Landsat Enhanced Thematic Mapper Image Research Articles

Impact of Changes in Land use and land cover on Agricultural Productivity in Northern Nigeria

The challenges posed by changes in land use and land cover are greatly affecting agricultural productivity in the semi-arid region of Nigeria. Land use and land cover change affects ecosystem functions and services, which leads to people migrating from one place to another, predominantly rural dwellers. The research studies the impact of land use and land cover change on agricultural productivity and the factors influencing the changes. To achieve this, a remote sensing technique was employed. The Landsat Thematic Mapper Image of 1990, Landsat Enhanced Thematic Mapper Image of 2005 and Landsat Enhanced Thematic Mapper Image of 2020 were collected and analysed. Six classes of land use and land cover were obtained using Earth Resource Development Assessment System Imagery 9.1 software. The result shows that there was a decrease in the land area occupied by wetland, shrubland and water body, while there was an increase in the land area occupied by bare lands/dunes, settlement and scattered cultivation from the year 1990 to 2020. Increase in bare land/dune, scattered cultivation and settlement were mainly as a result of climate change resulting from overgrazing, a southward movement of dunes from the Sahara desert, increasing farming activities and increasing demand for shelter by the increasing population. The decrease in shrubland, water body, and wetland were also linked to climate change resulting from increasing deforestation, and increasing use of wetland for farming activities. Based on the results of the analysis, it is recommended that measures should be taken to integrate sustainable land management and climate change adaptation options for sustainable Agriculture.

A modified approach for change detection using change vector analysis in posterior probability space

Abstract. The multispectral and multitemporal data coming from satellites allow us to extract valuable spatiotemporal change. Consequently, Earth surface change detection analysis has been used in the past to monitor land cover changes caused by different reasons. Several techniques have been used for that purpose and change vector analysis (CVA) has been frequently employed to carry out automatic spatiotemporal information extraction. This work describes a modified methodology based on Supervised Change Vector Analysis in Posterior probability Space (SCVAPS) with the final aim of obtaining a change detection map in Blida, Algeria. The proposed technique is a Modified version of Supervised Change Vector Analysis Posterior probability Space (MSCVAPS) and it is applied at the same region that the original technique studied in the literature. The classical Maximum Likelihood classifier is the selected method for supervised classification since it provides good properties in the posterior probability map. An improved method for threshold determination based on Double Flexible Pace Search (DFPS) is proposed in this work and it is employed to obtain the most adequate threshold value. Then, the MSCVAPS approach is evaluated by two cases study of the land cover change detection in the region of Blida, Algeria, and in the region of Shunyi District, Beijing, China, using a pair of Landsat Thematic Mapper images and pair of Landsat Enhanced Thematic Mapper images, respectively. The final evaluation is given by the overall accuracy of changed and unchanged pixels and the kappa coefficient. The results show that the modified approach gives excellent results using the same area of study that was selected in the literature.

An integrated approach for groundwater potential zoning in shallow fracture zone aquifers

This study presents an integrated approach for the identification of groundwater occurrences in shallow fracture zone (SFZ) aquifers using remote-sensing, geological, and geophysical data. The Central Eastern Desert of Egypt was selected as a test site for the present study. The distribution of major faults and shear zones was extracted from a fusion image generated by injecting high-spatial resolution phased array L-band synthetic aperture radar (PALSAR) images into Landsat Enhanced Thematic Mapper images. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model was processed to extract the drainage systems, slope, and topographic wetness index (TWI). The multidate PALSAR imagery acquired during rainy and dry seasons was used to estimate the relative soil moisture content. The lithology, fractures, drainage density, slope, TWI, and soil moisture content were used as thematic layers for groundwater occurrence in the SFZ aquifers. A GIS model of groundwater potential was developed by selecting the most probable locations for groundwater in each layer. The results indicate that the spatial distribution of the existing water wells is in agreement with the model where all wells fall in the regions of high groundwater potential zones. A geophysical survey was conducted using ground penetrating radar (GPR), indicating that the high groundwater potential zones are promising for drilling shallow wells. The adopted approach can be used as a cost-effective tool for groundwater exploration in the SFZ aquifers in the study area and in areas of similar geologic and hydrogeologic settings elsewhere.

A Hierarchical Approach to Forest Landscape Pattern Characterization

Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

Estimating east Mediterranean forest parameters using Landsat ETM

The conservation of Jordan's Mediterranean forest requires the use of remote sensing. Among the most important parameters needed are the crown-cover percentage (C) and above-ground biomass (A). This study aims to: (1) identify the best predictor(s) of C using Landsat Enhanced Thematic Mapper (ETM) bands and the derived transformed normalized difference vegetation index (TNDVI); (2) determine if C is a good predictor of A, volume (V), Shannon diversity index (S) and basal area (B); and (3) generate maps of all these parameters. A Landsat ETM image, aerial photographs and ground surveys are used to model C using multiple regression. C is then modelled to A, V, S and B using linear regression. The relationship between C and Landsat ETM bands (1 and 7) plus the TNDVI is significantly high (coefficient of determination R 2 = 0.8) and is used to produce the C map. The generated C map is used to predict A (R 2 = 0.56), V (R 2 = 0.58), S (R 2 = 0.50) and B (R 2 = 0.43). Cross validation for the predicted C map (cross-validation error = 5.3%) and for the predicted forest-parameter maps (cross-validation error = 13.7%–19.9%) shows acceptable error levels. Results indicate that Jordan's east Mediterranean forest parameters can be mapped and monitored for biomass accumulation and carbon dioxide (CO2) flux using Landsat ETM images.

Assessing the Impact of Land Use/Land Cover on Urban Heat Island Pattern in Nanjing City, China

Land use/land cover (LULC) change with urbanization affects urban heat island (UHI). We selected the Landsat Enhanced Thematic Mapper images for year 2005 to retrieve land surface temperature (LST)...

An object-based method to map wetland using RADARSAT-1 and Landsat ETM images: test case on two sites in Quebec, Canada

The Canadian Wildlife Service, Quebec region, of Environment Canada tested a multiscale object-based classification method on two test sites using satellite images to map wetlands in the context of the Canadian Wetland Inventory (CWI). The objective of this study was to assess the method most adapted for the Canadian inventory program to map five wetland classes (bog, fen, swamp, marsh, and shallow water), for a minimal geographical unit of 1 ha, from RADARSAT-1 and Landsat-7 enhanced thematic mapper (ETM) data. The top-down object-based classification selected was based on the Canadian Wetland Classification System and identifies quickly and precisely the ecologically meaningful polygons of wetlands. Validation was done on two levels: (i) between “other” versus “wetland” and (ii) between each class of wetland. Global accuracy values for the first level are greater than 80% for both test sites and about 76% and 67%, respectively, for the two sites for the second level. This approach is well adapted to wetland mapping on both the thematic and the spatial level.

Population Estimation Using Landsat Enhanced Thematic Mapper Imagery

An assessment of two groups of approaches for estimating urban population with remote‐sensing information is presented in this article. These approaches, zonal and pixel‐based models, are applied to Landsat Enhanced Thematic Mapper images of a portion of Columbus, Ohio, to generate population estimates. The zonal approach uses impervious surface fraction, spectral radiance, and land‐use/land‐cover classification to derive population estimates. The pixel‐based approach uses impervious surface fraction and spectral radiance to estimate the population of residential areas. To assess robustness, these models were applied to Dayton, Ohio. A comparative study indicates that the models generated promising results in estimating regional population counts. However, zonal regression with spectral radiance produced large errors (76%) for census block groups, whereas other models gave significantly better estimation accuracy. Comparing the performance of the indicators, impervious surface fraction is competitive, and slightly but consistently better than land‐use classification. In comparison with traditional zonal approaches, pixel‐based models give somewhat better estimation accuracy.

Spatial Resolution Improvement by Merging MERIS–ETM Images for Coastal Water Monitoring

The Medium Resolution Imaging Spectrometer (MERIS) launched in March 2002 and has been providing images since June 2002. Before its launch, we had implemented a method to improve its resolution by merging its images with Landsat Enhanced Thematic Mapper images in order to preserve the best characteristics of the two images (spatial, spectral, temporal). We now present the results of this method for real MERIS images (level 1b and 2) in a coastal area. The robustness of the method is studied as well as the influence of the delay between the acquisitions of the two images.

Prediction of spatial ET-fluxes using remote sensing and field data of selected areas in the Eastern Part of Abu Dhabi, United Arab Emirates

Evapotranspiration (ET) is a major source of water depletion in arid and semi-arid environments; and it is a poorly quantified variable in the hydrological cycle. Remote sensing has the potential application to quantify this variable especially at large scale. The present study reports methodology useful to determine whether derived variables from remotely sensed data, such as vegetation and soil brightness indices, could be used to predict ET. To achieve this goal, various regression analyses were conducted between data derived from satellites, field meteorological stations, and ET values. Selected sub-scenes of Landsat Enhanced Thematic Mapper images free of cloud were used to derive Normalized Difference Vegetation Index (NDVI) and Soil Brightness Index using ER-Mapper and JMP software packages. From the obtained relationship between NDVI and ET, it was observed that ET increases sharply with increase in NDVI. The predicted ET results obtained from the multiple regression functions of field ET, NDVI, solar radiation, wind velocity, and/or temperature are comparable with the ET values obtained by Penman-Monteith method. The results showed that a remotely sensed vegetation index could be used, indirectly, to determine ET values. However, there is still considerable work to be done before simple and full automated extraction of ET from the reported methods can be achieved for large-scale applications.

Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data

This paper analyses and maps the spatial distribution of soil moisture using remote sensing: National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Landsat-Enhanced Thematic Mapper (ETM+) images. The study was carried out in the central Ebro river valley (northeast Spain), and examines the spatial relationships between the distribution of soil moisture and several meteorological and geographical variables following a long, intense dry period (winter 2000). Soil moisture estimates were obtained using thermal, visible and near-infrared data and by applying the ‘triangle method’, which describes relationships between surface temperature (Ts ) and fractional vegetation cover (Fr ). Low differences were found between the soil moisture estimates obtained using AVHRR and ETM+ sensors. Soil moisture estimated using remote sensing is close to estimations obtained from climate indices. This fact, and the high similarity between estimations of both sensors, suggests the reasonable reliability of soil moisture remote sensing estimations. Moreover, in estimations from both sensors the spatial distribution of soil moisture was largely accounted for by meteorological variables, mainly precipitation in the dry period. The results indicate the high reliability of remote sensing for determining areas affected by water deficits and for quantifying drought intensity.

Spectral and temporal linear mixing model for vegetation classification

The objective of this study is to find a better method for sub-pixel classification of vegetation. The proposed new technique of a linear mixing model (LMM) is the sequential combination of spectral LMM and temporal LMM. Sub-pixel components of ‘relative green vegetation’ are derived by spectral LMM; sub-pixel components of vegetation types are estimated by subsequent temporal LMM. The proposed method was applied to five temporal Landsat Enhanced Thematic Mapper (ETM) images for the year 2000 for areas south of Lake Baikal, Russia. Dominant vegetation types there are pine, birch/aspen, shrubs and wheat with weedy plants. Ground truth data of vegetation types were prepared by field survey and visual interpretation of Landsat ETM images by experts. Both the comparisons of classification results among the proposed method and conventional LMM methods and the simulation results among them indicate that the proposed spectral and temporal LMM has better accuracy than conventional methods.

Assessing forest canopy density in a highly variable landscape using Landsat data and FCD Mapper software

Summary Forest canopy density (FCD), estimated with the FCD Mapper, was correlated with basal area and predominant height (PDH) for 48 field plots, measured in highly variable native eucalypt forest at Toolara, south-eastern Queensland, Australia. The Mapper was produced for the International Tropical Timber Organisation and is available on a CD-ROM. It estimates FCD as an undefined index of canopy density using reflectance characteristics of Landsat Enhanced Thematic Mapper images. The Mapper is a ‘semi expert’ computer program which uses interactive screens to allow the operator to make decisions concerning the classification of land into bare soil, grass or forest. The results of a FCD classification are therefore dependent on the operator's decisions and were found to be highly sensitive to small changes in settings. A positive, weak (r 2 = 0.36) nonlinear relationship of FCD with basal area was observed, while a strong (r 2 = 0.68) similar relationship was observed between FCD and PDH. The strong relationship of FCD with PDH suggests that this remote sensing technique has promise for forest inventory, but that a quick and robust method of measuring FCD in the field is still required for ground truthing.

Impact of Topographic Normalization on Land-Cover Classification Accuracy

For pixel classifying algorithms to perform effectively, effects of topographic relief must be minimized or removed. In areas of high topographic relief, problems arise when spectral variations in ground target illumination and radiance, owing to differences in incident radiation and nonLambertian reflectance behavior, respectively, cause identical land-cover types to reflect differently, or different cover types to reflect similarly. A Landsat Enhanced Thematic Mapper image was processed using band ratios, the Minnaert Correction, aspect partitioning, and combinations of these treatments to generate independent land-cover classifications. Treatment classification accuracy was determined using error matrices and the Kappa statistic. Producer’s and User’s Accuracies were examined to determine if treatments were superior at producing greater classspecific accuracy. None of the treatments produced a significantly more accurate classification; however, assessment of class-specific accuracies indicated accuracy gains using aspect partitioning alone or in combination with the Minnaert Correction.

Forest Transitions in Mexico: Institutions and Forests in a Globalized Countryside*

Forest-transition theory suggests that economic development eventually leads to forest recovery, but there is great uncertainty about the existence, the characteristics, and the mechanisms of forest transitions that might be occurring under current socioeconomic conditions. A case study in a small region of highland Mexico finds agricultural abandonment and forest regeneration, but also forest degradation due to woodcutting. A discussion locates those findings in the international political economy of agriculture and emigration in Mexico, which are substantially different from the conditions associated with forest recovery in developed countries. Although regional forest transitions are possible in marginal agricultural areas such as that studied here, their environmental implications depend on the evolution of social institutions coordinating rural people's environmental actions. * The author would like to acknowledge the useful critiques from four anonymous reviewers and express his thanks to Jeff Ueland for his invaluable assistance in the preparation of Figure 1. The background of that figure comes from Landsat Enhanced Thematic Mapper images kindly provided by José AntonioBenjamín Ordóñez Díaz and Omar Raúl Masera Cerutti from their project CONACyT 32715-N, funded by the Mexican Council on Science and Technology. Figure 1 Location of the case-study communities against a backdrop from the Landsat Enhanced Thematic Mapper (band 3) from the spring of 2000, the dry season.