Multi-temporal Landsat Satellite Images Research Articles

Identification of Pine Wilt Disease-Infested Stands Based on Single- and Multi-Temporal Medium-Resolution Satellite Data

Pine wilt disease (PWD) is known for its high lethality and rapid transmission, earning it the name “cancer of the pine tree”. The prompt removal of infested pine trees is an effective measure for preventing and controlling pine wilt disease. Accurate and efficient monitoring technologies are crucial for the scientific prevention and control of this plant disease. Currently, numerous remote sensing monitoring studies have been conducted on pine wilt disease. However, there is limited research on the temporal identification of PWD-infested forest stands over large areas. To build classification models, this study utilized three machine learning algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). We aimed to investigate the effectiveness of single-temporal and multi-temporal Landsat and Sentinel-2 satellite images PWD-infested forest stands detection. The results indicated that, at a spatial resolution of 30 m, Landsat-9 and Sentinel-2 remote sensing images effectively identified PWD-infested forest stands, with classification accuracies of 77.87% and 78.91%, respectively. Higher spatial resolutions in Sentinel-2 remote sensing images were associated with improved identification capabilities. Furthermore, multi-temporal Landsat satellite data (with a classification accuracy of 85.95%) significantly enhanced the performance of the monitoring model compared to single-temporal Landsat satellite data (with a classification accuracy of 77.87%). The RGI difference was found to be the optimal vegetation index. In conclusion, by combining multi-temporal and single-time-phase Landsat remote sensing data, a monitoring model for PWD-infested forest stands was constructed. It achieved a classification accuracy of 88.26%. In this study, a higher accuracy in identifying pine wilt disease and a lower economic cost were achieved by Landsat and Sentinel images, offering valuable insights for the management of pine wilt disease.

Spatiotemporal landuse dynamics driven by socioeconomic context in the southwestern coastal districts of Bangladesh

Socio-economic aspects and the increasing saltwater intrusion toward the southwestern coastal districts of Bangladesh are the main driving force for the alteration of landuse landcover (LULC) pattern. The present study employed a supervised classification approach using the maximum likelihood algorithm method in the geospatial environment to quantify the spatiotemporal LULC changes in the southwestern coastal districts of Bangladesh. The study used multi-temporal Landsat satellite images and identified the influencing socio-economic factors to find the relation with LULC changes. The study has seen a significant relationship between socioeconomic factors and landuse dynamics. Nearly 1500 km2 area has been converted to shrimp farming in almost all districts previously used for rice cultivation or uncultivated. It is actually due to the profitable shrimp production and saline water ingress toward the inland area that induced people to alter the landuse pattern. From 2010 to 2019, shrimp production increased significantly in the study area. The output of the study could be beneficial to policymakers for implementing the landuse policy and sustainable landuse management focusing on the aquaculture industry in this area.

Land Change in Tirthan Valley: The Case of Great Himalayan National Park Conservation Area

Humans are dominant agent of landscape change. Off lately, anthropogenic induced changes have visibly degraded environment around the world putting a serious question over the sustainability of both natural and man-made ecosystems. The sustainability of the ecosystems has found to be directly linked to changes in LULC. The present study aims to analyse long-term land use change in the Tirthan Valley of Himachal Pradesh; the region is a part of protected area collectively known as GHNPCA. With the help of multi-temporal satellite images of LANDSAT, this study reconstructs the trajectory of land changes over a period of five decades. The data shows an increase in forest cover (7.29%), rocky/barren land (45.35%), agriculture/horticulture land (28.94%) and settlement/ built-up (190.48%) while decrease in pasture/open land (29.97%), snow cover (56.14%) and river/water body (54.52%). The results have shown significant land changes in post protected area status.

Monitoring of Surface Water bodies in Mainpuri District Using Remote Sensing and GIS Technology

Abstract: These days, a range of analytical tasks are carried out using popular, computer-dependent, time-oriented methodologies such as remote sensing and GIS (Geographic Information System) technology. Remote sensing technology provides a synoptic perspective that aids in mapping the dynamics of surface water bodies at regional and global scales. With this study, we can gather information on seasonal changes over time in the Mainpuri District because variations in the distribution of surface water have an impact on the hydrogeology and physical surroundings of the area. Mainpuri District has a Ramsar site named Saman Bird Sanctuary, which is home to a significant number of migratory and local migratory birds, primarily Sarus cranes. The research demonstrated that the area's abundance of waterbodies benefits biodiversity by giving migratory birds enough food and shelter. Shrinkage and expansion in surface water bodies are useful for determining the ecology of the region. In the current work, pre-monsoon and post-monsoon multi-temporal Landsat satellite images have been used to monitor spatiotemporal changes in the water bodies’ surface area in the Mainpuri District of Uttar Pradesh, India. In the north-west to southeast of the district, waterbodies' temporal changes show a 60% sharp decline in the number of waterbodies from 1992 to 2022.

Coastal landscape classification using convolutional neural network and remote sensing data in Vietnam

The length of global coastline is about 356 thousand kilometers with various dynamic natural and anthropogenic. Although the number of studies on coastal landscape categorization has been increasing, it is still difficult to distinguish precisely them because the used methods commonly are traditional qualitative ones. With the leverage of remote sensing data and GIS tools, it helps categorize and identify a variety of features on land and water based on multi-source data. The aim of study is using different natural – social profile data obtained from ALOS, NOAA, and multi-temporal Landsat satellite images as input data of the convolutional-neural-network (CvNet) models for coastal landscape classification. Studies used 900 cut-line samples which represent coastal landscapes in Vietnam for training and optimizing CvNet models. As a result, nine coastal landscapes were identified including: deltas, alluvial, mature and young sand dunes, cliff, lagoon, tectonic, karst, and transitional landscapes. Three CvNet models using three different optimizer types classified the landscapes of other 1150 cut-lines in Vietnam with the accuracies about 98% and low loss function value. Excepting dalmatian, karst and delta coastal landscapes, five others distribute heterogeneous along the coasts in Vietnam. Therefore, the evaluation of additional natural components is necessary and CvNet model have ability to update new landscape types in variety of tropical nation as a step toward coastal landscape classification at both national and global scales.

Impact of Iran’s Forest Nationalization Law on Forest Cover Changes over Six Decades: A Case Study of a Zagros Sparse Coppice Oak Forest

Forest nationalization policies in developing countries have often led to a reduction in local forest ownership rights and short- or long-term exploitative behaviors of stakeholders. The purpose of this research is to quantify the effect of Iran's Forest Nationalization Law (FNL) in a part of Zagros Forest over a 68-year time period (1955-2022) using 1955 historical aerial photos, 1968 Corona spy satellite photography, and classification of multi-temporal Landsat satellite images. A past classification change detection technique was used to identify the extent and the pattern of land use changes in time. For this purpose, six periods were defined, to cover the time before and after the implementation of FNL. A 0.27% deforestation trend was identified over the period after the FNL. Dense and open forested area has decreased from 7175.62 ha and 68,927.46 ha in 1955 to 5664.26 ha and 59,223.38 ha in 2022. The FNL brought decisive changes in the legal and forest management systems at the state level, mainly by giving their ownership to the state. Accordingly, the FNL and the related conservation plans have not fully succeeded in protecting, rehabilitating, recovering, and developing the sparse Zagros Forest ecosystems, as their most important goals.

СКОРОСТЬ НАСТУПЛЕНИЯ БОЛОТНЫХ ГЕОСИСТЕМ НА СОСНЯК БЕЛОМОШНИК (НА ПРИМЕРЕ БОЛЬШОГО КАМСКОГО БОЛОТА, ПРЕДУРАЛЬЕ)

The dynamics of raised bogs in the north of the Kama region were studied using multi-temporal Landsat satellite images with stable snow cover. The use of winter images was determined by the need to exclude the factor of the underlying surface (soil cover, grass and subshrub vegetation) from the spectral image and to fix changes only in the tree layer of pine forests. Manual (expert) classification of the multitemporal composite was carried out according to spectral features. The displacement of the boundaries of bog phytocenoses deep into the first terrace is observed at an average distance of 75–85 m, which corresponds to the rate of displacement of critical conditions for pine life (formation of dead wood) – about 2 m/year. In the course of field works on test sites, the extent of tree vegetation inhibition and the intensity of the bog’s advance on adjacent geosystems were recorded. In the summer season of 2022, there was conducted a study of the composition of phytocenoses in the lower layer of vegetation in the border zone of the southern ‘shore’ of the Bolshoye Kamskoye Bog. The change of pine-cotton grass-subshrub-sphagnum phytocenosis to pine-subshrub-moss phytocenosis was recorded at a distance of 135–165 m when the bog invaded the forest geosystems. The main changes were recorded in the lower tier of vegetation. It is assumed that the change in the lower tier in time overtakes the process of the forest stand degradation, but in terms of the rate of boundaries displacement in space, it shows almost the same values – about 2 m/year.

Detecting land use and land cover change for a 28-year period using multi-temporal Landsat satellite images in the Jukskei River catchment, Gauteng, South Africa

The Jukskei River catchment is one of the urban catchments in the central part of Gauteng province covering a large part of City of Johannesburg Metropolitan Municipality and small part of Tshwane and Ekurhuleni Metropolitan Municipalities that have witnessed tremendous land use/land cover (LULC) change over time. Jukskei River catchment is one of the fastest growing catchments in terms of population and change in LULC over time. Therefore, it is very important to detect the nature and extent of these changes in order to identify the direction and future expansion of LULC within the catchment area. To accomplish that, multi-temporal satellite remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) 1987, Landsat-5 Thematic Mapper (TM) 2001 and Landsat-8 Operational Land imager (OLI) 2015 were used to detect LULC change in Jukskei River catchment area. The Jukskei River catchment was classified into four major LULC classes including: Built-up area, bare surface, sparse vegetation and intact vegetation. The analysis of the results revealed that for the past 28 years (i.e., 1987-2015), built-up and bare surface areas have increased by 56.2% (42713.1 ha) and 8,2% (6225.1 ha) while intact and sparse vegetation have decreased by 9.8% (7455.0 ha) and 25.8% (19659.6 ha), respectively. The overall accuracies for 1987, 2001, and 2015, were 85.9%, 87.5%, and 92.5% respectively, with Kappa Index of Agreement (KIA) of 81.3%, 83.3%, and 90% which indicates the accuracy of classified images with the reference images. The results revealed by this study can be used for decision-making activities and policy development regarding land use management within the catchment.

Coastal and landuse changes of Burullus Lake, Egypt: A comparison using Landsat and Sentinel-2 satellite images

Monitoring water bodies such as lakes is expensive, but remote sensing made it cost-effective. The extensive anthropogenic activities negatively impact the water lakes everywhere. Moreover, the Egyptian coastal lakes are not an exception. This paper investigates changes in land use/land cover and detects coastal changes of Buullus Lake, ranked to be the second-largest Egyptian northern Mediterranean coastal lake, using Remote Sensing and Geographic Information Systems (GIS) techniques. Multitemporal Landsat satellite images from 1984 to 2019 have been collected and processed where Landsat and Sentinel-2A images for 2019 have been used for comparison purposes. In addition field visits have been conducted to verify the obtained results. The maximum likelihood supervised classification approach was shown to be more accurate in monitoring changes in the Lake's water bodies. Classes of the supervised classification included eight classes; seawater, lake water, floating vegetation, sand, urban, agricultural areas, fish farms, and drainage canals. Analysis of the satellite data indicated that: between 1984 and 2019, the lake's water bodies and floating vegetation area were reduced by 16 % and 52 %, respectively. While agricultural lands increased by (648 km2). In addition, there has been a significant growth in the area of fish farms (290 km2). The resulted updated spatiotemporal maps and extracted water features assist decision-makers in taking the necessary steps to stop or minimize anthropogenic activities. These steps will help to restore or maintain the lake to host the government planned activities to achieve the Sustainable Development Goals (SDGs) on the lake level.

Crop type detection using an object-based classification method and multi-temporal Landsat satellite images

Crop type detection using an object-based classification method and multi-temporal Landsat satellite images

Analysing Process and Probability of Built-Up Expansion Using Machine Learning and Fuzzy Logic in English Bazar, West Bengal

The study sought to investigate the process of built-up expansion and the probability of built-up expansion in the English Bazar Block of West Bengal, India, using multitemporal Landsat satellite images and an integrated machine learning algorithm and fuzzy logic model. The land use and land cover (LULC) classification were prepared using a support vector machine (SVM) classifier for 2001, 2011, and 2021. The landscape fragmentation technique using the landscape fragmentation tool (extension for ArcGIS software) and frequency approach were proposed to model the process of built-up expansion. To create the built-up expansion probability model, the dominance, diversity, and connectivity index of the built-up areas for each year were created and then integrated with fuzzy logic. The results showed that, during 2001–2021, the built-up areas increased by 21.67%, while vegetation and water bodies decreased by 9.28 and 4.63%, respectively. The accuracy of the LULC maps for 2001, 2011, and 2021 was 90.05, 93.67, and 96.24%, respectively. According to the built-up expansion model, 9.62% of the new built-up areas was created in recent decades. The built-up expansion probability model predicted that 21.46% of regions would be converted into built-up areas. This study will assist decision-makers in proposing management strategies for systematic urban growth that do not damage the environment.

Analysis and assessment of the use of chernozems of the Kungur forest-steppe based on remote sensing data

Based on the combined analysis of soil maps and multi-temporal satellite images of Landsat–5 and Sentinel–2, an analysis of land use within the areas of chernozem soils of the Kungur forest-steppe was carried out. The boundaries of modern lands are established: arable land, meadows and pastures without subdivision, forests and fallow lands. The assessment of changes in the structure of land use on chernozem soils over the past 30 years is given. The current trend consists in a general reduction in the area of agricultural land used, including the area of arable land on chernozem decreased from 73 to 42% of the area, hayfields and pastures – from 5 to 1%. The land removed from agricultural use occupies an area of 19.8 thousand hectares. On abandoned lands, tree and shrub vegetation develops. 65% of the area of chernozem soils is erosion-hazardous.

Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

In recent years, the world has shown considerable concerns about environmental degradation accompanied by urban expansion. In terms of size, Bogura is equivalent to most of the major cities in Bangladesh, yet no thermal and ecological assessment has ever been conducted here. This study uses multitemporal Landsat satellite images between 2001 and 2020 to investigate the thermal and ecological conditions of Bogura Sadar (sub-district). Land surface temperature (LST) is obtained from Landsat images using the widely used radiative transfer equation. The thermal and ecological conditions are evaluated by computing urban heat island (UHI) and urban thermal field variance index (UTFVI) from LST data. The influence of vegetation, built-area, water-body, and bare soil on LST are examined using land cover indices through pixel-level multivariate linear regression analysis. According to the findings of this sub-district-scale (urban and rural areas) study, the mean LST has increased by 0.62 °C in the last 20 years. As per local administrative-wise findings, LST has increased in most areas, regardless of their urban or rural function. The difference between the urban area and the rest of the surroundings was significant (1.74 °C) in 2020. In 2001, UHI affected area was 5.65 km2, which expanded to 8.84 km2 in 2020. Thermal and ecological conditions are worse in urban areas than its surrounding areas. The regression models of the LST and land cover indices could explain more than half (R2: 0.66 to 0.73) of LST variation over the years. Land cover could explain the LST in 2020 to the least extent implying that anthropogenic activities have greater influence than earlier. Land cover could explain less than half of the LST variation in the urban area.

The dynamics of landscape changes surrounding a firefly ecotourism area

Land use land cover (LULC) patterns have been reported to significantly affect ecotourism areas. Understanding the dynamic patterns of LULC changes is important for the efficient management of these areas. This research determined the influence of LULC changes on mangrove areas and identified the status of mangrove vegetation cover surrounding a firefly ecotourism area using multi-temporal Landsat satellite images from 2002 through 2017. A maximum likelihood classifier and normalised vegetation index differencing were used to determine LULC changes. Mangrove vegetation cover was examined using fraction vegetation cover analysis. Classification accuracy for LULC type was 86.1% and 87.2% for 2002 and 2017, respectively. The results showed a 22.06% loss (122.52 ha) and 4.47% increase (24.99 ha) in mangrove areas. Mangrove areas were converted to oil palm plantations (32.7%), forests (32.3%), settlements (9.25%), barren land (8.99%), rivers (6.8%), swampland (6.11%), urban development (1.67%), rubber plantations (1.25%), bodies of water (0.77%) and roads (0.09%). Increases in mangrove vegetation cover were observed along riverbanks away from development activities. Decreases in mangrove vegetation cover were affected by land-use activities. Proper management is required to avoid further loss of mangrove areas, and appropriate LULC management planning is needed to conserve mangrove areas and sustain firefly ecotourism.

Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh

Cities are developing rapidly and subsequently influences the land use/land cover (LU/LC) changes. Rajshahi city in Bangladesh facing rapid urban expansion by replacing LU/LC such as vegetation cover, water bodies (natural resources) and bare land to urban settlements. This study aims to identify the LU/LC changing patterns of Rajshahi city for 2000, 2010 and 2020 and simulate future LU/LC changes for 2040. Multi-temporal cloud-free Landsat satellite images were used to estimate the LU/LC and urban settlement changing pattern using remote sensing and geographic information system techniques. A support vector machine algorithm was used to classify the LU/LC classes. All the classified LU/LC maps demonstrate excellent accuracy by showing more than 85% overall accuracy. The multi-layer perception Markov chain (MLP-MC) model was used to predict the future LU/LC scenario for 2040. Validation of the predicted LU/LC map demonstrated more than 90% overall accuracy. The study found that an average of 3.6 km2 urban settlements increased at 10 years interval by replacing the natural resources. The future prediction shows that more than 60% of the total city area will be transformed into urban settlements by 2040. The dramatic increase in urban settlements and reduction in green cover and water bodies will damage the environmental sustainability of Rajshahi city by increasing temperature and heatwaves. This study will help the urban planners and policymakers to make proper sustainable development decisions by understanding the LU/LC changing pattern.

Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images

Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.

Evaluation of water quantity\u2013quality, floodplain landuse, and land surface temperature (LST) of Turag River in Bangladesh: an integrated approach of geospatial, field, and laboratory analyses

Rapid urbanization, industrialization, and development activities are posing numerous threats to the rivers of Bangladesh. In the recent decade, Turag River has been exposed to severe threats due to anthropogenic activities. The present research deals with the evaluation of water quantity–quality, landuse and landcover (LULC), and land surface temperature (LST) of Turag River and its floodplain by using an integrated approach of field study, laboratory analysis, and geospatial techniques. LULC mapping of the study area has been done using multi-temporal Landsat satellite images by unsupervised method, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index indices (NDWI) approach. From LULC classification, the study shows that the average reduction of water bodies from 1989 to 2019 is about 60–66%, and the rate of reduction is about 14.25 ha per year. Due to anthropogenic activities and rapid urbanization, a large number of water bodies have been depleting. The average temperature of the river water from filed observed data is 30.23 °C and from image processed data is about 28.32 °C. The minimum and maximum values of pH, EC, TDS, and Eh are 6.87–8.44, 808–1192 μs/cm, 406–739 ppm, − 31 to – 150 mV, respectively. The concentration ranges of cations (Na, Ca, Mg, K, Fe, and Mn) are 85.43–247.60, 28.59–39.47, 11.1–13.58, 5.21–13.26l, .07–1.14, .006–.47 mg/l consecutively. The ranges of anions concentration (HCO3−, Cl−, SO42−, NO3−) are 165.45–388.87, 54.81–194.07, 52.49–289.63, l9.63–126.93 mg/l, respectively. It is evident that HCO3−, NO3−, and Mn exceed the standard limit of Bangladesh Water Quality Standard (BWQS) and World Health Organization (WHO), indicating that the water of the Turag River is deteriorating that is not safe for human consumption and aquaculture purposes. The study will assist to take some effective measures to control the pollution and uphold the water quality and natural riparian processes. So, the outputs of the study will contribute to the future urban landuse planning in the river floodplain area to mitigate ongoing alarming pollution and unplanned industrialization.

Channel morphology and prediction of mid-line channel migration in the reach of Ganga River using GIS and ARIMA modeling during 1975–2020

AbstractThe dynamic nature of meandering poses several challenges in a river. The river Ganga shows severe bank erosion in many of its stretches which creates insecurity to the habitats. In the present study, channel morphology and lateral mid-line migration for 1975 to 2020 in 5 years intervals have been studied. The prediction of lateral mid-line migration from 2020 to 2050 by using multi-temporal Landsat satellite images was made by using the ARIMA model. The river reach was divided into 8 bends and 48 cross-sections were identified. The channel length was observed as 224.35 km in 1975 which reduces to 199.96 km in 2020. A decreasing trend was observed for the mean of channel length and meander ratio, and an increasing trend was noted in the mean of sinuosity ratio and tortuosity ratio. A total of 11 cross-sections showed the rightward shifting and 36 cross-sections showed the leftward shifting. Observed and predicted values showed a good R2 value of 0.90 and 0.89 at CS-24 and CS-25, respectively. The results may be used for planning and management of various river training work and understanding the river system dynamics.

Identification of Desakota Region and Urban Growth Analysis in Patna City, India Using Remote Sensing Data and GIS

In India, the urban population is likely to be 40% of its total population by the year 2030. In such a scenario, managing urban areas is a challenge for planners in terms of developing sustainable infrastructures and addressing environmental issues. The transitional areas in the surroundings of the peripheral region of large and growing cities are called Desakota. Monitoring of such areas is essential as these provide the foundation for future urban establishments. This study tried to identify and assess the peripheral growth of Patna city, a megacity of the state of Bihar in India together with new developing urban areas by applying well-established image classification techniques on the multi-temporal Landsat satellite images of the years 1995, 2005 and 2015. Analysis of the LULC revealed a significant built-up growth in an elongated shape preferably on the upland areas. We also observed an increase in vegetation class area which is in fact due to the increased practice of agricultural plantation in low-lying areas and along the river course. Moreover, the study identified the Desakota regions for different periods, mainly along the major roads in the periphery of the city. The results were validated in the field using GPS based field survey.

Spatio-temporal Analysis of South Western States of Nigeria using Remote Sensing Techniques

The south western states of Nigeria have witnessed urban growth over time and the effect of this is urban growth has resulted in loss of vegetation, waterbody, bare soil, mangroves and gain in built up area for residential and commercial purposes. This research utilizes Remote Sensing techniques in mapping of Land Use/Land Cover changes that has taken place in south western states of Nigeria between a period of 15 years from 2003 to 2018 at a five year interval using Multi temporal Landsat satellite images (MSS, TM, and ETM+).Using supervised classification algorithm, the images were classified into bare soil, built-up area, vegetation and water body, which was used to carry out change detection analysis or time series analysis. Change detection analyses were carried out on the imageries to obtain the physical expansion of the area due to various land use. Results obtained from the analysis of built-up area dynamics for fifteen years revealed that the states have been undergoing urban expansion processes at the detriment of other landcover. The expansion of the built-up area from the analysis shows that the urban center is spreading to adjoining non-built-up areas in all directions. The analysis and quantification of the spatial trend revealed that urban expansion patterns and developmental processes of the past trends and present trends can provide better understanding of the dynamics of spatial increase in built up area and guide for sustainable urban development planning for future urban growth.