Thematic Mapper Bands Research Articles

The use of remote sensing technology in defining the water depth in the lakes and water bodies: Western Iraq as a case study

The study's primary purpose is to explore an appropriate way of monitoring and assessing water depths using the satellite remote sensing technique of the Al Habbaniyah Lake in Iraq. This research studied the experience-conditions (thresholds) of different bands for multi-temporal satellite image data with different satellite image sensors (Landsat 5-TM, and EO1-ALI) for the same region, to recognize regions of water depths. The threshold values are taken that to separate the Al Habbaniyah Lake to the required depths (shallow, deep, and very deep), as a supervised method. A three-dimension feature space plot had used to represent these regions. The relationship of the mean values of the three separated water regions with all TM and ALI bands is studied. Other lakes in Iraq were used to actualize the validity and accuracy of this technique to find the water depth regions; Al Qadisiyha and Al Mosul lakes are in the West and North of Iraq of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-5 Thematic Mapper (TM) satellite images respectively. This technique succeeded in determining Al Habbaniyah lake depths but failed to recognize the regions of water depths for some of the lakes which have the big depths by correct form such as; Al Qadisiyha and Al Mosul lakes.

An Estimation of the Land Surface Temperature, Derived from the Landsat Satellite, for the Major Cities in Sindh Province, Pakistan

Thermal infrared remote sensing data are used to make estimates of the land surface temperature (LST) by recording the radiant energy emitted by the surface of the Earth. Satellite data and image processing software also allow for LST estimation. Since its launch, two thermal infrared bands aboard the Landsat satellite have been used to continuously observe Earth, providing data for the estimation of LST and the normalized difference vegetation index (NDVI). Due to the significant uncertainty in data from both Landsat 5 thematic mapper (TM) thermal band 6, which has a wavelength of 10.40–12.50 m, and Landsat 8 thermal infrared sensor (TIRS) Band 11, as indicated by USGS calibration notifications, it was advised to use TIRS Band 10 data as a single spectral band for LST estimation. For LST estimation from Landsat 5 and Landsat 8, the mono-window (MW) approach was used with TM and TIRS Bands 6, 10, and data with a resolution of 120 and 100 m. (Path-152 and Row-40, 41, 42, and 43). The emission coefficient was calculated using the operational land imager (OLI) Bands 4 and 5 (30 m resolution) and the normalized difference vegetation index (NDVI) proportion of vegetation method. Based on the results, the LST was higher in the arid regions, whereas the NDVI was higher in the less arid parts. Also, the LST findings were compared to the air temperature data, both data were found to be consistent with one another. The approach of MW algorithm could be a useful tool for estimating LST from TM data acquired from Landsat 5 and Landsat 8 TIRS Bands 6 and 10.

Anisotropy Parameterization Development and Evaluation for Glacier Surface Albedo Retrieval from Satellite Observations

Glacier albedo determines the net shortwave radiation absorbed at the glacier surface and plays a crucial role in glacier energy and mass balance. Remote sensing techniques are efficient means to retrieve glacier surface albedo over large and inaccessible areas and to study its variability. However, corrections of anisotropic reflectance of glacier surface have been established for specific shortwave bands only, such as Landsat 5 Thematic Mapper (L5/TM) band 2 and band 4, which is a major limitation of current retrievals of glacier broadband albedo. In this study, we calibrated and evaluated four anisotropy correction models for glacier snow and ice, applicable to visible, near-infrared and shortwave-infrared wavelengths using airborne datasets of Bidirectional Reflectance Distribution Function (BRDF). We then tested the ability of the best-performing anisotropy correction model, referred to from here on as the ‘updated model’, to retrieve albedo from L5/TM, Landsat 8 Operational Land Imager (L8/OLI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and evaluated these results with field measurements collected on eight glaciers around the world. Our results show that the updated model: (1) can accurately estimate anisotropic factors of reflectance for snow and ice surfaces; (2) generally performs better than prior approaches for L8/OLI albedo retrieval but is not appropriate for L5/TM; (3) generally retrieves MODIS albedo better than the MODIS standard albedo product (MCD43A3) in both absolute values and glacier albedo temporal evolution, i.e., exhibiting both fewer gaps and better agreement with field observations. As the updated model enables anisotropy correction of a maximum of 10 multispectral bands and is implemented in Google Earth Engine (GEE), it is promising for observing and analyzing glacier albedo at large spatial scales.

Landsat-Based Estimation of the Glacier Surface Temperature of Hailuogou Glacier, Southeastern Tibetan Plateau, Between 1990 and 2018

Glacier surface temperature (GST) is influenced by both the energy flux from the atmosphere above and the thermal dynamics at the ice–water–debris interfaces. However, previous studies on GST are inadequate in time series research and mountain glacier surface temperature retrieval. We evaluate the GST variability at Hailuogou glacier, a temperate glacier located in Southeastern Tibetan Plateau, from 1990 to 2018. We utilized a modified mono-window algorithm to calculate the GST using the Landsat 8 thermal infrared sensor (TIRS) band 10 data and Landsat 5 thematic mapper (TM) band 6 data. Three essential parameters, including the emissivity of ice and snow, atmospheric transmittance, and effective mean atmospheric temperature, were employed in the GST algorithm. The remotely-sensed temperatures were compared with two other single-channel algorithms to validate GST algorithm’s accuracy. Results from different algorithms showed a good agreement, with a mean difference of about 0.6 ℃. Our results showed that the GST of the Hailuogou glacier, both in the upper debris-free part and the lower debris-covered tongue, has experienced a slightly increasing trend at a rate of 0.054 ℃ a−1 during the past decades. Atmospheric warming, expanding debris cover in the lower part, and a darkening debris-free accumulation area are the main causes of the warming of the glacier surface.

Characterization of short-term salinity fluctuations in the Neretva River Delta situated in the southern Adriatic Croatia using Landsat-5 TM

Abstract Elevated soil and water salinity can lead to increased risk of crop yield and quality reduction as well as land degradation. This is especially emphasised in arid and semi-arid regions with high levels of evaporation and irrigation practice. In this study, we assessed the potential of applying Landsat-5 Thematic Mapper (TM) to evaluate low to moderately salt-affected agricultural areas of the Neretva River Delta, situated in the southern Adriatic region of Croatia. Monthly measurements of water electrical conductivity (ECw), sampled from drainage/irrigation canals, were used as in situ salinity indicators. The relationship between agricultural plots reflectance (32 Landsat-5 TM images, distributed over 22 months from May 2009 to October 2011) and water salinity (measured at ten monitoring stations) was analysed using six Landsat-5 TM bands and spectral indices. Correlation analysis, simple (SLR) and multiple linear regression (MLR) models were implemented to predict ECw values using the satellite data. Very strong correlations were achieved in the spatial domain for August 2009, September 2010 and September and October 2011, while correlations in the temporal domain had relatively lower values. The Landsat-5 TM data managed to explain 43% of ECw variance using the SLR model, and 62% of the variance using the MLR model with three regressors. Landsat-5 TM remotely sensed data, paired with the in situ observations, showed a promising potential in enhancing the quality of environmental monitoring of low to moderately salt-affected agricultural fields in the irrigated deltas.

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

AbstractSpecies distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.

Dynamic analysis of Robinia pseudoacacia forest health levels from 1995 to 2013 in the Yellow River Delta, China using multitemporal Landsat imagery

ABSTRACTThe Robinia pseudoacacia forest in the Yellow River Delta (YRD), China, was planted in the 1970s and has continuously suffered dieback and mortality since the 1990s. Timely and accurate information on forest growth and forest condition and its dynamic change as well is essential for assessing and developing effective management strategies. In this study, multitemporal Landsat imagery was used to analyze and monitor changes of the R. pseudoacacia forest in the YRD from 1995 to 2013. To do so, Landsat image band reflectance, three fraction images calculated by using a multiple endmember spectral mixture analysis (MESMA) method, and four vegetation indices (VIs) were used to discriminate three health levels of R. pseudoacacia forest in years 1995, 2007, and 2013 with a random forest (RF) classifier. The four VIs include a difference infrared index (DII) developed in this study, normalized difference vegetation index, soil-adjusted vegetation index, and normalized difference infrared index (NDII), all of which were computed from Landsat Thematic Mapper and Operational Land Imager multispectral (MS) bands. The dynamic changes of the forest health levels during the periods of 1995–2007 and 2007–2013 were analysed. The analysis results demonstrate that three fraction images created by MESMA method and four VIs were powerful in separating the three forest health levels. In addition to the Landsat MS bands, the additional three fraction images increased the classification accuracy by 14−20%; if coupled with the four VIs, the overall accuracy was further increased by 5−6%. According to the importance values calculated by RF classifier for all input features, the DII vegetation index was the second effective feature, outperforming NDII. From 1995 to 2013, a total of 2615 ha of forest in the study area suffered from mortality or loss.

Evaluating land surface albedo estimation from Landsat MSS, TM, ETM +, and OLI data based on the unified direct estimation approach

Surface albedo is widely used in climate and environment applications as an important parameter for controlling the surface energy budget. There is an increasing need for albedo data to be available for use in applications that require a fine spatial resolution and for validating coarse-resolution datasets; however, such products with long-term global coverage are not available thus far. Existing algorithms for Landsat albedo estimation all require surface reflectance from explicit and reliable atmospheric correction, which may sometimes be unavailable or carry uncertainties due to saturated visible bands or a lack of dense vegetation. In addition, most of the existing algorithms require concurrent clear-sky observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for bidirectional reflectance distribution function (BRDF) correction, which limited the data availability for Landsat albedo estimation. To overcome these problems, in this study, we adopt the direct estimation approach previously used with coarser resolution data, such as MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS), and apply it to multiple Landsat data obtained by Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). By incorporating Landsat spectral response functions and a database of bidirectional reflectance distribution function (BRDF) into radiative transfer simulations, a unified algorithm is developed to estimate surface albedo directly from the Landsat top-of-atmospheric reflectance data obtained by MSS, TM, ETM+, and OLI with few ancillary inputs. To overcome the saturation problems in the visible bands of TM and ETM+ over very bright surfaces, a refined approach is employed by using only non-saturated bands. The validation results against ground measurements over various land cover types and climate regions show that our algorithm is effective for both snow-free and snow-covered surfaces and can achieve root-mean-square errors (RMSEs) of not more than 0.034. In addition, we show the high potential of the earlier MSS data for producing consistent surface albedo estimations based on inter-comparison with TM-based results with RMSEs of 0.011–0.017 and R2 of 0.858–0.963. This long-term, 30-m resolution surface albedo estimation can date back to the early 1980s, which allows for improved understanding of long-term climate change and land cover change effects.

Modelling and mapping of above ground biomass (AGB) of oil palm plantations in Malaysia using remotely-sensed data

ABSTRACTEstimating accurate above ground biomass (AGB) of oil palm plantation in Malaysia is crucial as it serves as an important indicator to assess the role of oil palm plantations in the global carbon cycle, particularly whether it serves as carbon source or sink. Research on oil palm AGB in Malaysia using remote sensing is almost insignificant and it has known that remote sensing provides easy, inexpensive and less time consuming over larger areas. Therefore, this study focuses on evaluating the potential of Landsat Thematic Mapper (TM) data with combination of field data survey to predict AGB estimates and mapping the oil palm plantations. The relationships of AGB with individual TM bands and various selected vegetation indices were examined. In addition, various possibilities of data transform were explored in statistical analysis. The potential models selected were obtained using backward elimination method where R2, adjusted R2 (R2adj), standard error of estimate (SEE), root mean squared error (RMSE) and Mallows’s Cp criterion were examined in model development and validation. It was found that the most promising model provides moderately good prediction of about 62% of the variability of the AGB with RMSE value of 3.68 tonnes (t) ha-1. In conclusion, Landsat TM offers the low cost AGB estimates and mapping of oil palm plantations with moderate accuracy in Malaysia.

Urban Growth Monitoring using Remote Sensing and Geo-Informatics: Case Study of Gandhinagar, Gujarat State (India)

The Gandhinagar town has undergone unprecedented urban growth after assuming the status of a capital of Gujarat state, India in 1971. The Gandhinagar city lies on the western bank of river Sabarmati and city is also known as the “Tree capital of India” because of greenery with 54% green cover. The urban sprawl in Gandhinagar town was analyzed using multi-temporal Landsat TM data from 1981 to 2015. Spectral indices namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Built-up Index (BUI) were generated from the Landsat TM bands covering visible Red (R), Near Infrared (NIR) and Short Wave Infrared (SWIR) wavelength regions. Spectral variations in built-up, open spaces, urban vegetation and water areas were studied by generating two-dimensional spectral plots of NDBI and BUI. The spectral response of built up areas like Urban-dense and urban-sparse classes are quite distinct from other cover types like open spaces, urban vegetation and water. The results indicate that the normalized difference built-up index (NDBI) and Built-up Index (BUI) were very useful for mapping urban built-up areas in Gandhinagar town using Landsat Thematic Mapper (TM) data. The built-up area in Gandhinagar increased from 1100.1 ha during Jan 1989 to 1907.3 ha during Jan 2015 which indicates about 73 per cent increase in the built-up area during the period of 26 years. The urban vegetation which includes parks and gardens in the city has also shown increasing trend during the period of 1989 to 2015. However, the area under open spaces has decreased as the development of built-up area has increased as per development plan of the Gandhinagar town.

Comparison of remote sensing indices for monitoring of desert cienegas

ABSTRACTThis research considers the applicability of different vegetation indices at 30 m resolution for mapping and monitoring desert wetland (cienega) health and spatial extent through time at Cienega Creek in southeastern Arizona, USA. Multiple stressors including the risk of decadal-scale drought, the effects of current and predicted global warming, and continued anthropogenic pressures threaten aquatic habitats in the southwest and cienegas are recognized as important sites for conservation and restoration efforts. However, cienegas present a challenge to satellite-imagery based analysis due to their small size and mixed surface cover of open water, exposed soils, and vegetation. We created time series of five well-known vegetation indices using annual Landsat Thematic Mapper (TM) images retrieved during the April–June dry season, from 1984 to 2011 to map landscape-level distribution of wetlands and monitor the temporal dynamics of individual sites. Indices included the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII). One topographic index, the Topographic Wetness Index (TWI), was analyzed to examine the utility of topography in mapping distribution of cienegas. Our results indicate that the NDII, calculated using Landsat TM band 5, outperforms the other indices at differentiating cienegas from riparian and upland sites, and was the best means to analyze change. As such, it offers a critical baseline for future studies that seek to extend the analysis of cienegas to other regions and time scales, and has broader applicability to the remote sensing of wetland features in arid landscapes.

Delineation of Debris-Covered Glaciers Based on a Combination of Geomorphometric Parameters and a TIR/NIR/SWIR Band Ratio

Accurate mapping of glaciers is important for the management of water resources, studying glacier mass-balance, and monitoring glacier health. However, many glaciers are partially covered by debris of varying thicknesses, and these debris-covered glacier areas are difficult to distinguish clearly from the adjacent valley rock using optical remote sensing data due to their similar reflectance in the visible-to-near-infrared (VNIR) wavelength region. For better detection of the debris-covered glacier margin, a new method was proposed here which considers glacier terrain features (concave and convex landscapes) as well as VNIR reflectance, shortwave-infrared reflectance (SWIR), and thermal infrared (TIR) information. The proposed method uses several geomorphometric parameters (slope, plan, and profile curvature) and a Landsat Thematic Mapper (TM) band ratio image TM TIR/(TM NIR/TM SWIR) for mapping the Yengisogat glacier in the Shaksgam valley of China. The final debris-covered glacier boundary is generated by thresholding the band ratio image and overlaying it onto the result derived from cluster analysis performed using plan, profile curvature, and combined with mean of slope information. Evaluation of the final debris-covered glacier outline was carried out through comparison with GIS data from several reference data sets, and the final Yengisogat glacier outline had a difference of less than 1% in area compared to the reference data sets.

PALSAR-FBS L-HH Mode and Landsat-TM Data Fusion for Geological Mapping

Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural lineaments extraction using PALSAR (Phased Array type L-band Synthetic Aperture Radar ) Fine Beam Single (FBS) L-HH polarization and Landsat-5 TM (Thematic Mapper ) datasets. These data were preprocessed to retrieve ground surface reflectance and backscatter coefficients. To overcome the geometry acquisition between the two sensors, they were geometrically and topographically rectified using ASTER-V2 DEM. Intensity-Hue-Saturation, directional filters and automatic lineaments extraction were applied on the datasets for lithological units’ discrimination and structural delimitation for potential mineral exploration. The obtained results showed good relationship among the topographic morphology, rock-substrate, structural variations properties, and drainage network. The spectral variations were easily associated with lithological units. Likewise, the morpho-structural information highlighted in the PALSAR image was visible without altering the radiometric integrity of the details in TM bands through the fusion process. Moreover, predominant lineaments directions trending NE-SW, NS, and NW-SE were identified. Results of this study highlighted the importance of the PALSAR FBS L-HH mode and TM data fusion to enhance geological features and lithological units for mineral exploration particularly in tropical zones.

REGIONAL GEOLGICAL MAPPING IN TROPICAL ENVIRONMENTS USING LANDSAT TM AND SRTM REMOTE SENSING DATA

Abstract. Landsat Thematic Mapper (TM) and Shuttle Radar Topography Mission (SRTM) data were used to produce geological maps in tropical environments. Lineament, lithology and landform maps were produced for all states in peninsular Malaysia in this study. Kedah, Perak and Terengganu states have been selected as case studies to demonstrate the results of the data and techniques used. Directional filtering technique was applied to Landsat TM bands 4, 5 and 3 for lineament mapping. The lithology map was produced using Landsat TM bands combination consist of bands 4, 3 and 2. Digital elevation model and landform map were produced using SRTM data in 3 Dimension (3D) and 2 Dimension (2D) perspective views, respectively. The produced geological maps and the remote sensing data and methods applied in this study are mostly appropriate for hazard risk mapping applications and mineral exploration projects in the peninsular Malaysia and tropical environments.

Spectral index utility for summer urban heating analysis

The land surface temperature (LST) retrieval in an urban environment by thermal remote sensing is a widespread scientific topic and several studies have been made to point out the correspondence between spectral indices and LST pattern. This work evaluates the potential of the spectral indices introduced in the literature, more than 30, by assessing their correlation with the summer LST in the heterogeneous urban area of Rome, Italy, considering two different triplets of images acquired during 2009 and 2011 by the Landsat Thematic Mapper (TM). The spectral indices have been divided into vegetation and built up-soil indices employing the reflective TM bands, and then ranked on the basis of their linear and monotonic relationship with the LST. Vegetation indices have a strong negative correlation with LST: vegetation area (VA), nonlinear index, modified soil adjusted vegetation index exhibit a greater Pearson and Spearman correlation coefficient with LST. The more useful spectral indices for built up and soil analysis, exhibiting a greater positive correlation with LST, are the impervious surface area (ISA), the bare soil index, the index-based built-up index, and the normalized difference built-up index. Interesting indications of the impact on the spectral index performance of specific land-cover classes embedded in an urban environment, such as the bare soil and the water classes were pointed out; for example, the reduction of the ISA and VA capability to display the full dynamic range of the LST pattern.

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years) comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images.

Geological and Geomorphological Applications of Low-Angle Illumination Satellite Imagery in Northern Ireland

Some geoscientists consider satellite imagery as having limited research potential due to their inability to elicit useful information. However, their lack of information extraction is often partly caused by a poor understanding of the importance of such parameters as illumination angle or waveband selection. For the analysis of geological or geomorphological features in vegetated terrain, high-angle illumination imagery acquired between March and October reveals little information. However, low-angle illumination imagery (<10° solar elevation) obtained in December clearly displays many geomorphological and geological structures not apparent at a higher sun angle. The North Antrim End Moraine can easily be mapped as a single feature on a December image. The Tempo-Sixmilecross Fault and Omagh Fault can also be readily mapped at low-angle illumination and the spatial relationships of these faults to associated structures readily determined. In vegetated terrain Thematic Mapper band 5 is superior to Thematic Mapper band 4 for information extraction. This is partly due to the lower reflectance variance for vegetation on Thematic Mapper band 5 compared with Thematic Mapper band 4, thus the continuity of features is not disrupted.

Following a site-specific secondary succession in the Amazon using the Landsat CDR product and field inventory data

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha−1 for PF versus 98.5 ± 21.4 ton ha−1 for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = −0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA.

Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification

This study was focused on addressing the need for accurate land-use/land-cover classification (LULC) maps in Iran and in other similarly developing countries. To generate and validate a new LULC map for northeastern Iran’s 2037.5 km2 Hable-roud watershed, a step-by-step process was developed and implemented, consisting of image preprocessing, extraction of training and reference sampling locations, decomposition of multi-spectral thematic mapper bands into features by independent component analysis methods, classification using these features and slope maps, enhancement of land-use classes through image segmentation and zonal statistics, then through consideration of normalized difference vegetation index and climatic zones, followed by ground truthing. This newly developed approach provided maps that distinguished dryland farming, irrigated farmland, forest plantations, and low-, medium-, and high-vegetation density rangelands, while currently available maps for the watershed lef t 39% of lands unclassified or in combined classes. The new maps’ ground-truthing-based overall accuracy and kappa coefficient were 88.3% and 0.83, respectively. In order to develop such an improved LULC map, it was necessary to go beyond the mere analysis of reflectance information, to incorporating climatic and topographic data through this newly proposed step-by-step approach.