Landsat Multispectral Scanner Data Research Articles

Wetland Hydroperiod Change Along the Upper Columbia River Floodplain, Canada, 1984 to 2019

Increasing air temperatures and changing hydrological conditions in the mountainous Kootenay Region of British Columbia, Canada are expected to affect floodplain wetland extent and function along the Columbia River. The objective of this study was to determine the seasonally inundated hydroperiod for a floodplain section (28.66 km2) of the Upper Columbia River wetlands complex using time series satellite image observations and binary open water mask extraction. A mid pixel resolution (30 m) optical satellite image time series of 61 clear sky scenes from the Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) sensors were used to map temporal variations in floodplain open water wetland extent during the April to October hydrologically active season from 1984 to 2019 (35 years). The hydroperiod from the first 31 scenes (T1: 18 years) was compared to the second 30 (T2: 16 years) to identify changes in the permanent and seasonal open water bodies. The seasonal variation in open water extent and duration was similar across the two time periods but the permanent water body extent diminished by ~16% (or ~3.5% of the floodplain). A simple linear model (r2 = 0.87) was established to predict floodplain open water extent as a function of river discharge downstream of the case study area. Four years of Landsat Multi-Spectral Scanner (MSS) data from 1992 to 1995 (12 scenes) were examined to evaluate the feasibility of extending the hydroperiod record back to 1972 using lower resolution (60 m) archive data. While the MSS hydroperiod produced a similar pattern of open water area to duration to the TM/OLI hydroperiod, small open water features were omitted or expanded due to the lower resolution. While MSS could potentially extend the TM/OLI hydroperiod record, this was not performed as the loss of features like the river channel diminished its value for change detection purposes. Radarsat 2 scenes from 2015 to 2019 were examined to evaluate the feasibility of continued mountain valley hydroperiod monitoring using higher spatial and temporal resolution sensors like the Radarsat Constellation Mission (RCM). From the available horizontal transmit/receive (HH) single polarization sample set (8 scenes), the hydroperiod pattern of open water extent to duration was similar to the longer Landsat time series and possessed greater feature detail, but it was significantly reduced in seasonal inundation area due to the systematic omission of open water areas containing emergent vegetation. However, accepting that differences exist in sensor-based hydroperiod attributes, the higher temporal resolution of RCM will be suited to mountain floodplain inundation monitoring and open water hydroperiod analysis.

Improving Landsat Multispectral Scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration

The Landsat Multispectral Scanner (MSS) data sensed by the Landsat 1–5 satellites make up a significant portion of the early Landsat data record. However, accurate MSS image geolocation has been difficult to achieve systematically due to a number of factors associated primarily with the older sensor and satellite technology. As of August 2019, only 49% of the Landsat MSS archive could be processed at the highest-level L1TP (precision and terrain corrected) level, and the remainder were processed as L1GS (systematically corrected) with inaccurate geolocation and no terrain correction. This paper presents a methodology to improve the geolocation of MSS time series. The methodology uses an area- and feature-based least-squares matching scale-space algorithm, with a time series registration implementation, that we developed previously using Landsat-8 and Sentinel-2 imagery. The methodology requires that at least three L1TP images in the time series acquired over a given path/row are available. A linear combination of a polynomial transformation and multiple radially symmetric radial-basis-functions (RBFs) to model local uncorrected terrain relief effects present in the L1GS images are used. The processing is automated and applied in two passes. The first pass screens L1TP images to select the well-aligned ones that are used as references. The second pass registers the target images, including the L1GS images and any misaligned L1TP images, to all the reference L1TP images. The transformation coefficients for each registered target image are derived by least-squares adjustment using densely-matched tie-points between the target and the reference images. The methodology is demonstrated using 12 months of Landsat-4 MSS images at four Landsat path/row locations that contain agricultural, mountainous, and coastal regions, including a total of 43 L1TP and 31 L1GS images. There were sufficient tie-points to characterize the degree of misregistration of 14 L1GS images that had significant mean misregistration shifts ranging from 7.33 to 17.42 60 m pixels. In addition, at one site, two L1TP images were found to be misaligned and have mean misregistration shifts of 1.27 and 2.20 60 m pixels. The methodology provides sub-pixel registration accuracy - after registration, the mean misregistration shifts for the 14 L1GS and two misaligned L1TP images varied from only 0.10 to 0.41 60 m pixels. The methodology does not use a digital elevation model, and examples illustrate that although the RBF transformations can compensate terrain relief distortion effects, larger (~0.5 to 1.0 pixel) misregistration errors can remain in areas with highly variable terrain relief. Results are also provided for Landsat-1 MSS imagery to demonstrate the applicability of the methodology to even the earliest part of the Landsat record. Detailed qualitative and quantitative results are presented and indicate the potential of the methodology to improve the geolocation of the Landsat MSS data record that is discussed with recommendations for future research.

Forest Cover Change Analysis Using Remote Sensing Techniques in Madhupur Sal Forest of Bangladesh

Deforestation, degradation, damages, transformation and over exploitation of forests are the common problem in different parts of the world. Timely monitoring and assessment of forest resources may help to address and identify the above mentioned problems and thus proper guidance may be given the forest resources manager for rational planning and management of forests. Apart from the conventional methods of forest monitoring, remote sensing with its unique capability of synoptic viewing, real time and repetitive nature offers a potential tool for monitoring and evaluation of forest resources and hence remote sensing technology has been successfully used in various studies like forest inventory, monitoring of forest cover changes and forest damage assessment. In the present research forest cover change analysis in ‘Madhupur Sal Forest’ located in central part of Bangladesh has been investigated using satellite remote sensing data and spatial analysis. Transformation of ‘Sal forest’ to other landuse has been studied using the Landsat MSS (Multi Spectral Scanner) data of 1973 and Landsat 8 OLI (Operational Land Imager) data of 2015. Driving forces behind the transformation of ‘Sal forest’ has also been investigated through GPS (Global Positioning System) based ground verification and interview with the people living in the locality.J. Environ. Sci. & Natural Resources, 10(2): 73-78 2017

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.

Land Cover Change Monitoring Using Landsat MSS/TM Satellite Image Data over West Africa between 1975 and 1990

Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data is a reliable and consistent source of information on land cover dynamics from the 1970s. This study examines land cover changes occurring between 1975 and 1990 in West Africa using a systematic sample of satellite imagery. The primary data sources for the land cover classification were Landsat Multispectral Scanner (MSS) for 1975 and Landsat Thematic Mapper (TM) for the 1990 period. Dedicated selection of the appropriate image data for land cover change monitoring was performed for the year 1975. Based on this selected dataset, the land cover analysis is based on a systematic sample of 220 suitable Landsat image extracts (out of 246) of 20 km × 20 km at each one degree latitude/longitude intersection. Object-based classification, originally dedicated for Landsat TM land cover change monitoring and adapted for MSS, was used to produce land cover change information for four different land cover classes: dense tree cover, tree cover mosaic, other wooded land and other vegetation cover. Our results reveal that in 1975 about 6% of West Africa was covered by dense tree cover complemented with 12% of tree cover mosaic. Almost half of the area was covered by other wooded land and the remaining 32% was represented by other vegetation cover. Over the 1975–1990 period, the net annual change rate of dense tree cover was estimated at −0.95%, at −0.37% for the other wooded land and very low for tree cover mosaic (−0.05%). On the other side, other vegetation cover increased annually by 0.70%, most probably due to the expansion of agricultural areas. This study demonstrates the potential of Landsat MSS and TM data for large scale land cover change assessment in West Africa and highlights the importance of consistent and systematic data processing methods with targeted image acquisition procedures for long-term monitoring.

Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia

We present a piecewise linear decision tree model for predicting percent of soil organic C (SOC) in the agricultural zones of Australia generated using a machine learning approach. The inputs for the model are a national database of soil data, national digital surfaces of climate, elevation, and terrain variables, Landsat multispectral scanner data, lithology, land use, and soil maps. The model and resulting map are evaluated, and insights into biogeological surficial processes are discussed. The decision tree splits the overall data set into more homogenous subsets, thus in this case, it identifies areas where SOC responds closely to climatic and other environmental variables. The spatial pattern of SOC corresponds well to maps of estimated primary productivity and bioclimatic zones. Topsoil organic C levels are highest in the high rainfall, temperate regions of Tasmania, Victoria, and Western Australia, along the coast of New South Wales and in the wet tropics of Queensland; and lowest in arid and semiarid inland regions. While this pattern broadly follows continental vegetation, soil moisture, and temperature patterns, it is governed by a spatially variable hierarchy of different climatic and other variables across bioregions of Australia. At the continental scale, soil moisture level, rather than temperature, seems most important in controlling SOC.

Change detection of land cover and land use using remote sensing and GIS: a case study in Kemer, Turkey

Suitable climate conditions as well as rare natural and cultural resources in the Mediterranean region of Turkey have made it a centre of attraction for two conflicting interests: agricultural production and tourism activity. In recent years, the natural appeal of the area and economic interests have dominated tourism over agriculture, forestry and wildlife and led to significant urban sprawl. The objective of this study was to investigate the dimensions of the land cover/use conversion of a quiet, small village (Kemer) into an internationally popular touristic destination. In the scope of this study, land cover and land use changes were analysed over approximately 30 years using Landsat Multispectral Scanner (MSS) data (1975), and Landsat Thematic Mapper (TM) data (1987, 1995 and 2003) by image classification techniques. In the land use hierarchy, the Coordination of Information on the Environment (CORINE) methodology was used as a base. Data organization and collection stages were achieved in a geographical information system (GIS) environment. Finally, the results indicate that, from 1975 to 2003, permanent crops decreased by 75% and most of these areas were structured. Throughout the same years a 55% decrease was determined to arise in heterogeneous agricultural areas. From 1975 to 2003, there had been no serious change in forests. The main reason for this is the accommodation of the Olympos‐Bey Mountains national park in the region.

Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data

During the period 1973–1999, the effects of air pollution on the terrestrial ecosystems in the border areas of Norway and Russia have been investigated by the use of satellite remote sensing. Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data, in combination with field work data collected in the period 1970–2000, were applied to produce land cover maps for seven different years, with an overall accuracy of 75–83%. On the basis of this monitoring, we can conclude that the main effect of air pollution was that the areas of lichen ( Cladina spp.)-dominated forests and mountain heaths were reduced from 37% in 1973 to 10% in 1994, followed by a slight increase to 12% in 1999. The lichen-dominated vegetation types were changed into barrens, partly damaged vegetation entities, and dwarf shrub (e.g., Vaccinium myrtillus)-dominated vegetation. We found a significant negative relationship ( r=−0.94, p=0.001) between the extent of the area of mixed forests with lichen content and the emissions of SO 2 during the period. The area of the category industrial barrens had a significant negative relationship ( r=−0.95, p=0.001) to the emissions of SO 2 during the same period.

Remote sensing of agriculture change in Oman

This paper uses Landsat Multi-Spectral Scanner (MSS) data from 1978 and Landsat Enhanced Thematic Mapper (ETM) data from 2000 to assess agriculture change in two test areas of the Batinah coast of Oman. Field work was carried out in 1979 and 2001 in the Khaburah and Sohar regions to support the analysis of the Earth observation data. Unsupervised and supervised classifications are explored to produce maps of environmental change, and from these maps estimates of agriculture area are produced. While there has been substantial and clear agriculture change in the test areas, stimulated by Oman's rapid economic change since 1970, the measurement of these changes presents challenges that are common to many remote sensing investigations of arid and semi-arid regions. The paper identifies the main areas and types of agriculture change in the two test regions.

An approach for assessing boreal forest conditions based on combined use of satellite SAR and multi-spectral data

The degree and spatial distribution of boreal forest ecosystem degradation in Russia are not well known. The objective of this study is to develop an interpretation basis for analysis of satellite remote sensing data using a set of indicators characterizing the ecological situation and the degree of industrial pollution. European Remote Sensing Satellite (ERS) Synthetic Aperture Radar (SAR) and Landsat Multi-Spectral Scanner (MSS) data are used in combination for this purpose, along with an exceptionally extensive in situ data set of ground measurements of spectral radiance of pine biocenose components, and the results of moss chemistry and bio-indicator studies from the ecologically stressed St Petersburg region. It is shown that ERS SAR images provide an assessment of forested area distribution and forest type classification. The main factors of variability in parameters such as Normalized Difference Vegetation Index (NDVI) that are most strongly related to in situ indicators reflecting the state of the forest are identified. A supervised classification of forest degradation was performed on the basis of the NDVI values from the Landsat images. The results obtained make it possible to specify the areas at a local level and perform regional assessments. The potential for multi-temporal ERS SAR and multi-spectral sensor observations to trace the dynamics of changes in forest ecosystems is evaluated.

Estimation of cirrus and stratus cloud height using landsat data and 3D graphical representation

Cirrus and stratus cloud heights are estimated using LANDSAT MSS(MultiSpectral Scanner) and TM(Thematic mapper) data. Cloud heights are calculated from the sun elevation angle and the horizontal distance between cloud and its shadow templates. The matching of these templates is accomplished by the 2D cross-correlation function. Before accomplishing the matching, histogram equalization, inversion of shadow template, and Fourier Fast Transformation are applied to both templates. Then 2D cross-correlation function is calculated in the frequency domain. Correlation coefficients are 3D graphically displayed to check visually how sharply the templates match. The effects of the skew angle and the nonnadir observation angle are taken into consideration to modify the direction from the cloud template to its corresponding shadow template. The results show that this method is accurate within several pixels.

Analysis of variations in land cover between 1972 and 1990, Kassala Province, Eastern Sudan, using Landsat MSS data

The paper describes an analysis of vegetation and land cover changes between 1972 and 1990 in Kassala Province, Eastern Sudan. Three areas were selected and studied using Landsat Multi-Spectral Scanner (MSS) data. A maximum likelihood routine was used and sand land, grassland, forest, irrigated areas, kerrib, water, cultivated areas and mountain areas were classified. The study indicates increases in sand land, forest and cultivated areas, and a decrease in grassland.

An estimate of forest cover extent and change in Jamaica using Landsat MSS data

Landsat Multispectral Scanner (MSS) data were used to derive an island-wide estimate of forest extent and change for Jamaica for the period 1987-1992. The study demonstrates how Landsat MSS data provide an invaluable resource for monitoring tropical forest change when, as in this study, a simple classification of forest versus non-forest is made. Using a broad definition of forest cover and a hybrid unsupervised/supervised classification, the study derived an average per annum island-wide deforestation rate of 3.9% for this period. This estimate was then compared to existing estimates for the island and its reasonabless argued for in view of the latter's limitations and the study's own accuracy assessment. By providing a broad snapshot of the rate of forest loss on the island, it is hoped that this study's country-wide estimate will highlight the severity of the deforestation problem to scientists and policy-makers, and will provide the basis for more detailed forest classification studies of the island.

Acacia canopy cover changes in Rawashda forest reserve, Kassala Province, Eastern Sudan, using linear regression NDVI models

The paper aims to assess the acacia canopy cover changes for the period 1972-1990 in the Rawashda forest reserve, Eastern Sudan. A linear regression model was applied using Landsat Multi-Spectral Scanner (MSS) data from 1972, 1979 and 1990. The study found an increase in canopy cover from 1972 to 1990, with the larger and denser forest areas occurring in 1990.

Assessment of environmental impacts of river basin development on the riverine forests of eastern Kenya using multi-temporal satellite data

The utility of Landsat MSS (Multispectral Scanner) and SPOT XS data in monitoring the impacts of river basin development on a riverine forest located in the lower Tana River Basin of eastern Kenya was evaluated. Land cover change maps derived from Landsat MSS indicated little change in total forest area between 1975 and 1984. Land cover change maps derived from SPOT XS data indicated a 27% decline in forest area between 1989 and 1996. Mean patch size and area-perimeter ratio of the closed riverine forest remained virtually unchanged whereas these parameters for the open forest class decreased by 31% and 4% respectively. In addition, the average extent of the open riverine forest from the river channel declined by about 200 m between 1989 and 1996. This decline was attributed to decreased extent of floods along the floodplain following construction of dams in the upper river basin, and increased exploitation of the forests for fuelwood, especially in the vicinity of the established Bura Irrigation and Settlement Project. The greater lateral movement observed in the location of the river channel for the 1975-1985 period, compared to the 1985-1996 period, was also attributed to construction of dams in the upper river basin.

Landscape cover dynamics pattern in Meghalaya

Human interventions in natural systems have resulted in large changes in vegetation composition and distribution patterns. The Land Use Change and Climate Change (LUCC) study under the International Geosphere Biosphere Program (IGBP) is a major initiative in this regard. Changes in land use and hence in vegetation cover, due to climatic change and human activity, affect surface water and energy budgets directly through plant transpiration, surface albedo, emissivity and roughness. They also affect primary production and, therefore, the carbon cycle. Thus, there is a need for spatial and temporal characterization of vegetation cover at different scales, from the global and continental scale to the local patch scale. Satellite remote sensing provides detailed information regarding the spatial distribution and extent of land use changes in the landscape. Meghalaya, in north-east India, is one of the most important, biologically rich landscapes. Degradational activities, namely shifting cultivation, clear felling of forests for timber, and mining, have altered the natural landscape to a great extent. Because of these increased anthropogenic activities the natural landscape has been modified which has resulted in a fragmented landscape with poor species composition. These changes in the landscape were analysed using IRS 1A, 1B and Landsat Multi-Spectral Scanner (MSS) data during the period 1980-1995. The vegetation type maps were prepared by a visual interpretation technique in order to study the land cover dynamics pattern in Meghalaya.

Deforestation, Reforestation and Forest Fragmentation on the Upper Coastal Plain of South Carolina and Georgia

Abstract Rates of deforestation and reforestation were measured using Landsat Multispectral Scanner data for a 100-km by 100-km section of the Upper Coastal Plain of South Carolina and Georgia. Landsat data from 1974, 1979, 1984, 1988 and 1991 were (1) classified as pine forest, hardwood forest and nonforested areas and (2) compared across years to indicate forest cutting and regeneration. In 1974 hardwood forests occurred on 268,335 ha, or 27% of the area, and these forests were largely uncut in subsequent years. In 1974 pines occurred on 202,613 ha, or 20% of the area, but cutting rates of approximately 6600 ha y−1 reduced the area of this initial pine forest to 110,146 ha in 1988 and 82,795 ha in 1991. The rate of pine cutting on privately owned land was 4.0% y−1, which is greater than that observed for most tropical forests. Pine reforestation rates were 3200 ha y−1 and were too small to maintain the area in pine. The total pine area in 1988, including remnants of the 1974 forest and reforestation sin...

Multi-scale analysis of disturbance regimes in the northern Chihuahuan Desert

Remote sensing facilitates cross-scale validation, enables analysis of processes and patterns in time and space, and is thus viable for the conduct of earth system science. Multi-scale analyses of natural vegetation patterns and processes in the northern Chihuahuan Desert show that natural vegetation is capable of recovering from short-term, high intensity disturbances such as an atomic bomb blast. In contrast, mesquite dunelands persist on other sites grazed before the blast, showing the Chihuahuan is less resilient to long-term low intensity disturbances. A geographic information system (GIS) was used to register historical Landsat Multi-Spectral Scanner (MSS) data acquired over Trinity National Historic Site (TNHS), New Mexico, and the vicinity. Aerial and ground photography provide supporting detail, at finer scales, regarding the distribution and pattern of natural vegetation at TNHS Ground Zero and adjacent weapons impact targets. Aside from initial mechanical or thermal damage to vegetation from the first atomic test over a half century ago, analyses of vegetation at satellite and airphoto scales show no apparent persistent blast effects around TNHS.

NALC Land Cover Change Detection Pilot Study: Washington D.C. Area Experiments

As a part of North American Landscape Characterization (NALC) project, 75 change-detection techniques and variations were systematically tested and evaluated using both visual and statistical methods. These change-detection experiments were conducted in the Washington, D.C. region using a standard NALC Landsat Multispectral Scanner (MSS) data set. While more testing of the NALC data is needed in geographically diversified regions, our initial results suggest that the automated scattergram controlled regression normalized image differencing and normalized difference vegetation index differencing outperform most other change-detection techniques. These techniques are worthy of further testing and of large-scale applications for NALC data.

An analysis of land cover changes in the Northern Forest of New England using multitemporal Landsat MSS data

Landsat Multispectral Scanner (MSS) data were used to characterize land cover types, land cover changes, and landscape patterns in the Northern Forest region of Vermont, New Hampshire, and western Maine, USA. Eight Landsat MSS scenes from 1973, 1978, 1984/85, and 1990/91 were used to cover the regional study area and 18-year time period. Four watershed segments within the Northern Forest area were analysed: the Lamoille River watershed (151814 ha), the Connecticut River watershed (706880 ha), the Androscoggin River watershed (655445 ha), and the Kennebec River watershed (457168 ha). Multitemporal analysis indicates that the percentage of forested land that was converted to open land in each watershed over the 18-year period is similar, and is estimated to be approximately 10% of the forested land in each watershed segment.