Landsat TM Research Articles

Evaluation of Sustainable Spatial Suitability of Urban and Population Expansion in Al Hillah City Using Remote Sensing Techniques

The study focused on explaining urban expansion and sustainable development of urban land and explaining the role of population expansion in Al Hillah city, Al Hillah city in the center of Babylion Governorate located. The study relied on analyzing the population data of the city of Al Hillah for a period of time (22 years) for the period (2000-2022). This data was analyzed and its role in planning and designing residential areas and neighborhoods in the Al Hillah city was analyzed based on the standards of urban planning and sustainable growth of cities. Landsat 5TM was used in the investigation, Landsat 8OLI satellite data to retrieve the NDVI, NDBI, and NDWI. The findings showed that between 2000 and 2022, urban and population growth increased, and the gradual disappearance of green lands and agricultural during these years as a result of poor planning, and this will lead to the elimination of green and agricultural areas. Urban areas are established quickly, as population pressure leads to the elimination of agricultural lands, In addition to the high temperatures, which led to changing livelihoods, affecting green lands as well, and increasing urban expansion at high rates. The recent results are useful for planning, management and sustainable development of the urban areas. There was a pressing need to make the right choice in this situation, and while it didn’t help to solve the full issue, it did help to address a significant portion of it by rerouting this growth and arranging it away from agricultural regions in order to protect future generations.

Assessment the Impact of Climate Changes on Snow Cover Area Using Landsat Data in Sulaymaniyah City, Iraq

Snow due to its unique reflective nature and wide surface exposure is an important component of the Earth’s radiation balance. Data from Landsat5 TM and Landsat8 OLI images were used to detect the change in snow cover, values of water bodies, and land surface temperature during the study period years 1987, 1999, 2009, and 2022. ArcGIS 10.8 is used to determine the variations and calculate NDSI, NDSInw, NDWI, and LST index indices. This study aimed to monitor the snow cover area in Sulaymaniyah city, northeastern Iraq, and evaluate the impact of climate changes on snow cover. The results showed significant changes in the snow cover, and the area and percentage of snow cover decreased continuously during the study periods. Some regions have preserved the snow cover and these regions are concentrated in the northeastern part of the province. The finding also revealed that NDSI overestimated the amount of snow and ice cover, because it employs the near-infrared and visible bands, and NDSInw can extract ice cover in areas with water cover more effectively due to its usage of the NIR band.

Seasonal Flooding Impact on Land Surface Temperature(LST) in Response to Vegetation Index Using Spectral Radiance Model in Kirkuk, Iraq

Change detection of land use is essential in management and planning decisions. Land Surface Temperature (LST) has a significant importance in many geoscience aspects, such as heat transportation between land and atmosphere, state of corps, heat flux, greenhouse effect both locally and regionally, surface moisture and surface energy budget estimation, the monitoring of urban heat island, evapotranspiration and environmental biogeochemistry process simulation. A geographic information system was efficiently utilized to observe the changes in land use different categories and to map annual and seasonal LST variations. In this study, Land sat 5 and 8 images for the years 2010, 2017 and 2019 were used to study the flooding imact on the LST. The Mono-Window algorithm and sepectral rediance model on Landsat-5 TM and OLI were used to estimate LST. Taking 2010 as a reference, an empirical study shows that the No-vegetation category percentage was 9.28 % and 11.03% in the 2017 and 2019 summer seasons, respectively, while it was -1.48% and -0.46% in winter. Vegetation change was -60.9% and -50.04% in the 2017 and 2019 summer seasons, respectively, while it was 28.28% and -6.11% in winter. Water area change was -65.8% and 84.1% in the 2017 and 2019 summer seasons, respectively, while it was -38.5% and 59.89% in winter. The results indicate that the water area had greatly decreased during 2017 which led to increase the LST and the floods in 2019 led to decrease the LST by 5-8 degrees. The temperature trend analysis reveals a definite negative association between LST and flooded area, representing this study’s significant finding.

Impacts of urban land use and land cover change on wetland dynamics in Jimma city, southwestern Ethiopia

Abstract Wetland has been substantially decreased by anthropogenic activities worldwide, requiring urgent conservation action. The present study aimed to analyze the effects of land use land cover change on wetland dynamics from 1992 to 2022 in Jimma City, Southwestern Ethiopia. Landsat TM of 1992, ETM+ of 2000, ETM+ of 2009 and OLI/TIRS of 2022 were used in this study. Landsat images were classified by using supervised classification with maximum likelihood algorithm. The results revealed that grassland, wetland and vegetation land cover classes declined by 2,304.9 ha, 1410.2 ha and 820.5 ha, respectively whereas farm land, settlement and bare land increased by 2,199 ha, 2,111 ha and 225.4 ha, respectively. Results show that the normalized difference water index (NDWI) has positive and negative strong relationship with normailized difference vegetation index (NDVI) and normalized difference built up index (NDBI) with the coefficent determination (R2) of 0.96 and 0.98, respectively. Due to rapid urbanization and declining vegetation cover in the study area, about 98% of wetland land cover in the study area has been lost over the past decades. Therefore, the governmental and non-governmental organizations should give special attention to wetland restoration and management in the study area.

INDUSTRIAL INVESTING AS AN INSTRUMENT OF SUSTAINABLE DEVELOPMENT IN ALGERIA (CASE IN BOUIRA PROVINCE - EASTERN ALGERIA)

Algeria has developed an interesting manufacturing sector since its independence with the aim to develop the national economy. In this paper, we'll highlight the industrial investments that Bouira's municipal government (Easter Algeria) made between 2003 and 2020. To achieve this goal, we used a series of LANDSAT 7ETM+/LANDSAT 8OLI satellite images acquired respectively in 2006, 2014, and 2020. The processing applied to images evaluates the normalized vegetation (NDVI), which provides an estimation of the used area for industrial investment. All processed images were combined in a GIS (Geographic Information System), to digitize subsequently industrial areas. This research shows that over 242.69 ha of Oued El-Berdi's zone was used for industrial projects.

Understanding the role of surface runoff in potential flood inundation in the Kashmir valley, Western Himalayas

In the present study, direct runoff and Total runoff were estimated at temporal scales (2000–2018) focusing on flood potential in the catchments of Kashmir valley. The valley is recurrently prone to catastrophic floods with increased Total runoff frequency in recent decades (post-2010 periods). LANDSAT ETM+ satellite image based land use/land cover mapping exhibited the dominance of vegetation cover (46%) primarily in the peripheral hilly regions, in contrast to agricultural land (29%) and built-up (1.6%) in the elongated valley region. The high anomalous rainfall (>500 mm) in most of the catchments during 2014–2018 caused an increased runoff apparently leading to catastrophic floods inundation (as observed in 2014 with accumulated rainfall of 713 mm and cumulative Total Runoff of 55 LCM). A higher contribution towards Total Runoff in northern catchments (>56%) exhibited higher susceptibility to flood inundation as compared to the southern catchments (<42%). The study highlighted a high vulnerability in the large proportion of built-up land (148 km2) within close proximity to Jhelum river and below 1560m elevation range due to the erratic rainfall induced runoff floods. The insignificant proportion of built-up led to less contribution towards the high runoff yet increases the severity of runoff and flood inundation due to high imperviousness. The rapid built-up growth coupled with deterioration of natural wetlands in the urban-rural fringe increased the impact of catastrophic flood manifolds. The study provided a regional and comprehensive runoff assessment that can be utilized to develop adequate flood control measures at the catchment level in the valley.

APPRAISAL OF CLIMATE CHANGE DISASTER AND FOOD SECURITY IN KHYBER PAKHTUNKHAW PAKISTAN USING GEOSPATIAL TECHNOLOGIES

Abstract. Drought is one of the most severe natural disasters. It negatively influences the crop output considerably as water resources are inadequate across large geographic areas. Food insecurity, hunger, and poverty are widespread in three Pakistani provinces: Baluchistan, Khyber Pakhtunkhwa, and Sindh. Until March/April 2022, roughly 4.66 million people, or 25% of the population examined, were severely food insecure. Historically, the severity of a drought is evaluated using several different indicators, with the selection of indices depending on the data that is easily accessible. By multiple aspects, including potential evapotranspiration and rainfall data on a standard timescale and computing monthly, seasonal, or annual data, the Reconnaissance Drought Index (RDI) has a significant advantage over the other indices in determining drought severity. When rainfall in a region is significantly lower than the amount of potential evapotranspiration, drought situations can occur on the earth. Rainfall will often occur in locations with an increase in the relative humidity of the air. The reconnaissance drought index (RDI) for Khyber Pakhtaunkhawa province in Pakistan was calculated from 1981 to 2020 for 3-, 6-, and 12-month timeframes. This research employed rainfall, minimum and maximum temperature data from KPK province to describe drought using the DrinC program, the Man Kendall test, and Sen's slope calculator to determine drought patterns and variation. All regions had moderate, severe, and extreme droughts, with RDIs between -1.0 and -3.5. RDI 3-, 6-, and 12-month evaluations highlighted 1991, 1994, 2000, 2010, 2015, and 2017. These results indicate that productivity declines in dry and subsequent years due to seasonal rainfall and soil moisture retention.Wheat production averaged 1.7 tones ha-1 year-1, with significant variations. Between October and December, it is an essential to have favorable growing conditions to increase productivity. If droughts occur in this era, the yield will be substantially more negatively impacted than if they occur later in the growing cycle. The 3-month RDI values offer a more accurate description of this variation than the six-month and twelve-months RDI Values. Geospatial drought analysis indicates a considerable variance in drought patterns across the Kpk province over the last 4 decades. Because long-term droughts sometimes affect water quality, especially groundwater, a rise in the intensity of long-term droughts could pose a risk to the water management in the regions that have been examined. Analysis of the Landsat TM 30 m data took place. At the same time, Landsat imageries were utilized to generate the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for drought evaluation. The findings of the NDWI indicate an increase in value from 0.58 to 0.7 for the KPK region.

Exploring the performance of spectral and textural information for leaf area index estimation with homogeneous and heterogeneous surfaces

AbstractLeaf area index (LAI) is one of the key parameters of vegetation structure, which can be applied in monitoring vegetation growth status. Currently, abundant spatial information (e.g., textural information), provided by the developing remote sensing satellite techniques, could boost the accuracy of LAI estimation. Thus, the performance of spectral and textural information must be evaluated for different vegetation types of LAI estimation in different surface types. In this study, different spectral vegetation indices (SVIs) and grey‐level co‐occurrence matrix‐based textural variables under different moving window sizes were extracted from Landsat TM satellite data. First, the ability of different types of SVIs for LAI estimation in different surface types was analysed. Subsequently, the effect of different texture variables with different moving window sizes towards LAI estimation accuracy in different vegetation types was explored. Lastly, the performance of SVIs combined with textural information for the LAI estimation in different vegetation types was evaluated. Results indicated that SVIs performed better for LAI estimation in the homogeneous region than that in the heterogeneous region, and difference vegetation index was more remarkable for LAI estimation in different vegetation types than other SVIs. In addition, variations in texture variables and moving window sizes had a large influence on LAI estimation of natural vegetation with high canopy heterogeneity. SVI combined with textural information can efficiently improve the accuracy of LAI estimation in different vegetation types (R2 = 0.672, 0.455 and 0.523 for meadow, shrub and cantaloupe, respectively.) compared with SVI alone (R2 = 0.189, 0.064 and 0.431 for meadow, shrub and cantaloupe, respectively.). Especially for natural vegetation (meadow, shrub), the addition of textural information can greatly improve the accuracy of LAI estimation.

Spatial mapping of the leaf area index using remote sensing and ground measurements – the Biebrza National Park case study

The purpose of the described research was an attempt to estimate the leaf area index (LAI) parameter describing the structure of the vegetation based on the Landsat 5TM satellite imagery and field measurements made with the use of an optical plant canopy analyzer. The study was carried out in north-eastern Poland in the Biebrza river valley within the boundaries of the Biebrza National Park during the growing season of the year 2007. There were 13 spectral indices given in the literature known to be correlated with the LAI. The highest coefficient of determination and the highest correlation coefficient were obtained for the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI) indices for the wetland areas in the Biebrza river valley. The field measurements of the leaf area index and its spatial representation on satellite image show that the vegetation of natural river valleys is characterized by high spatial and seasonal variability. The study of the LAI on such large natural areas that are extensively used also requires knowledge of the methods of land use and the application of individual agrotechnical measures.

Monitoring of drainage system and waterlogging area in the human-induced Ganges-Brahmaputra tidal delta plain of Bangladesh using MNDWI index

Waterlogging is one of the major global problems which affects agro-economic activities around the world. In the coastal areas of Bangladesh, especially the southwestern coast, drainage congestion and waterlogging are very common which makes the area uninhabitable. Therefore, timely checking of drainage systems and surface water, and conveying data on the dynamics of drainages and surface water are important for plan and supervisory processes. The present study took an effort to illustrate the waterlogging and morphological change of the rivers in the southwestern coast of Bangladesh through the Modification Normalized Difference Water Index (MNDWI) values which are valuable indicators for monitoring the water area and land use pattern change. Landsat images (Landsat L8 Oli TIRS, Landsat ETM+, Landsat TM) were used in the research. The study reveals that from 1989 to 2020, the shallow water area (mostly covered with rivers) decreased by ∼14.30 km2 yr−1, whereas the wet-land area (mostly covered with beels and water logging areas) increased by ∼ 67.12 km2 yr−1. The bare land area also increased at a rate of ∼ 36.90 km2 yr−1. On the other hand, the green vegetation decreased at a rate of ∼166.1 km2 yr−1, whereas the moderate green vegetation area increased by ∼ 69.77 km2 yr−1 for the same period. In the coastal zones of Bangladesh, the polders, embankments, upstream dams, etc., enhance more sedimentation within the channels rather than in the nearby tidal plains. As a result, the shallow water area which is mostly covered by rivers is gradually decreasing. Moreover, due to increase in wet-land areas with salinity intrusions which affect the vegetation. Therefore, the green vegetation area is regularly declining due to demolition or conversion to moderate green vegetation. The findings of the research will be supportive for coastal scientists worldwide, policy makers & planners, and finally supportive for sustainable management of the coastal areas including Bangladesh.

Dynamics and future prediction of LULC on Pare River basin of Arunachal Pradesh using machine learning techniques.

Anthropogenic disturbances caused by increasing population densities are a significant concern as they accelerate climate change. Thus, regular monitoring of land use/land cover (LULC) is essential to mitigate these effects. Pare River basin of Arunachala Pradesh situated in the foothills of Eastern Himalayas was selected for this study. Landsat-5 TM and Landsat-8 OLI data from 2000 (T1), 2015 (T2), and 2020 (T3) were used to prepare the LULC map. A support vector machine (SVM) classifier in the Google Earth Engine (GEE) environment was utilized for classification of LULC, while the TerrSet software environment was used for change analysis and projection using the CA-MC model. The SVM classifier produced overall all classification accuracies of 0.91, 0.85, and 0.91 with kappa values of 0.88, 0.82, and 0.89 for T1, T2, and T3, respectively. The CA-MC model, which combines Markov chain and hybrid cellular automata, was calibrated with various predictor variables, including natural, proximity, and demographic variables along with T1 and T2 LULC and validated using T3 LULC. The MLP was used for calibration, and an accuracy rate of above 0.70 was employed to generate transition potential maps (TPMs). The TPMs were used to project future LULC for 2030, 2040, and 2050. Validation analysis produced satisfactory results, with Kno, Klocation, Kquality, and Kstandard values of 0.96, 0.95, 0.95, and 0.93, respectively. Receiver operating characteristics (ROC) analysis showed an excellent area under the curve (AUC) value of 0.87. The findings of this study provide important insights to decision-makers and stakeholders in addressing the impacts of LULC changes.

Comparison of Forest Restorations with Different Burning Severities Using Various Restoration Methods at Tuqiang Forestry Bureau of Greater Hinggan Mountains

Forest disturbances and restoration are key processes in carbon transmission between the terrestrial surface and the atmosphere. In boreal forests, fire is the most common and main disturbance. The reconstruction process for post-disaster vegetation plays an essential role in the restoration of a forest’s structure and function, and it also maintains the ecosystem’s health and stability. Remote sensing monitoring could reflect dynamic post-fire features of vegetation. However, there are still major differences in the remote sensing index in terms of regional feasibility and sensibility. In this study, the largest boreal primary coniferous forest area in China, the Greater Hinggan Mountains forest area, was chosen as the sampling area. Based on time series data from Landsat-5 TM surface reflectance (SR) and data obtained from sample plots, the burned area was extracted using the Normalized Burn Ratio (NBR). We used the pre- and post-fire difference values (dNBR) and compared them with survey data to classify the burn severity level. The Normalized Difference Vegetation Index (NDVI) (based on spectrum combination) and the Disturbance Index (DI) (based on Tasseled-Cap transformation) were chosen to analyze the difference in the degree of burn severity and vegetation restoration observed using various methods according to the sequential variation feature from 1986 to 2011. The results are as follows: (1) The two remote sensing indexes are both sensitive to fire and the burn severity level. When a fire occurred, the NDVI value for that year decreased dramatically while the DI value increased sharply. Alongside these findings, we observed that the rangeability and restoration period of the two indexes is significantly positively correlated with the degree of burn severity. (2) According to these two indexes, natural vegetation restoration was faster than the restoration achieved using artificial methods. However, compared with the NDVI, the DI showed a clearer improvement in restoration, as the restoration period the DI could evaluate was longer in two different ways: the NDVI illustrated great changes in the burn severity in the 5 years post-fire, while the DI was able to show the changes for more than 20 years. Additionally, from the DI, one could identify felling activities carried out when the artificial restoration methods were initially applied. (3) From the sample-plot data, there were few differences in forest canopy density—the average was between 0.55 and 0.6—between the diverse severity levels and restoration methods after 33 years of recovery. The average diameter at breast height (DBH) and height values of trees in naturally restored areas decreased with the increase in burn severity, but the values were obviously higher than those in artificially restored areas. This indicates that both the burn severity level and restoration methods have important effects on forest restoration, but the results may also have been affected by other factors.

Dynamicity of Land Use/Land Cover (LULC) An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation (DMC) in India

The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods, changing the landscape of regions outside the city and fostering the growth of physical infrastructure. Using multi-temporal satellite images, the dynamics of Land Use/Land Cover (LULC) changes, the impact of urban growth on LULC changes, and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India. The study used different case studies to highlight the study area's heterogeneity, as the phenomenon of change is not consistent. Landsat TM and OLI-TIRS satellite images in 1991, 2001, 2011, and 2021 were used to analyze the changes in LULC types. We used the relative deviation (RD), annual change intensity (ACI), uniform intensity (UI) to show the dynamicity of LULC types (agriculture land; built-up land; fallow land; vegetated land; mining area; and water bodies) during 1991–2021. This study also applied the Decision-Making Trial and Evaluation Laboratory (DEMATEL) to measure environmental sensitivity zones and find out the causes of LULC changes. According to LULC statistics, agriculture land, built-up land, and mining area increased by 51.7, 95.46, and 24.79 ​km2, respectively, feom 1991 to 2021. The results also suggested that built-up land and mining area had the greatest (LST), whereas water bodies and vegetated land showed the lowest LST. Moreover, this study looked at the relationships among LST, spectral indices (Normalized Differenced Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI)), and environmental sensitivity. The results showed that all of the spectral indices have the strongest association with LST, indicating that built-up land had a far stronger influence on the LST. The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km2/a, respectively, during 1991–2021. In summary, this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land, thus making effective peri-urban planning decisions.

Mapping and simulating the spatial-temporal changes in the Lagos wetland ecosystem: A step-by-step approach to creating a carbon-neutral, sustainable urban city

BackgroundLagos is the largest megacity in Africa, with numerous lagoons and wetlands. Due to anthropogenic activities, Lagos’ wetlands have undergone significant alterations in the last few decades. The UN's Sustainable Development Goals (SDGs) and the campaign programs for carbon neutrality in the city offer a plan of action that recognizes the necessity of managing water-related habitats, such as wetlands. Wetlands are essential to accomplishing the SDGs because they provide a solution to many global issues like climate change, food insecurity, and water scarcity. In some ways, each of the SDGs is related to wetlands, but SDGs 6 and 13 are particularly significant to this study. The application of remote sensing and Geographic Information Systems (GIS) is a crucial tactic for monitoring the achievement of these SDGs. ObjectivesThe study aims to map and simulate the spatial-temporal changes in the Lagos wetland from 2000 to 2050 for the achievement of a carbon-neutral city. MethodsIn this study, Landsat ETM+ (2000, 2010) and OLI 2020 were used to monitor the spatial-temporal changes in Lagos wetlands. The maximum likelihood classification method was adopted for the supervised classification. The simulation analysis was done with the cellular automata-Markov model. ResultsThe findings show that the water body increased throughout the study, rising from 658.76 km2 in 2000 to 681.60 km2 in 2010, and to 712.0 km2 in 2020. Marshy areas have decreased progressively from 39.41 km2 in 2000 to 14.48 km2 in 2010 and 0.078 km2 in 2020. Swamps and mangrove wetland classes had changed from 448.24 km2 and 406.29 km2 in 2000 to 317.74 km2 and 538.87 km2 respectively in 2010, and to 435.29 km2 and 405.33 km2 respectively in 2020. The simulated results show that water bodies, swamps, marshy areas, and mangroves will respectively change to 711.99 km2 and 747.11 km2, 405.26 km2 and 400.16 km2, 0.15 km2 and 0.13 km2, as well as 455.29 km2 and 455.30 km2 in the simulated years 2030–2050. ConclusionThe study shows that Lagos has witnessed spatial-temporal changes in its wetland classes within the last two decades. The results prove that remote sensing and GIS are effective tools for studying water-related habitats such as wetlands.

Analysis of the Spatial and Temporal Evolution Characteristics and Driving Forces of the Surface Thermal Environment in Lanzhou City

The urban heat island effect is becoming more and more serious due to the continuous expansion of cities in China, and improving the urban thermal environment is an important prerequisite for creating a livable city. Based on the Landsat TM images of 2001 and 2011 and Landsat TIRS images of 2021, this study investigated the spatial and temporal distribution and evolution characteristics of the urban thermal environment in Lanzhou City in the last 20 years by using the radiation equation conduction method to invert the surface temperature of Lanzhou City. The results show the following: (1) The radiation range of the medium temperature zone in Lanzhou City spreads in an “extended” style from 2001 to 2021 until it covers the whole main urban area, the secondary high and low temperatures decrease, and the corresponding medium temperature zone increases over a large area. (2) The average temperatures of Lanzhou urban area and its arable land, forest, grassland, and water area within the urban area reaches 10–25 °C, and the heat island area decreases by 9.56% in 20 years, with the high-temperature zone decreasing by 42.32%. (3) The proportion of water bodies and the proportion of impermeable surfaces are dominant factors in the spatial differentiation of surface temperature, and the interaction and synergy of various influencing factors affect the spatial differentiation of surface temperature.

Remote sensing based investigation of coastal LULC dynamics in the coastal region of Bangladesh

Rapid land-use transition on the landscape is accelerating the overall land cover change by altering naturally productive land into more complex land-uses. This study explored the spatial and temporal dynamics of land use and land cover (LULC) as landscape exploration though satellite imagery in the southwest of Bangladesh from 2000 to 2020. The goal of this study was to quantify the overall trend for different LULC changes using satellite imagery from Landsat TM and Landsat OLI/TIRS. Sequential K-means-based unsupervised and more accurate maximum likelihood classification (MLC) algorithm-based supervised methods were used to analyze land-use change for each year. The trend of land-use changes and transition results between 2000 and 2020 showed a massive increase in built-up (2010.654 ha/year) land-use; 525546.18 ha of forest increase (1.7%) in two decades, whereas 13351.59 ha (1.10%) of forest or plantation area was transformed into the built-up area. Remote sensing (RS) based land dynamics is always land cover analysis, especially for the vast area of interest.

Spatiotemporal analysis of land surface temperature owing to NDVI: A case study of Vadodara District, Gujarat

The expeditious extension of LULC in the name of development is the root cause of global warming. Replacement of natural resources due to the expansion of manmade erections is accountable for the increase in LST of Earth’s topography. The impression of change in LULC is reflected in LST. To seize the rising temperature, the lamentation of a new plan of action for urbanization is of utmost requisite. This paper examines the change in LULC and its spatiotemporal impact on LST in Vadodara, which is situated on the bank of river Vishmamitri river. Vadodara an arid region has three main seasons and these are summer, monsoon, and winter. The climate is characterized by hot summer and dryness in the non-rainy seasons. May is the hottest month while January is the coldest month. The annual rainfall of the district is 475.2 mm. Hence, to analyze we used multi-spectral and multi thermal Landsat TM and ETM+ satellite images to monitor the evaluation of LULC and its impact on LST from 2001(pre-monsoon) to 2021(pre- monsoon). The study explores to what extent observed LST can be examined by vegetation cover measured through NDVI from2001 to 2021. To achieve this an analysis of co-relation is performed between LULC and LST and by using spectral indices comprising NDVI with the help of software like ArcGIS 10.2 and Erdas Imagine 2014. It had been observed that a considerable increase in LST in Vadodara was 58.338°C (Max) and 21.9014°C (Min) in 2001(pre-monsoon) to 60.844 °C (Max) and 24.6784 °C (Min) in 2021(pre-monsoon) which is just about 2°C increase in Max and 3 degrees increase in Min for LST in past 20 years. It was also observed that there is an inverse relationship between LST and NDVI. The value of NDVI is observed that change from 0.380711(H) and -0.59322(L) in 2001 to 0.551(H) to -0.351193(L)in 2021. Moreover, SMI places a vital role to investigate and verify the relation between LST and NDVI. Henceforth, to verify SMI was also calculated and it was noticed that places with high LST value and low NDVI value contained less soil moisture and places with less LST value and high NDVI values contained more soil moisture. Thus, it can be concluded that, if urban planners and decision-makers implement suitable land-use strategies then Earth’s topography can be protected from adverse effects of urban heat by planting adequate and appropriate trees in bare soil and beside the impervious areas, thus the expansion of UHI can also be controlled. Moreover, with the help of SMI values, it will also be beneficial for the agricultural sector.

Quantifying forest land-use changes using remote-sensing and CA-ANN model of Madhupur Sal Forests, Bangladesh

The conversion of forest cover due to anthropogenic activities is of great concern in the Madhupur Sal Forest in Bangladesh. This study explored the land use changes in the Sal Forest area from 1991 to 2020, with the prediction of 2030 and 2040. This study examined and analyzed the changes in five land use classes viz., waterbodies, settlement, Sal Forest, other vegetation, and bare land, and predict those classes using Cellular Automated Artificial Neural Network (CA-ANN) model. The Sankey diagram was employed to represent the change percentage of Land Use and Land Cover (LULC). The LULC for 1991, 2000, 2010, and 2020 derived from Landsat TM and Landsat OLI images, were used to predict the periods of 2030 and 2040. During the last 30 years, the Sal Forest area decreased by 23.35%, whereas the settlement and bare land area increased by 107.19% and 160.89%. The greatest loss of the Sal Forest was observed from 1991 to 2000 by 46.20%. At the same period of time the settlements were increased by 92.68% indicating the encroachment of settlement in the Sal Forest area. The Sankey diagram revealed a major conversion was found between other vegetation and the Sal Forest area. There was a vis-à-vis between other vegetation and the Sal Forest area from 1991 to 2000 and from 2000 to 2010. Interestingly, there was no conversation of the Sal Forest area to other land use from 2010 to 2020, and the prediction showed that the Sal Forest area will be increased by 52.02% in 2040. The preservation and increment of the Sal Forest area suggested strong governmental policy implementation to preserve the forest.

Deriving a year 2000 glacier inventory for New Zealand from the existing 2016 inventory

Abstract Due to adverse snow and cloud conditions, only a few inventories are available for the maritime glaciers in New Zealand. These are difficult to compare as different approaches and baseline data have been used to create them. In consequence, glacier fluctuations in New Zealand over the past two decades are only known for a few glaciers based on field observations. Here we present the results of a new inventory for the ‘year 2000’ (some scenes are from 2001 and 2002) that is based on glacier outlines from a recently published inventory for the year 2016 and allowed consistent change assessment for nearly 3000 glaciers over this period. The year 2000 inventory was created by manual on-screen digitizing using Landsat ETM+ satellite imagery (15 m panchromatic band) in the background and the year 2016 outlines as a starting point. Major challenges faced were late and early seasonal snow, clouds and shadow, the geo-location mismatch between Landsat and Sentinel-2 as well as the correct interpretation of ice patches and ice under debris cover. In total, we re-mapped 2967 glaciers covering an area of 885.5 km2 in 2000, which is 91.7 km2 (or 10.4%) more than the 793.8 km2 mapped in 2016. Area change rates (mean rate −0.65% a−1) increase towards smaller glaciers. Strongest area loss from 2000 to 2016 occurred at elevations ~1900 m but the highest relative loss was found below 800 m a.s.l. In total, 109 glaciers split into two or more entities and 264 had wasted away by 2016.

Land Use Land Cover Change Detection of Oil Exploration and Production in Melut County, Upper Nile State, South Sudan

This study explores the Land Use Land Cover Change (LULC) of oil industry on environment and local communities in Melut County located in Upper Nile State in South Sudan; as well as engaging the stakeholders in oil sectors to adopt the best techniques of sustainable oil exploration and production in the area. This research is based on remote sensing imageries collected for 2000 and 2018. The satellite imageries data were collected from Landsat-5 TM and Landsat-8 ETM+ for the two points. These satellite imageries were used to reach inaccessible areas in the Area. ENVI 5.1 software was used to analyze the satellite imageries. The results reveal tremendously significant environmental degradation as a result of land use changes over time that leads to deterioration of the livelihoods of the oil producing communities in Melut County. The main contributions to LULC were from exploration and production activities such as drilling, and oil processing. Produced water and oil well pads were the major catastrophes that lead to destruction of croplands, damage of vegetation cover, and contamination of surface and ground water in the area. There are inadequate social services in the areas around oil fields; hence, the areas are characterized by poverty, lack of access to resources, unimproved social amenities and vulnerability to the poor unhealthy conditions in the oil environment. Furthermore, the government should regulate the oil industry by scrutinizing any environmental violation, and heavily fine the non-complying individuals and companies.