Operational Land Imager Satellite Research Articles

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Assessment of forest cover and forest loss using satellite images in Thua Thien Hue province, Vietnam

Deforestation in the tropics continues inexorably with severe implications for biodiversity conservation, climate regulation and ecosystem services. This study investigated variation in forest cover in Thua Thien Hue province, Vietnam, using the Landsat Thematic Mapper and Operational Land Imager satellite images over the period 1989–2021. Imageries were classified using the maximum likelihood classification technique for the years 1989, 2006, and 2021 and were evaluated for accuracy using the kappa coefficient for each year. Furthermore, forest cover losses and gains were evaluated using the Normalized Difference Vegetation Index and Soil Adjusted Vegetation Index, which were compared with the output of the supervised classification. Results showed that the forest cover of Thua Thien Hue province has drastically declined over the years. The forest cover, which was estimated at 68.88% (3461.46 km2) of the total land area in 1989, increased to 69.04% (3469.51 km2) in 2006 and subsequently decreased to 57.55% (2891.81 km2) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging, illegal production land, and forest fires. Overall, our results show the necessity of forest management, rational land-use planning policy, and increased community awareness of conservation and sustainable development of forest resources in the study area in the future.

Investigating the effectiveness of Landsat-8 OLI and Sentinel-2 MSI satellite data in monitoring the effects of drought on surface water resources in the Western Cape Province, South Africa

Drought frequency and magnitude are increasing, posing significant risks to surface water bodies, particularly in arid and semi-arid regions. Leveraging on moderate resolution satellite data, such as Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI), offers unique opportunities for monitoring surface water availability and drought impacts. In this study, we assessed the extent to which Landsat-8 OLI and Sentinel-2 MSI satellite data can be used to characterise and monitor the impacts of drought on water resources in the Western Cape, South Africa. Multispectral indices including Normalised Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), and the Land Surface Water Index (LSWI+5) were computed to determine the most suitable method for surface water detection and drought monitoring. Sentinel-2-derived NDVI emerged as the most suitable index for mapping surface waterbodies, demonstrating an overall accuracy of 77.27%. However, LSWI+5 exhibited limitations, misclassifying built-up and mountainous areas as surface waterbodies. SPI, VCI, and WRSI effectively identified drought periods and correlated well with climate data, revealing low rainfall and high evapotranspiration rates during the 2016–2018 drought period. These findings provide valuable insights into surface water variability and the impacts of drought on water resources. Overall, our study underscores the feasibility of utilizing moderate resolution datasets to assess drought impacts on surface water resources, enabling improved water resource management, drought detection, and preparedness efforts in resource limited environments.

Effects of Climate Change, Land Use and Land Cover Variability on Green and Blue Water in Wami/Ruvu Basin, Tanzania

Water basins are the primary food sources, giving green water and blue water worldwide. Despite the basins’ potential, information on the periodical variations in blue water and green water is sparse, particularly in developing countries. The study specifically evaluated the changes in land use and land cover variability (LULCV), effects of land use and land cover variability on green water and blue water variations, and effects of climatic changes on green water and blue water. The evaluation involved the Enhanced Thematic Mapper and Operational Land Imager satellite images of 1990, 2000, 2010 and 2020. Image processing utilized the Soil and Water Assessment Tool (SWAT) in ArcGIS software. The land use and land cover variabilities indicated that land use supporting social-economic activities increased, while natural land cover decreased. Proportionally, blue water per annum was decreasing due to declining natural vegetation, enhanced by the increased socio-economic activities. Whereas, the increase in green water per annum was due to the increased temperatures, boosted by climate changes. Since the temperature rise is mainly uncontrolled, greenhouse farming should be encouraged for making green water more productive in agriculture and communities should be encouraged to practice environmentally friendly anthropogenic activities for sustainable green water and blue water management.
 Keywords: Basin, Green water, Blue water, SWAT model, Climate change

PENDUGAAN POTENSI KARBON HUTAN DENGAN SISTEM PENGINDRAAN JAUH

The relationship between remote sensing and biomass is that remote sensing is one of the best approaches for regional biomass estimation when forest stand data in the field are difficult to obtain. One used images satellite estimate forest carbon and biomass is the Landsat 8 OLI (Operational Land Imager) image with resolution of 30 meters. Study estimate potential tree carbon stored forest by using remote sensing system using Landsat 8 OLI satellite imagery. Regression analysis of actual biomass calculation using allometric equations developed lowland heterogeneous tropical forests with vegetation index NDVI (Normalized Difference Vegetation Index) and ARVI (Atmospherically Resistant Vegetation Index). Determination best of model by calculating the lowest standard error of estimation of each regression model by calculating carbon 50% from above-ground biomass. Estimation carbon potential in study area using linear regression model on the transformation the ARVI vegetation index (Y = 443.24 ln(X) + 576.93). The amount of biomass that The highest forest in the study area is 225.37 Ton/ha with the highest carbon potential of 112.69 Tons/ha. The forest carbon potential value obtained is classified into 5 classes using the Natural Breaks (Jenks) method facilitate at process estimating forest carbon potential so knowing distribution carbon of forest.

The Impact of Land Use Land Cover Change (LULCC) On Runoff Coefficient at Upper Bengawan Solo Watershed, Central Java (Jawa Tengah) Province

Land use land cover change (LULCC) can cause hydrological impact. LULCC for economic activities can provide social and economic benefits, however, these changes often have an impact on decreasing environmental quality. This study aims to determine LULCC in the impact on hydrological conditions, especially on the runoff coefficient. The research location is in the Upper Solo Watershed which has an outlet in Jurug, which has an area of approximately 368,792.43 ha. Water discharge from 2013 to 2021 was obtained from the Bengawan Solo Water Resources Management Office, Ministry of Public Works and Public Housing (PUPR). LULCC data were analyzed from Landsat 8 Operational Land Imager (OLI) satellite imagery recorded in 2013, 2018 and 2021. Based on Landsat 8 OLI satellite imagery results, land cover/use has changed in 2013, 2018 and 2021. Some types of land use that have changed significantly are forest area, dry land agriculture, built-up areas and paddy fields. In the period 2013 - 2018, there was a reduction in the forest area of 0.22% of the watershed area, a reduction in the area of dry land agriculture by 7.16% and addition of a built-up area of 7.16% of the watershed area, an increase in the area of paddy fields by 2.87%. In the period 2018 - 2021, there was a reduction in a forest area of 2.82% of the watershed area, a reduction in water bodies of 0.47% addition of dry land agriculture of 1.51%, and addition of paddy fields of 1.45%. In 2013 the runoff coefficient was 0.41. In 2018, with the addition of a built-up area of 7.16% of the watershed area, the runoff coefficient increased to 0.54. In 2021 the runoff coefficient was decreased to 0.46. LULCC from 2013 to 2021 causes the runoff coefficient value to increase from 0.41 to 0.46.

Exploring the Addition of Airborne Lidar-DEM and Derived TPI for Urban Land Cover and Land Use Classification and Mapping

The classification and mapping accuracy of urban land cover and land use has always been a critical topic and several auxiliary data have been used to improve the classification accuracy. However, to the best of our knowledge, there is limited knowledge of the addition of airborne Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and Topographic Position Index (TPI) for urban land cover and land use classification and mapping. The aim of this study was to explore the addition of airborne lidar-DEM and derived TPI to reflect data of Landsat Operational Land Imager (OLI) in improving the classification accuracy of urban land cover and land use map- ping. Specifically, this study explored the mapping accuracies of urban land cover and land use classifications derived using: 1) standalone Landsat OLI satellite data; 2) Landsat OLI with acquired airborne lidar-DEM ; 3) Landsat OLI with TPI ; and 4) Landsat OLI with airborne lidar-DEM and derived TPI. The results showed that the addition of airborne lidar-DEM and TPI yielded the best overall urban land cover and land use classification accuracy of about 88%. The findings in this study demonstrated that both lidar-DEM and TPI had a positive impact in improving urban land cover and land use classification.

Dry-season variability in near-surface temperature measurements and landsat-based land surface temperature in Kenyatta University, Kenya

Understanding thermal gradients is essential for sustainability of built-up ecosystems, biodiversity conservation, and human health. Urbanized environments in the tropics have received little attention on underlying factors and processes governing thermal variability as compared to temperate environments, despite the worsening heat stress exposure from global warming. This study characterized near surface air temperature (NST) and land surface temperature (LST) profiles across Kenyatta University, main campus, located in the peri-urban using in situ traverse temperature measurements and satellite remote sensing methods respectively. The study sought to; (i) find out if the use of fixed and mobile temperature sensors in time-synchronized in situ traverses can yield statistically significant temperature gradients (ΔT) attributable to landscape features, (ii) find out how time of the day influences NST gradients, (iii) determine how NST clusters compare to LST values derived from analysis of ‘cloud-free’ Landsat 8 OLI (Operational Land Imager) satellite image, and (iv) determine how NST and LST values are related to biophysical properties of land cover features.. The Getis–Ord Gi* statistics of ΔT values indicate statistically significant clustering hot and cold spots, especially in the afternoon (3–5 PM). NST ‘hot spots’ and ‘cold spots’ coincide with hot and cold regions of Landsat-based LST map. Ordinary Least Square Regression (OLS) indicate statistically significant (p < 0.01) coefficients of MNDWI and NDBI explaining 15% of ΔT variation, and albedo, MNDWI, and NDBI explaining 46% of the variations in LST patterns. These findings demonstrate that under clear sky, late afternoon walking traverses records spatial variability in NST within tropical peri-urban environments during dry season. This study approach may be enhanced through collecting biophysical attributes and NST records simultaneously to improve reliability of regression models for urban thermal ecology.

Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques.

Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a short time is a prime factor for administrators for better environmental assessment and sustainable development of urban areas. Traditional survey approaches, on the other hand, are unable to meet the demand for rapid urban land management development, and there is a pressing need to develop new methods to address the limitations of existing ones. This study compares various urban spectral indices to other existing approaches in order to determine which index provides a better representation of the impervious area in the urban watershed. Landsat 8 OLI (Operational Land Imager) satellite images acquired on 15 March 2014 and 31 March 2020 are used in the present study. Indices, namely Modified Built-up Index (MBUI), SwiRed Index (SwiRed), and Enhanced Built-up and Bareness Index (EBBI), were utilized to extract impervious areas. Thresholding of indices is done by comparing them with 1000 reference points taken from Google Earth imagery of the respective years. The accuracy of the urban indices is assessed by comparing the results with high-resolution Google Earth Satellite Images. The impervious area is extracted from spectral indices and other remote sensing techniques such as maximum likelihood classification and support vector machine classification techniques. The overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed for the 2014 dataset was found to be 95.1%, 90.8%, 83.9%, 78.9%, and 87.3%, respectively, and the overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed was found to be 96%, 89.2%, 89.1%, 85.5%, and 92.6%, respectively. Impervious areas in the heterogeneous urban environment can be monitored in a better way and within lesser time by using spectral indices generated using Landsat 8 OLI (Operational Land Imager) satellite data.

Micro hydropower plant potential study based on Landsat 8 operational land imager satellite data

Remote sensing technology has been widely applied in various fields, including oil, gas, and mineral exploration, spatial planning, and environmental monitoring. This paper describes the application of remote sensing technology for the potential study of a renewable micro hydropower plant (MHP) using Landsat 8 satellite data. The Sukaati Watershed, West Java, Indonesia, was selected as the case study area. Landsat 8 satellite data, acquired on August 21, 2020, was applied to extract information on land use, geology, and potential landslides. Drainage patterns, watershed boundaries, and head height were obtained from topographic map data. Drainage patterns, watershed boundaries, and land use are used to calculate flow rates. Geological map and landslide are the basis of layout of MHP components, such as water intake, dam, waterway, settling tank, penstock, and powerhouse. A field survey to acquire actual flow rate and head height was conducted to validate the results of the remote sensing data interpretation. Two potential sites of MHP were selected with a hydropower design of 129 kW and 5.18 MW. This study showed that remote sensing technology is beneficial for studying the potential of MHP because fieldwork can be done more quickly and efficiently.

Measuring land losses caused by water erosion using the SWAT model in the Ourika watershed in the High Atlas of Morocco

In Morocco, the phenomena of water erosion cause significant economic losses mainly linked to the silting up of dams, the degradation of equipment and socio-economic infrastructures, the loss of soil productivity and the insecurity of the population. The SWAT (Soil and Water Assessment Tool) model was used to estimate the quantities of sediments generated by the various erosive processes at the level of the Ourika watershed. The SWAT modeling, which is done with daily time steps, used as basic data; a Digital Elevation Model GDEM-ASTER (Global Digital Elevation-Advanced Space borne Thermal Emission and Reflection Radiometer) with 30 m of resolution, a land cover map developed from the Landsat 8 OLI (Operational Land Imager) satellite image of 2017 with 30 m of resolution and a soil map published by FAO (Harmonized World Soil Database). Also, daily meteorological data from the Tensift Water Basin Agency over a period from 1992 to 2001 were used. The results obtained showed that soil losses due to water erosion in the Ourika watershed reached an average of 9.18 t.ha-1.year-1. The model was calibrated and validated using the SWAT-CUP (SWAT Calibration and Uncertainty Procedures) software SUFI-2 (Sequential Uncertainty Fitting) and after several simulations and iterations a determination coefficient R2 of 0.76 was obtained.

2019 Land Cover Map of Southeast Asia at 30 m Spatial Resolution with Changes Since 2010

Last few decades there are lots of changes in Southeast Asia land cover due to development, industrialization, increasing population, socio-economic activities, and food demands. This research work analysis the maximum likelihood supervised classification approach for Southeast Asia land cover mapping and changes at 30m resolution from 2010 to 2019 using Landsat 8 OLI (Operational Land Imager) satellite data. The resulted maps cover eight land cover classes as barren land, cultivated, developed (built-up area), forest, grassland, shrubland, water, and wetland in Myanmar, Laos, Vietnam, Thailand, Cambodia, and North Malaysia Southeast Asia countries with very high accurate informative information. The resulted maps and information in this paper are very useful to researchers, regional to a national and international level decision and policy makers for sustainable development, fortification of natural resources at the same time protection of fragile eco-environment in Southeast Asia so that they will utilize in future.

Lithological and alteration mineral mapping for alluvial gold exploration in the south east of Birao area, Central African Republic using Landsat-8 Operational Land Imager (OLI) data

Geological field mapping and mineral exploration still remain very laborious in many parts of the inter tropical regions of African plate due to remoteness, low security and high cost. Currently, a number of remote sensing satellite imagery are freely available and can be broadly used for lithological mapping and mineral exploration in inaccessible regions of African plate. The Birao region of the Central African Republic is well known for its alluvial deposits of gold and copper. The Birao region is located in subtropical belt of Africa plate. In this study, Landsat-8 Operational Land Imager (OLI) satellite imagery was used for lithological and alteration mapping in the south-eastern part (inaccessible zone) of Birao region for alluvial gold exploration. Image processing techniques such as False Color Composite (FCC), Band Ratio (BR), Principal component analysis (PCA), minimum noise fraction (MNF) and Spectral Angle Mapper (SAM) were executed to extract spectral information related to lithological units and alteration minerals. Clay and carbonate minerals, iron oxides, ferrous silicates and lithological units, including the Neo-Chadian Formation, the Lower Proterozoic Formations and the Recent Alluvial Formation were identified and discriminated in the study area. The band ratio of 6/7, 4/2 and 6/5 were typically used to map clay, iron oxides and ferrous silicates, respectively. The results of band ratio and PCA techniques were integrated with fuzzy logic modeling that considerably highlighted hydrothermal alteration minerals and high perspective zones for alluvial gold exploration. Additionally, an updated lithological-mineralogical map was generated for south-eastern part of the Birao region by fusing the FCC, MNF and SAM techniques. Comparison with previous lithological map of the study area, virtual verification, fieldwork and laboratory data confirmed good rate of accuracy for the result obtained from the Landsat-8 OLI satellite data. The approach presented in this study is a low-cost technique that highly applicable for lithological mapping and mineral exploration in remote subtropical regions of African plate.

Estimating aboveground biomass using Landsat 8 OLI satellite image in pure Crimean pine (Pinus nigra J.F. Arnold subsp. pallasiana (Lamb.) Holmboe) stands: a case from Turkey

The aim of this study was to estimate the aboveground biomass (AGB) using Landsat 8 Operational Land Imager (OLI) satellite image in Araç Forest Enterprise. The ground data obtained from ground measurements in 317 sample plots were used to accomplish this purpose. The AGB for each sample plot was calculated using this data. The original band brightness, vegetation indices and texture feature values of each sample plot were calculated from the coordinate values of each sample area that were transferred to the Landsat 8 OLI satellite image. The relationships between the AGB with the original band brightness, vegetation indices and texture features values generated from Landsat 8 OLI satellite image were determined by multiple regression analysis. The results revealed that the AGB model using Band2, Band4, Band6 and Band11 had an adjusted coefficient of determination (), of 0.445,37.1738 ton/ha−1, the AGB model using FII, DVI, EVI and IPVI vegetation indices had an adjusted of 0.387,=39.4221 ton/ha−1, the AGB model using Band1_5x5_COR, Band3_3x3_COR, Band3_9x9_COR, Band4_3x3_M, Band7_5x5_V, Band7_9x9_COR, Band8_7x7_COR, Band10_9x9_HOM, Band11_3x3_COR, Band11_5x5_M and Band11_7x7_COR had an adjusted of 0.552, = 33.7669 ton/ha−1. In conclusion, the texture features values were better for the prediction of AGB by using the Landsat 8 OLI satellite image.

Mangrove health along the hyper-arid southern Red Sea coast of Saudi Arabia.

Changes in land use and land cover have severely influenced the sustainability of mangrove vegetation, especially in the hyper-arid, hyper-saline Red Sea coastal waters of Saudi Arabia. The present study investigates the effect of effluents released from an adjoining shrimp farm on the sustainability of a nearby mangrove woodland during operation and after closure of the farm. In addition, the consequences of dredging activities to fill coastal waters for land reclamation to develop a mega seaport at Jazan Economic City are explored. A band image-difference algorithm was applied to Landsat 5 Thematic Mapper and Landsat 08 Operational Land Imager satellite images obtained on different dates, which revealed a prominent vigour boom in the mangrove forest while the shrimp farm operated but a gradual decrease in vigour after its closure. During the investigation time frame of 2016 and 2017, spectral vegetation analysis of Sentinel-2A satellite images highlighted a strong negative correlation between dredging operations for seaport construction and the adjacent fragile mangrove forest. Dredging operations were responsible for a reduction of 19.30% in the Normalized Difference Vegetation Index, 27.5% in the Leaf Area Index, and 19.0% in the Optimized Soil Adjusted Vegetation Index. The results clearly show the potential application of spectral vegetation indices in the monitoring and analysis of anthropogenic impacts on coastal vegetation. We suggest strong management efforts for monitoring, assessing, and regulating measures to offset the negative trends in the sustainability of mangroves in Red Sea coastal regions.

Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images

Mango is a very important fruit which is liked by majority of the population due to its nutritional value and excellent taste. India is the largest producer of mango in the world. Accurate information is required for policy decision making in terms of providing subsidy, area expansion, and crop insurance planning. Hence, this type of information may be retrieve through satellite images by using the image classification techniques, which are playing a crucial role in crop cover classification, yield prediction and crop monitoring etc. Classification of optical satellite images is still a challenging task due to effect of changing atmospheric conditions such as cloud, snow, haze, dust, fog, and rain etc. In this paper, knowledge based decision tree classification (DTC) has been proposed to classify the mango orchards of Lucknow district using multi-temporal Landsat 8 operational land imager (OLI) images from year 2015 to 2017 and further mango orchard area were also estimated. In order to develop the DTC, separability analysis for various land cover classes was carried out on different vegetation indices namely, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and soil adjusted vegetation index (SAVI). In order to analyze the performance of DTC, most commonly used satellite image classifiers such as unsupervised classifier (i.e. ISODATA) and supervised classifier (i.e. Maximum Likelihood) have been used and it is observed that the proposed DTC outperformed these traditional classifiers. Also, accuracy assessment has been carried out to measure the performance of proposed DTC and it is observed that all of the three images from 2015 to 2017 are classified with high overall accuracy, which is ranging from 70.66% to 86.69%. Kappa Coefficient (KC) for all the three images ranged from 0.65 to 0.83, which indicates that classified images are highly acceptable for area estimation.

Identification of water-stressed area based on the interrelationship of soil moisture and seasonal rice cultivation

The present study identifies water-stressed areas based on linkages between NOAH model-based soil moisture and seasonal paddy rice cultivation in the Dwarakeswar–Gandheswari river basin. Soil moisture provides information about crop water stress distribution. A new analytic hierarchy process (AHP)-based index is proposed for water stress evaluation. The spatially distributed paddy rice cultivation is essential from the water resource management point of view. Temperature vegetation dryness index (TVDI), regional water stress index, standardized water level index, and vegetation health index (VHI) are considered for detecting water-stressed area. Water stress indices, soil moisture, and paddy rice cultivation are considered on a seasonal basis (Boro, Aus, and Aman) for the period 2011–2016. The spatial patterns of TVDI, VHI, and rice cultivation are derived from Landsat 7 Enhanced Thematic Mapper Plus and Landsat 8 Operational Land Imager satellite images. Kendall’s tau is considered for correlation analysis. Sensitivity analysis is performed for the generation of AHP-based new water-stressed zone map. The results show that the downstream portion of the river basin has low water stress compared to the upstream part. Ultimately, the results are validated using 20 field sample points. This analysis will be helpful for the irrigation water management framework.

Developing land-cover driver model for estimating the intensity of surface urban heat islands using landsat 8 satellite imagery

It is of utmost importance to understand and monitor the impact of urban heat islands on ecosystems and overall human health in the context of climate change and global warming. This research was conducted in a tropical city, Hanoi, with a major objective of assessing the quantitative relationships between the composition of the main land-cover types and surface urban heat island phenomenon. In this research, we analyzed the correlation between land-cover composition, percentage coverage of the land cover types, and land surface temperature for different moving window sizes or urban land management units. Landsat 8 OLI (Operational Land Imager) satellite data was utilized for preparing land-cover composition datasets in inner Hanoi by employing the unsupervised image clustering method. High-resolution (30m) land surface temperature maps were generated for different days of the years 2016 and 2017 using Landsat 8 TIRS (Thermal Infrared Sensor) images. High correlations were observed between percentage coverage of the land-cover types and land surface temperature considering different window sizes. A new model for estimating the intensity of surface urban heat islands from Landsat 8 imagery is developed, through recursively analyzing the correlation between land-cover composition and land surface temperature at different moving window sizes. This land-cover composition-driven model could predict land surface temperature efficiently not only in the case of different window sizes but also on different days. The newly developed model in this research provides a wonderful opportunity for urban planners and designers to take measures for adjusting land surface temperature and the associated effects of surface urban heat islands by managing the land cover composition and percentage coverage of the individual land-cover types.

Spatiotemporal monitoring of hydrilla [Hydrilla verticillata (L. f.) Royle] to aid management actions

AbstractHydrilla is an invasive aquatic plant that has rapidly spread through many inland water bodies across the globe by outcompeting native aquatic plants. The negative impacts of hydrilla invasion have become a concern for water resource management authorities, power companies, and environmental scientists. The early detection of hydrilla infestation is very important to reduce the costs associated with control and removal efforts of this invasive species. Therefore, in this study, we aimed to develop a tool for rapid, frequent, and large-scale monitoring and predicting spatial extent of hydrilla habitat. This was achieved by integrating in situ and Landsat 8 Operational Land Imager satellite data for Lake J. Strom Thurmond, the largest US Army Corps of Engineers lake east of the Mississippi River, located on the border of Georgia and South Carolina border. The predictive model for presence of hydrilla incorporated radiometric and physical measurements, including remote-sensing reflectance, Secchi disk depth (SDD), light-attenuation coefficient (Kd), maximum depth of colonization (Zc), and percentage of light available through the water column (PLW). The model-predicted ideal habitat for hydrilla featured high SDD, Zc, and PLW values, low values of Kd. Monthly analyses based on satellite images showed that hydrilla starts growing in April, reaches peak coverage around October, begins retreating in the following months, and disappears in February. Analysis of physical and meteorological factors (i.e., water temperature, surface runoff, net inflow, precipitation) revealed that these parameters are closely associated with hydrilla extent. Management agencies can use these results not only to plan removal efforts but also to evaluate and adapt their current mitigation efforts.

An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI

Yield forecasting is essential for management of the food and agriculture economic growth of a country. Artificial Neural Network (ANN) based models have been used widely to make precise and realistic forecasts, especially for the nonlinear and complicated problems like crop yield prediction, biomass change detection and crop evapo-transpiration examination. In the present study, various parameters viz. spectral bands of Landsat 8 OLI (Operational Land Imager) satellite data and derived spectral indices along with field inventory data were evaluated for Mentha crop biomass estimation using ANN technique of Multilayer Perceptron. The estimated biomass showed a good relationship (R2 = 0.762 and root mean square error (RMSE) = 2.74 t/ha) with field-measured biomass.