) data and aerial images with a linear spectral mixture analysis (SMA) method. Anshan is selected as the study area. Two endmember extraction methods were used in this paper: 1) traditional image-based method and 2) up-scaling method. (When we get the fractions of components from a coregistered 0.6-m spatial resolution image, the linear spectral mixture model is applied to unmix the TM image and obtain the required endmembers.) For both methods, four fraction images (sunlit canopy, shaded canopy, sunlit background, shaded background) were calculated by linear spectral mixture model and used to derive CC. Results showed that CC can be fitted best with sum of fractions of sunlit canopy and shaded canopy at S-shaped curve and the up-scaling endmember extraction method is better than traditional image-based endmember extraction method. Finally, the up-scaling endmember extraction method was used to map forest CC in Anshan forested region. The measured forest CC distribution map was used to validate the estimated map. Results show that the estimated CC and measured CC have little difference and the estimated CC is slightly lower. The majority of Anshan forest CC values were between 0.4 and 0.8.

Land use transitions and their effects on water environment in Huang-Huai-Hai Plain, China

This paper analyzes the spatio-temporal dynamic patterns of land use in Huang-Huai-Hai Plain, one of the China's most important grain production bases experiencing rapid urban–rural transformation development, using high-resolution Landsat TM (Thematic Mapper) data and series data of water environmental quality monitoring in 2000 and 2010, and related socio-economic data from government departments. After assessing the change of water environmental quality of Huang-Huai-Hai Plain during 2000–2010, three spatial econometric regression models including Spatial Lag Model (SLM), Spatial Error Model (SEM) and ordinary least squares (OLS) are used to explore the correlationships between land use transitions and water environmental changes. The outcomes indicated that, during the research period, land use pattern changes in the study area were characterized by the loss of large quantities of farmland and the increase of construction land and water body. On the whole, the water environment in the study area was obviously improved, but there also existed partial deterioration as evidenced by the increase of monitoring sections with water environmental quality below Class V. In general, the increases of grassland, forested land and water body have positive effects on water environmental quality while farmland and construction land have negative effects on that. The authors argue that construction land use and farmland use have almost equal negative effects on water environmental quality, and managing agricultural non-point source pollution is pivotal to improve local water environmental quality. Finally, some of the major implications for managing the land and water resources in the plain areas of China as well as other developing countries undergoing rapid urban–rural transformation development are discussed in the aspects of ecological farming practices, rural construction land management, land use planning and urban planning.

An investigation of spatial variation of suspended sediment concentration induced by a bay bridge based on Landsat TM and OLI data

This study investigates the spatial variation of suspended sediment concentration (SSC) induced by Hangzhou Bay Bridge (HBB) in the coastal waters of the East China Sea. Based on Landsat Thematic Mapper (TM), Operational Land Imager (OLI) data and in-situ measurements, spectral characteristics of waters in Hangzhou Bay and SSC retrieved from near infrared single band have been analyzed. The results revealed significant difference of SSC in the water on two sides of HBB. SSC increases downstream of the bridge under conditions of low turbidity (SSC<300mgl−1) upstream water, while SSC decreases when high turbidity water (SSC>400mgl−1) upstream. This study shows that the interaction of bridge piers and currents has important influences on SSC distribution by inducing hydrodynamic factors and by changing suspended sediment transport. Remotely sensed data with high spatial resolution as TM and OLI can be applied to investigate SSC variations induced by a bridge in the bay area.