Landsat Multispectral Scanner Research Articles

Analysis of methods for assessing the degree of water pollution

The most important modern methods for the monitoring of water bodies, their state of pollution and the assessment of water quality are considered. Commonly accepted, standard-ized methods for assessing water quality in water bodies include ground-based sampling, which is untimely and expensive under certain conditions. Best suited for practical use in large areas is the remote approach, which combines ground and satellite measurements to classify changes and detect pollution in closed water bodies. Satellite monitoring is a reliable tool for obtaining operational information on the state of water bodies and their changes with a certain periodicity, thanks to the use of a large set of heterogeneous data. And the availabil-ity of archived data becomes a significant advantage, opening up the possibility of comparing the results obtained and analyzing the factors influencing the state of water bodies and the environment. The paper describes the main methods and datasets that can be used for the ap-plication of remote monitoring methods based on machine learning. The use of Landsat mul-tispectral sensors, including TM (Thematic Mapper), MSS (Multispectral Scanner), ETM (Enhanced Thematic Mapper), and OLI (Operational Land Imager), is proposed as the main data set. Examples of the application of the remote sensing approach to the assessment of wa-ter pollution over large areas will be analyzed. Possible machine learning methods for solv-ing an applied problem are presented.

New techniques for old fires: Using deep learning to augment fire maps from the early satellite era

Disturbances such as fire play a critical role in forest ecosystems. However, anthropogenic fires can profoundly impact forests to the point of destabilizing ecosystems. In addition, fires have legacy effects on environments which may be observed in forests for decades after the fire is extinguished. Thus, understanding the extent of historic fires in a landscape is vital to understanding current forest structure and ecological processes (e.g., carbon sequestration capacity and provision of habitat) and, therefore, essential for informing land-management decisions. However, little work has been done to map forest fires pre 1980s due to the challenges of interpreting imagery from the 1970s-era Landsat Multispectral Scanner (MSS) platform. MSS imagery is distinguished from recent satellite missions through lower temporal, spatial, and spectral resolutions. Recent advances in image processing have brought the goal of high-quality MSS classifications within reach. In this study, we use deep learning, specifically UNet (a fully convolutional neural network (CNN)), to detect historic forest fires in MSS imagery for the forest-dominated regions of Quebec, Canada. While other studies have applied deep learning to present-day satellite data for land cover classification, hardly any work has specifically applied deep learning to MSS data for fire detection. We trained our UNet model on 206 MSS images that were labelled by applying thresholds to the Burned Area Index inside polygons drawn by the authors around burned areas. We then used the trained model to label burns in 5104 MSS images that were compiled to generate annual burned area maps. Our results identified (with a 95% confidence interval) 3503.95 ± 484.90 km2 of burns not previously reported in any database; this represents a 35.30 ± 3.94% increase in the total known burned area across the forest-dominated regions of Quebec between 1973 and 1982.

Detection and Delineation of Agricultural Land Losses in Minna, Niger State, Nigeria

This study assessed the losses in agricultural lands and enrichment of other land usages in agricultural area in Minna and environs in the state capital of Niger state, Nigeria. Data captured using Landsat Multispectral Scanner (MSS) (1990), Thematic Mapper (TM) (2000-2010) and Enhanced Thematic Mapper plus (ETM+) (2000, 2010 and 2020) used to quantify LULC changes. A post-classification matrix prepared to assess the modification and conversion in LULC from 1990 to 2020. The significant modification in LULC observed as agricultural lands and vegetation underwent noticeable decline by 714.39km2 and 578.94km2, respectively whereas built up area increased substantially to a value of 96.91km2. About 12.29km2 agricultural land lost to barren land and 12.83km2 converted to water bodies. Finally, the encroachments of vegetation and built up area in agricultural area caused substantial decreased the available land for agricultural activities. Remote sensing data provide useful for estimations of agricultural land loss on a regional scale.

Urban space change and future prediction of Kanpur Nagar, Uttar Pradesh using EO data

Urban Land use changes, measurements, and the analysis of rate trends of growth would help in resources management and planning, etc. In this study, we analyze the urban change dynamics using a support vector machine model. This method derives the urban and rural land-use change and various components, such as population growth, built-up areas, and other utilities. Urban growth increases rapidly due to exponential growth of population, industrial growth, etc. The population growth also affects the availability of various purposes in its spatial distribution. In this present study, we carried out using multi-temporal satellite remote sensing data Landsat MSS (Multispectral scanner), ETM+ (Enhanced thematic mapper), OLI (Operational land imager) for the analysis of urban change dynamics between years 1980-1990, 1990-2003, 2012-2020 in Kanpur Nagar city in the state of Uttar Pradesh in India. In our study, we used SVM (Support Vector Machine) Model to analyze the urban change dynamics. A support vector machine classification technique was applied to generate the LULC maps using Landsat images of the years 1980, 1990, 2003, and 2020. Envi and ArcGIS software had used to identify the land cover changes and the applying urban simulation model (CA- Markov model) in Idrisi selva edition 17.0 software. The LULC maps of 2003 and 2020 were used to simulate the LULC projected map for 2050 using (Cellular automata) CA- Markov based simulation model.

Four decadal urban land degradation in Pakistan a case study of capital city islamabad during 1979–2019

The study of Land Use Land Cover (LULC) changes and their patterns enhance our understanding about various man caused phenomenon on earth surface. The Capital City of Pakistan, Islamabad has expereinced high rate of urbanization and population increase in the last 3-4 decades and as a result, the LULC changes have occurred in an unsustainable way. The accurate and reliable data of LULC changes for the study period is not available. The main aim of this study was to provide compatible data on LULC change during the last 40 years for the functional city Islamabad, Pakistan. LULC statistics were extracted from Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) images. For each of the target years (1979, 1989, 1999, 2009 and 2019) LULC information was obtained through a supervised classification algorithm. The study area was classified into five major landscape categories, namely built-up, agriculture, forest, water bodies and barren land. The results have indicated that the urban expansion has been increasing due to mass migration and uncontrolled urbanization in the city. The urban sprawl analysis revealed that built-up area and agricultural land has expanded by 41.7% and 5.20% respectively, during 1979–2019. On the other hand, the forest and water bodies declined by 9.03% and 1.21% respectively. The land use changes, have forced the planners to revise the original master plan of the city and including the agricultural sector in the housing area during 2019.

Decadal-scale spatiotemporal changes in land use/land cover of El Potosi Basin at semi-arid northeast Mexico and evolution of peat fire between 1980-2020 CE

We estimated decadal-scale changes to the land use/land cover in one of the many drought-prone agricultural basins (El Potosi Basin) of northeast Mexico by using the temporal satellite images of Landsat Multi spectral scanner (MSS), Thematic Mapper (TM) and Operational Land Imager (OLI) for 1980, 1990, 2000, 2010 and 2020 CE. Increase in land without shrub and permanent uncultivated (fallow) land, and reduction in coverages of the agricultural land and forest over the last four decades reflected the intensities of water scarcity caused from global warming as well as the agriculture related anthropogenic activities. Enhanced aridity led to 136-fold surge (ca. 2.5–340 km2) in abandonment of cultivated areas covering almost half of the basin. Absence of cropland in 2010 CE reflected the severe drought of early 21st Century but augmentation in usages of groundwater reestablished the agriculture a decade later in ca.15.7 km2. Over the last four decades, the subsurface fire from combustion of peat covered ca.17.4 km2 mostly at expenses of lands with and without shrub and agriculture land. Our temporal and spatial land use/land cover assessments might be helpful for private and government agencies to evaluate the short-term impacts of climate change and anthropogenic activities in order to mitigate their effects in this water scarce region.

Spatio-temporal analysis of urban sprawl in Islamabad, Pakistan during 1979–2019, using remote sensing

Urbanization in Pakistan is increasing at 3% annually, the highest in South Asia. 50% of the population is expected to urbanize by 2025. The capital city of Pakistan, Islamabad has experienced phenomenal increase in the urban population and extent in the last four decades. The aim of this research was to analyze the urban sprawl of Islamabad and changes in the Land Use and Land Cover (LULC), with the help of satellite images. LULC statics were extracted from Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) images, for the years 1979, 1989, 1999, 2008 and 2019. There is an increase of 377 sq. km in built-up areas and slight increase of 47 sq km in the agriculture land. The forest cover has been reduced by 83 sq km and the water bodies have also reduced. The barren land has experienced an unprecedented decrease of 333 sq km in the meanwhile. The master plan is under revision to regularize such uncontrolled constructions and accommodate the increased demand. The city, as a result, has been facing issues of water supply, sanitation, transportation etc. This has seriously jeopardized the master plan of the city.

Landsat-based lake distribution and changes in western Alaska permafrost regions between the 1970s and 2010s

Lakes are an important ecosystem component and geomorphological agent in northern high latitudes and it is important to understand how lake initiation, expansion and drainage may change as high latitudes continue to warm. In this study, we utilized Landsat Multispectral Scanner System images from the 1970s (1972, 1974, and 1975) and Operational Land Imager images from the 2010s (2013, 2014, and 2015) to assess broad-scale distribution and changes of lakes larger than 1 ha across the four permafrost zones (continuous, discontinuous, sporadic, and isolated extent) in western Alaska. Across our 68 000 km2 study area, we saw a decline in overall lake coverage across all permafrost zones with the exception of the sporadic permafrost zone. In the continuous permafrost zone lake area declined by −6.7% (−65.3 km2), in the discontinuous permafrost zone by −1.6% (−55.0 km2), in the isolated permafrost zone by −6.9% (−31.5 km2) while lake cover increased by 2.7% (117.2 km2) in the sporadic permafrost zone. Overall, we observed a net drainage of lakes larger than 10 ha in the study region. Partial drainage of these medium to large lakes created an increase in the area covered by small water bodies <10 ha, in the form of remnant lakes and ponds by 7.1% (12.6 km2) in continuous permafrost, 2.5% (15.5 km2) in discontinuous permafrost, 14.4% (74.6 km2) in sporadic permafrost, and 10.4% (17.2 km2) in isolated permafrost. In general, our observations indicate that lake expansion and drainage in western Alaska are occurring in parallel. As the climate continues to warm and permafrost continues to thaw, we expect an increase in the number of drainage events in this region leading to the formation of higher numbers of small remnant lakes.

Interplay of Climate Variability, Land Use Change, Crop Yield and Farm Households Food Security in Offin River Basin, Ghana

Climate variability and adapted land use systems have altered climate systems. Their impact on agro-ecosystems adversely affects crop yield, food security and livelihoods of farm households. This paper examines the coupled effects of climate variability and land use change on crop yield and food security among farm households in the Offin River Basin. The study used remote sensing, geographic information systems, time series climate and crop yield data and farm household survey in examining the impacts of climate variability and land use change on crop yield and farm household food security. Landsat Multi-Spectral Scanner (MSS), Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper plus (ETM+) were used to assess land use and land cover change and agricultural drought conditions in the area. Spline interpolation techniques and Spearmans’ rank correlation were employed to assess the trend and spatial distribution of rainfall and temperatures in the basin. Multiple linear regression model (MLRM) and Pearson correlation were used to assess the effect of rainfall and temperature on major food crop yields. Household Food Balance Model (HFBM) was used to measure food security status at farm household level. The results revealed that climate variability coupled with adapted land use systems in Offin River basin have contributed to massive deforestation, accelerated soil degradation and recurrent agricultural droughts resulting in the loss of productive farmlands and low food crop yields. Household food balance model (HFBM) analysis revealed that 60.8% of the farm households in the basin could not meet the minimum recommended energy level of 2900 kilo calories intake per person per day while 39.2% of farm households were food secured subsisting on more than recommended kilo calories per person per day. Utilization of inland valleys, food crop management practices, soil and water conservation and management technologies, mixed cropping systems and crop diversification were found to have contributed positively and significantly to farm household food security. It is recommended that Government agencies such as MoFA, GASIP and existing Non-Governmental Organizations (NGOs) should increase investment in soil and water conservation strategies (SWCS) and inland valleys food production systems to enhance food production and food security systems among farm households.

Modern glacier dynamics in Mt. Tsambagarav in the Mongolian Altai Mountain Range

This study presents the modern glacier dynamics in Mt. Tsambagarav in the Mongolian Altai Mountain Range over the last four decades. This is the first review of this type of glacier dynamics for this massif. Changes in glacier area in Mt. Tsambagarav are estimated using normalized indexes (Normalized Difference Snow Index and Normalized Difference Principal Component Snow Index). Spatial distribution of the modern glaciers delineated with Landsat Multispectral Scanner (MSS: resolution of 80 m), Landsat Thematic Mapper (TM: resolution of 30 m) and Landsat Operational Land Imager (OLI: resolution of 30 m) imageries. Result shows that Mt. Tsambagarav has lost 51.7% of the glacier area from 132.24 km2 in 1977 to 63.92 km2 in 2017. The loss in glacier area for Mt. Tsambagarav during the last 40 years reflect the rapid response of the modern glacier to climate change, i.e., it is highly sensitive to solar insolation and/or rapidly rising local and regional mean annual temperatures. The remote sensing data and field survey suggest that the modern glaciers would be disappeared on a scale of decades. Rapid melting of the glacier in this massif contributes to surface water resources in western Mongolia. This study demonstrates the importance of spatial analysis in the remote area for understanding the context of changes in the modern glaciers.

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.

Recessional pattern and surface elevation change of the Parvati Glacier, North-Western Himalaya (1965-2018) using remote sensing

ABSTRACT We present a long-term assessment (1965–2018) of recessional and area change patterns as well as the recent decade surface velocity (, 2000–2001 and 2017–2018) and elevation change (, 2003–2016) of the Parvati Glacier, north-western Himalaya. Multi-temporal high (Corona, Hexagon, and PlanetScope) to medium (Land Remote-Sensing Satellite (System, Landsat) Thematic Mapper (TM)/Enhanced Thematic Mapper+ (ETM+)/Operational Land Imager (OLI), Sentinel 2A- Multispectral Instrument (MSI), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) resolution images from 1965 to 2018 were analysed to evaluate multi-parameters of glacier dynamics. We estimated an average annual recessional rate of 37.5 ± 0.5 m and total area loss of 2.2 ± 0.01 km2 for the Parvati Glacier over the past half-century (i.e. 1965–2018). During 1973–1989, the Parvati Glacier experienced a significant recession (70.9 ± 3.1 m a−1) that resultant into disintegrated of the glacier in two separate glaciers between 1981 and 1989. We observed a higher average recession rate (28.4 ± 1.6 m a−1) of the detached glacier after its disintegrated from the Parvati Glacier (18.7 ± 1.6 m a−1) between 1989 and 2018. However, the debris-covered ice area of the Parvati Glacier (about 12%) increased twice that of the detached glacier (about 6.1%) during 1994–2018. The estimated reveals a substantial slowdown by about 27.7% and about 20% for the Parvati and detached glacier, respectively, during 2000–2001 and 2017–2018. Overall the annual average surface lowering rate (−0.7 ± 0.5 m a−1) for both the glacier has been almost similar during 2003–2016 but varied significantly at altitudinal. Our study shows inconsistency in previously reported recessional rate and observed advance phases in 1976–1989 and 2009–2013 for the Parvati Glacier during almost the same duration of the study. It is mainly ascribed to the misinterpretation of glacier boundaries using the Survey of India (SoI) topographic map of 1962 where glacier boundaries more generalized and by using coarser resolution of Landsat Multispectral Scanner (MSS) images (1970s), particularly for the debris-covered area. Overall, the Parvati Glacier shows a significant recession and frontal area change; and in the recent decades, extensive surface lowering, along with an observable slowdown in glacier and appearance of ice-cliffs in the ablation area collectively point towards substantial glacier degeneration over the period of our analysis.

REMOTE SENSING AND MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE ESTIMATION OF URBAN EXPANSION (CASE OF DARKHAN CITY, MONGOLIA)

Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI &amp;amp; TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.

Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia

Permanent conversion of woodland to large-scale commercial agriculture, pastures or urban areas and temporary or partial removal of indigenous trees for shifting cultivation and selective logging remained major environmental challenges in the tropical region. Cognizant of the environmental changes prevailing in the pastoral and agro-pastoral areas of Southern Ethiopia, we have examined the past conversion of woodland to other land uses through the analysis of Landsat Multi-spectral scanner (MSS) 1973, Thematic Mapper(TM) 1986, Enhanced Thematic Mapper (ETM+) 2003, Operational Land Imagery (OLI) 2017 and then projected the future change in land use/cover (LUC) as well. We have employed Cellular Automata Markov chain model to simulate and predict LUC changes between 2017 and 2060. Four spatial driver variables such as distance to road and settlement, slope and elevation were used to run the simulation. Prior to the prediction, we have simulated the LUC of 2017 using transition potential maps of 2003 and transition matrix between 1973 and 2003. The predictive power of the model was then examined by comparing the reference and simulated LUC maps of 2017and also using the kappa index. A good correlation was obtained between the reference and simulated LUC maps of 2017. In addition, the computed kappa index was above 0.9, which implies that the model is effective in predicting change in LUC. The analysis result revealed that in the entire monitoring period (1973–2017) the area lost 89,875 ha of woodland. The loss is expected to continue during the period 2017–2060, with an estimated loss of 32,423 ha of woodland, if a proper measure is not taken against the continuous loss of woodland. Thus, relentless efforts are needed to rehabilitate the already degraded land and also minimize the potential loss of woodland in the future through the implementation of conservation – livelihood approach, REDD + project, and sustainable land use management strategies.

A Multitemporal Remote Sensing Image Registration Method Based on Water Bodies for the Lake-Rich Region

Accurate multitemporal remotely sensed image registration is essential for water body change detection and analysis of lake dynamics. However, lake-rich regions, such as Siberia, are mostly dominated by rivers and lakes, with few stable geometry features such as those typically used as control points for image registration. Lakes in Arctic regions are generally not static; their shorelines tend to expand and shrink seasonally, and may change substantially between different years, making it difficult to find consistent features for image registration. Consequently, traditional image-to-image registration methods, and even many sophisticated registration algorithms, rarely achieve accurate geometric correction in Arctic regions due to a lack of sufficient control points. In this article, we proposed a summary lake spatial attribute, the inferred deepest point of the lake (DPL), as a feature that is relatively insensitive to lake area changes, and therefore useful for registration of multitemporal images. The central focus of the DPL estimation algorithm is the identification of the largest inner circle (LIC) of the lake polygon. First, the internal Voronoi diagram of a lake polygon is calculated by the ``divide-and-conquer” method. The medial axis (MA) is then calculated by Voronoi diagram simplification, and finally, the LIC center is obtained by computing the distance from the MA intersection to all polygon edges. The approach was used to register HJ-1 and Landsat multispectral scanner (MSS) images in Siberia, where water bodies dominate the landscape and change significantly over time. The proposed method found a large number of control points from the extracted water bodies. Subpixel registration accuracy of 0.62 pixels (18.5 m) and 0.33 pixels (19.6 m) root mean square error (RMSE) was obtained for the HJ-1 and MSS images, respectively. In comparison, the alternative method of using lake centroids, only achieved 0.75 pixels (22.4 m) and 0.49 pixels (29.3 m) RMSE. This registration accuracy improvement is potentially important for large-scale regional cartography and change detection applications.

Landsats 1–5 Multispectral Scanner System Sensors Radiometric Calibration Update

First launched in 1972, the Landsat satellite sensors have provided the longest continuous record of high-quality images of the earth’s surface that are used in both civilian and military applications. The Landsat Multispectral Scanner System (MSS) sensor was onboard Landsat-1 through Landsat-5. In fact, the MSS sensors provide the only systematic global multispectral space-based imagery of the earth’s surface from 1972 to 1982. This paper focuses on the radiometric calibration update of the Landsats 1–5 MSS sensors. The radiometric calibration was performed in both radiance- and reflectance-based scales through the cross-calibration approach. Simultaneous or near-simultaneous image collections were available for MSS sensor pairs and used for the cross-calibration. The estimated uncertainties for this calibration update exhibit progressively decreasing calibration accuracy, ranging from 5.1% for MSS-5 to 8.8% for MSS-1 in the green spectral band, for example. Finally, the new radiometric calibration coefficients were validated through the use of pseudo-invariant calibration sites (PICS). The temporal Top of the Atmosphere (TOA) radiance and TOA reflectance over the Sonoran Desert were plotted with the purpose of verifying the lifetime radiometric stability of MSS sensors. With the previous calibration, the agreement between the measurements of TOA reflectance over the Sonoran Desert was around 7.5% for all spectral bands. With the calibration update implemented in this study, the agreement between MSS sensors is 3.6%, 2.9%, 3.5%, and 5.9% for the green, red, near-infrared response (NIR-1), and NIR-2 spectral bands, respectively. This study ties all the Landsat legacy instruments from Landsat-1 MSS through Landsat-8 OLI to a consistent radiometric scale.

A relict habitat became an isolated habitat in the Luoshan Nature Reserve from 1977 to 2017: Natural or man‐made disaster?

Understanding the ecological trends of the relict habitats in the Luoshan Nature Reserve (LNR) has an important role in the persistence of endangered species and the socio‐economic sustainability in the Ningxia section of the arid desertification belt in North China. Based on data from the Landsat Multispectral Scanner (MSS)/Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) sensors (from 1977 to 2017), the MODIS normalized difference vegetation index (NDVI) and net primary productivity (NPP) (from 2000 to 2017), the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), annual average air temperature and precipitation (from 1977 to 2017), typical anthropogenic factors (population size, cultivated land and residential area) and natural ecological elements (MSS/TM/ETM+/OLI/MODIS NDVI and NPP) were quantitatively contrasted and analyzed for potential correlations. Some indicators of human activities, ecological processes and landscape geography have changed in the LNR. The proportion of residential area increased 3.09‐fold from 1977 to 2017. The population increased 34.30‐fold due to ecological immigration policy implementation in the Hongsipu immigration district. Before immigration, cultivated land increased, but after immigration, this land decreased quickly. Most of the dry‐cultivated land was converted through afforestation and a small part of the land was converted to irrigate cultivation land. We conclude that natural evolution led to the ecological relics, the isolated management to the LNR has objectively created an ecological island that will accelerate inbreeding depression and reduce biodiversity.

Remote sensing and geographic information systems techniques in studies on treeline ecotone dynamics

We performed a meta-analysis on over 100 studies applying remote sensing (RS) and geographic information systems (GIS) to understand treeline dynamics. A literature search was performed in multiple online databases, including Web of Knowledge (Thomson Reuters), Scopus (Elsevier), BASE (Bielefeld Academic Search Engine), CAB Direct, and Google Scholar using treeline-related queries. We found that RS and GIS use has steadily increased in treeline studies since 2000. Spatial-resolution RS and satellite imaging techniques varied from low-resolution MODIS, moderate-resolution Landsat, to high-resolution WorldView and aerial orthophotos. Most papers published in the 1990s used low to moderate resolution sensors such as Landsat Multispectral Scanner and Thematic Mapper, or SPOT PAN (Panchromatic) and MX (Multispectral) RS images. Subsequently, we observed a rise in high-resolution satellite sensors such as ALOS, GeoEye, IKONOS, and WorldView for mapping current and potential treelines. Furthermore, we noticed a shift in emphasis of treeline studies over time: earlier reports focused on mapping treeline positions, whereas RS and GIS are now used to determine the factors that control treeline variation.

Spatial and Temporal Variability of Open-Ocean Barrier Islands along the Indus Delta Region

Barrier islands (BIs) have been designated as the first line of defense for coastal human assets against rising sea level. Global mean sea level may rise from 0.21 to 0.83 m by the end of 21st century as predicted by the Intergovernmental Panel on Climate Change (IPCC). Although the Indus Delta covers an area of 41,440 km² surrounded by a chain of BIs, this may result in an encroachment area of 3750 km2 in Indus Delta with each 1 m rise of sea level. This study has used a long-term (1976 to 2017) satellite data record to study the development, movement and dynamics of BIs located along the Indus Delta. For this purpose, imagery from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) sensors was used. From all these sensors, the Near Infrared (NIR) band (0.7–0.9 µm) was used for the delineation and extraction of the boundaries of 18 BIs. It was found that the area and magnitude of these BIs is so dynamic, and their movement is so great that changes in their positions and land areas have continuously been changing. Among these BIs, 38% were found to be vulnerable to oceanic factors, 37% were found to be partially vulnerable, 17% remained partially sustainable, and only 8% of these BIs sustained against the ocean controlling factors. The dramatic gain and loss in area of BIs is due to variant sediment budget transportation through number of floods in the Indus Delta and sea-level rise. Coastal protection and management along the Indus Delta should be adopted to defend against the erosive action of the ocean.