Thematic Mapper Research Articles

Spatio-Temporal Analysis of Ecological Vulnerability and Driving Factor Analysis in the Dongjiang River Basin, China, in the Recent 20 Years

The global ecological environment faces many challenges. Landsat thematic mapper time-series, digital elevation models, meteorology, soil types, net primary production data, socio-economic data, and auxiliary data were collected in order to construct a comprehensive evaluation system for ecological vulnerability (EV) using multi-source remote sensing data. EV was divided into five vulnerability levels: potential I, slight II, mild III, moderate IV, and severe V. Then, we analyzed and explored the spatio-temporal patterns and driving mechanisms of EV in the region over the past 20 years. Our research results showed that, from 2001 to 2019, the DRB was generally characterized as being in the severe vulnerability class, with higher upstream and downstream EV classes and a certain amount of reduction in the midstream EV classes. Moreover, EV in the DRB continues to decrease. The spatio-temporal EV patterns in the DRB were significantly influenced by the relative humidity, average annual temperature, and vegetation cover over the past 20 years. Our work can provide a basis for decision-making and technical support for ecosystem protection, ecological restoration, and ecological management in the DRB.

Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana

The current trends of land use dynamics have revealed a significant transformation of settlement spaces. In the Wa Municipality of Ghana, the changes in land use and land cover are inspired by a plethora of driving forces. In this study, we assessed the geo-physical drivers of settlement expansion under land use dynamics in the Wa Municipality of Ghana. The study employed geospatial and remote sensing tools to map and analyse the spatio-temporal dynamics of the landscape, using Landsat satellite imageries: thematic mapper (TM), enhanced thematic mapper (ETM) and operational land imager (OLI) from 1990 to 2020. The study employed a binomial logistic regression model to statistically assess the geo-physical drivers of settlement expansion. Random forest (RF)–supervised classification based on spatio-temporal analyses generated relatively higher classification accuracies, with overall accuracy ranging from 89.33% to 93.3%. Urban expansion for the last three decades was prominent, as the period from 1990 to 2001 gained 11.44 km2 landmass of settlement, while there was 11.30 km2 gained from 2001 to 2010, and 29.44 km2 gained from 2010 to 2020. Out of the independent variables assessed, the distance to existing settlements, distance to river, and distance to primary, tertiary and unclassified roads were responsible for urban expansion.

Intraregional resettlement scheme and livelihood perspectives in Southwestern Ethiopia: challenges and prospects

ABSTRACT This study investigates the livelihood perspectives of resettlers, challenges and prospects of intra-regional resettlement scheme in Chewaka district of Ethiopia. Through multistage sampling procedure, 384 households were selected from sample kebeles for household survey. Descriptive and inferential statistics along with Household Food Balance Model have been employed to analyze data. The results revealed that resettlement scheme had brought substantial changes on livelihood of the resettled communities. It is successful in rescuing human life, improving livelihood assets and ensuring food security for most of the settlers. The study suggests urgent attention on improving infrastructures and environment conservations for betterment of livelihoods. Abbreviations: BZARDO: Buno Bedelle Zone Agriculture and Rural Development Office; DAs: Development Agents; TM: Thematic Mapper: ETM+: Enhanced Thematic Mapper Plus; NMSA: National Meteorological Service Agency; SNNPR: Southern Nations, Nationalities and Peoples Region

Monitoring of Turbidity Variation in the Ukai Reservoir, Gujarat, INDIA, during 1993-2018 using Landsat Series of Dataset

Turbidity is one of the important water quality parameters, which is required to understand the eco-hydrological process such as a trophic state of water, soil erosion into the river system, mixing of other water sources, runoff, discharge etc. An algorithm has been developed to estimate the turbidity (in NTU: Nephelometric Turbidity Unit) over inland waters using Red band of optical multispectral dataset. Field measurements were carried out over Ukai reservoir for 27-28th March 2018 for pre monsoon and 27-30th September 2018 for post monsoon seasons, sampling sites ranging from turbid to clear water. Where in situ water leaving reflectance and turbidity were measured. Model was derived between in situ measured turbidity and spectral reflectance of Red band of Landsat series of datasets includes Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) data from 1993-2018. The model was applied to derive the turbidity maps of Ukai reservoir for pre-monsoon (March, April and May months) season and post monsoon (September, October and November months) seasons. Overall turbidity was in the range of 1.47-25 NTU during the field data collection for both pre and post monsoon seasons. To investigate the results in detail, the reservoir was divided into three parts, i.e. Down (A), Middle (B) and Up Streams (C). The water was relatively clear in the downstream portion with average turbidity less than 5 NTU over the study period. While maximum turbidity was observed in the upstream portion with values more than 20 NTU. In the middle portion, the turbidity values were fluctuating within the range 4-13 NTU with an average value of 6 NTU. These turbidity maps can be used to determine underwater light attenuation that has importance in ecosystem modelling.

Quantitative analysis of spatial distribution of land surface temperature (LST) in relation Ecohydrological, terrain and socio- economic factors based on Landsat data in mountainous area

Land surface temperature (LST) is considered as one of the most significantly effective factors on the regional climate and ecology, playing an important role in connecting surface energy and water exchange. In mountainous regions, LST reveals lots of inconsistencies due to the effect of such factors as topography, vegetation, solar radiation, etc. We sought to investigate the the temporal and spatial variation LST in different years and its relationship with effective factors in 5 dimensions using Multiple statistical methods, the sepidan region in northwest Iran. The multi-factorial land use, topographic (elevation, slope, aspect), biophysical indices (normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference built up index (NDBI), and modified of normalized difference water index (MNDWI)), socio-economic (fossil fuel CO 2 emissions (FFCOE) and road density(RD)), and climate (temperature and solar radiation) was studied in the current research. To this end, Images of July 1998 and 2017 were extracted from Thematic Mapper (TM5) and Operational Land Imager/Thermal infrared sensors (OLI/TIRS8). Moreover, ordinary least squares regression (OLS), Best subset regression, and Hierarchical Partitioning Analysis (HP) were used to investigate the relationship between LST and relevant effective factors. The results indicated that the temperature range varied from 10 to 53 °C in the time period mentioned. The highest amount of LST was observed in barren land use and the lowest one was found in garden lands. An negative correlation was found between LST and elevation. On the other hand, the highest value of the Laps rate of surface temperature was observed in the southern aspects and the lowest one was observed in the western aspects. Furthermore, the highest and lowest values of lase rate were found in slopes less than 10°, and in 50 to 60-degree slopes, respectively. The results of the OLS correlation indicated a negative correlation between LST and NDVI, NDMI, and MNDWI, and a positive correlation of LST with climatic and socio-economic indicators. LST’s highest and lowest correlations were found to be with vegetation (R 2 = 0.95) and road density (R 2 = 0.1). Finally, while in 1998 temperature and vegetation were identified as the most influential factors on LST, it was the elevation that was found to be the most effective factor on LST in 2017 with the effective rate of 82.72%. This study offers a valuable viewpoint on the temporal and spatial variations of LST, their complexity, and the environmental factors that affect them. The viewpoint could, therefore, be used for prospective studies on the analysis of the ecosystem's reaction to climate changes.

Assessment the Quality of Life in Karachi City through the Integration of Space and Spatial Technologies

This study is an attempt to map and measure the quality of life in the urban area of Karachi, Pakistan, by using Landsat-7 sensor, Enhanced Thematic Mapper (ETM+) combined with Census data through the state-of-the-art Geographic Information System (GIS). For this purpose, the physical environment of the city is determined by the variables of Normalized Difference Vegetation Index (NDVI), surface temperature and land cover/use. These are extracted from the satellite image data through various techniques of remote sensing. On the other hand, the socioeconomic variables were obtained from the 2000 Karachi District Census Reports, to represent the living environment of the city. Finally, the integration of the physical variables with the socioeconomic variables was conducted in a GIS framework using an aggregated Z Sum score approach, in order to derive the quality of life scores for the city of Karachi on the basis of Administrative-Spatial Units called Union Councils (UCs). The results effectively demonstrated the efficiency of the Index raster techniques to evaluate and map the quality of life over the study area. In addition, the GIS techniques also isolated the contributing variables that may be responsible for the spatial variability in the quality of life.

Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data

Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an important input to most watershed models, land use/cover (LULC) data can affect hydrological predictions and influence the interpretation of modeling results. In addition, it has been shown that the use of soft data will further ensure the quality of modeling results to be closer to watershed behavior. In this study, the ecologically relevant flows (ERFs) are the primary soft data to be considered as a part of the modeling processes. This study aims to evaluate the impacts of LULC input data on the hydrological responses of the rapidly urbanizing Upper Cahaba River watershed (UCRW) located in Alabama, USA. Two sources of LULC data, i.e., National Land Cover Database (NLCD) and Digitized Landsat 5 Thematic Mapper (TM) images, were used as input in the Soil and Water Assessment Tool (SWAT) model for the years 1992 and 2011 using meteorological data from 1988 to 2013. The model was calibrated at the watershed outlet against daily streamflow from 1988 to 1993 using the 1992 LULC data and validated for the 2008–2013 period using the 2011 LULC datasets. The results show that the models achieved similar performances with both LULC datasets during the calibration and validation periods according to commonly used statistical rating metrics such as Nash Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and model percent bias (PBIAS). However, LULC input information had substantial impacts on simulated ERFs such as mean monthly streamflow, maximum and minimum flows of different durations, and low flow regimes. This study demonstrates that watershed models based on different sources of LULC and applied under different LULC temporal conditions can achieve equally good performances in predicting streamflow. However, substantial differences might exist in predicted hydrological regimes and ERF metrics depending on the sources of LULC data and the LULC year considered. Our results reveal that LULC data can significantly impact the simulated flow regimes of the UCRW with underlaying influences on the predicted biotic and abiotic structures of aquatic and riparian habitats.

Land Use Land Cover Study of Sentinel-2A and Landsat-5 Images Using NDVI and Supervised Classification Techniques

Land Use Land Cover (LULC) change monitoring plays very significant role in planning, policy making, management programs required for development activities at regional levels of any country. This study is an attempt to monitor LULC change of Vijayapura taluk, Karnataka, India for the period of 25 years from 1995 to 2021 using Remote Sensing (RS) and Geographic Information System (GIS). Satellite Images from Sentinel-2A MSI (Multispectral Imager), Landsat-5TM (Thematic Mapper) are used to generate LULC maps. Vegetation Change in the study area is computed using Normalized Difference Vegetation Index (NDVI) and results show that vegetation rate is increased from 0.6% in 1995 to 27.5% in 2021. Supervised Classification is carried out by using Maximum Likelihood Classification (MLC). 5 major classes considered for classification are namely: Waterbodies, Cropland/Vegetation, Fallow Land, Built-up Area and Barren Land. ArcGIS software tool is used for implementing the proposed study. Google Earth Pro used for accuracy assessment which is done by taking ground truth values for corresponding Classifications. Results show that the proposed system is able to achieve 88.16% of overall accuracy. Keyword : Land Use Land Cover, Supervised Classification, Remote Sensing, Maximum Likelihood Classification, Normalized Difference Vegetation Index, High Resolution, Multitemporal, Satellite Images.

Restoring SAR Images Using Effective Image Restoration Approach

Landsat 7 Enhanced Thematic Mapper Plus satellite images presents an important data source for many applications related to remote sensing. An effective image restoration method is proposed to fill the missing information in the satellite images. The segmentation of satellite images to find the SLIC Super pixels and then to find the image Segments. The Boundary Reconstruction is performed using Edge Matching to find the area of the missing region. Peak Signal to Noise Ratio and Root Mean Square Error using with boundary reconstruction and without boundary reconstruction to evaluate the quality and the error rate of the satellite images. The results show the capability to predict the missing values accurately in terms of quality, time without need of external information.The values for PSNR has changed from 25 to 90 and RMSE has changed from 180 to 4 in Red Channel of an image.This indicates that quality of the image is high and error rate is less.

The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research

Abstract. A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

Change detection and urban expansion of Port Sudan, Red Sea, using remote sensing and GIS.

During the last two decades, Port Sudan City has witnessed major environmental stresses resulting from urban expansion and port extensions. This research aims at analysing land water changes (LWCs), land use land cover (LULC) changes and urban expansion of Port Sudan using remote sensing and GIS. For that purpose, an integrated remote sensing and GIS approach was designed to analyse two Enhanced Thematic Mapper (ETM) and an Operational Land Imager (OLI) Landsat images covering the period from 1999 to 2018. LWCs were detected using mathematical remote sensing and GIS-based procedures, while LULC changes were analysed through a post-classification comparison (PCC) approach using a support vector machine (SVM) classifier for classification. Major detected LWCs include landfill activities in the port area and north lagoon of Kilo Tamanya, and dredging activities in Khor Mog. Areas gained by landfill may have improved the port and transport functions but buried coral reefs and caused environmental problems as well. Furthermore, the urban areas were twice doubled, which was mostly rapid and uncontrolled, adding more pressure to the already stressed services and administrative sectors. Threats to the agricultural and mangrove areas were also analysed. The agricultural and mangrove areas were decreased by 40% each, which has been shown to have negative impacts on society, food security and biodiversity. Sadly, the lost agricultural lands were changed into bare soil, slums and other industrial uses. In contrast, mesquite forests were naturally increased by 74%. Mesquites have a major role in combating desertification and providing energy for domestic use. The driving forces and constraints of the urban expansion were highlighted. The change information provided by the applied approach will support decision-makers in adopting integrated and compatible land and coast management planning in the studied coastal city.

Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq

The global and regional land use/cover changes (LUCCs) are experiencing widespread changes, particularly in Baghdad City, the oldest city of Iraq, where it lacks ecological restoration and environmental management actions at present. To date, multiple land uses are experiencing urban construction-related land expansion, population increase, and socioeconomic development. Comprehensive evaluation and understanding of the effect of urban sprawl and its rapid LUCC are of great importance to managing land surface resources for sustainable development. The present research applied remote sensing data, such as Landsat-5 Thematic Mapper and Landsat-8 Operation Land Imager, on selected images between July and August from 1985 to 2020 with the use of multiple types of software to explore, classify, and analyze the historical and future LUCCs in Baghdad City. Three historical LUCC maps from 1985, 2000, and 2020 were created and analyzed. The result shows that urban construction land expands quickly, and agricultural land and natural vegetation have had a large loss of coverage during the last 35 years. The change analysis derived from previous land use was used as a change direction for future simulation, where natural and anthropogenic factors were selected as the drivers’ variables in the process of multilayer perceptron neural network Markov chain model. The future land use/cover change (FLUCC) modeling results from 2030 to 2050 show that agriculture is the only land use type with a massive decreasing trend from 1985 to 2050 compared with other categories. The entire change in urban sprawl derived from historical and FLUCC in each period shows that urban construction land increases the fastest between 2020 and 2030. The rapid urbanization along with unplanned urban growth and rising population migration from rural to urban is the main driver of all transformation in land use. These findings facilitate sustainable ecological development in Baghdad City and theoretically support environmental decision making.

Assessment of Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case of Osogbo and Its Peripheral Areas in Nigeria

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing city areas. Landsat satellite imageries of three different time periods, i.e., Landsat Thematic Mapper (TM) of 1982, 2000 and 2018 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site, quantify the changes in the Osogbo and its peripheral areas from 1982 to 2018 over a period of 36 years. These data sets were imported in ArcGIS 10.3, ERDAS Imagine and IDRIS Selva, satellite image processing softwares to create a false colour composite (FCC), supervised classification methodology was employed using maximum likelihood technique. The images of the study area were categorized into four different classes namely Core-urban, Peri-urban, Vegetation, water body. The results indicate that during the last thirty-six (36) years, Core-Urban land and water body have been increased by 2.74% (38.20 km2) and 0.98% (13.69 km2) while Peri-Urban land, and vegetation cover have decreased by 0.35% (5.00 km2), and 3.36 % (46.87 km2), respectively. The results quantify the land cover change patterns in the city and its peripheral area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyse changes in land cover over time that can be used as inputs to land management and policy decisions.

Is myopia prevalence related to outdoor green space?

Rapid urbanisation and lifestyle changes have been associated with a huge increase in myopia across many parts of the world. There is strong evidence that environmental factors including time outdoors and urbanisation can influence the development of myopia, particularly in school-aged children. The aim of this study is to determine whether there is a relationship between the prevalence of myopia and the amount of vegetation/green spaces across different regions of the world, as a risk factor for myopia development. The prevalence of myopia in the 15 to 19-year age group in Australia, Brazil, China, Finland, India, Iran, Japan, Oman, Singapore, South Africa and the UK was obtained from a meta-analysis by Holden et al. Normalised Difference Vegetation Index (NDVI) was used to quantify green space exposure based on Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite data. Green space was measured in locations specific to 15studies that reported myopia prevalence. Simple linear regression was used to analyse yearly data, and a mixed effects model was applied to assess the significance of green space when study was a random effect. Myopia prevalence increases significantly when green space was <-0.2, but the effect was less apparent for values >-0.1. When a mixed effects model was used, the effect of green space was found to be significantly associated with myopia prevalence (p=0.05). There was evidence of a weak but significant non-linear relationship between myopia and green space, with the effect most apparent at low levels of green space. A larger data sample, along with further investigations into the utilisation of green spaces, are required to understand whether increasing the amount of green space can reduce myopia incidence and progression impact.

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

LAND SURFACE TEMPERATURE (LST) ESTIMATION AT KUSHTIA DISTRICT, BANGLADESH

Land Surface Temperature (LST) is a key phenomenon in worldwide climate change. The knowledge of surface temperature is important to a range of issues and themes in earth sciences, central to urban climatology, global environmental change, and human-environment interactions. In this study, LST for Kushtia District, Khulna division, Bangladesh, is derived using Arc-GIS software version from the images of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution, Landsat-7 Enhanced Thematic Mapper plus (ETM+) with opto-mechanical sensor and Spatial Resolution of 30 m (60 m – thermal, 15-m panchromatic) and Landsat-5 Thematic MAPPER (TM) satellites. A total time span of 20 years, starting from 1998 to 2018 is selected. At every 5 years interval starting from 1998, air temperature, LST, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been calculated. Using the equation from Landsat user’s handbook, the digital number of thermal infrared band is converted into spectral radiance. Plank’s Inverse Function is used to obtain the effective at-sensor brightness temperature from the spectral radiance. The surface emissivity based on NDVI classes is used to retrieve the final LST. The study reveals that LST is increasing with the passage of time. Maximum values of LST are found along the North-East and North-West regions of Kushtia district. NDVI is found to have positive correlation with LST. Also, it has been found that NDWI has little influence on LST. The reasons behind the rise and fall of LST in different years are explained from changes in total vegetation coverage and total abundance of water body coverage viewpoint. The spatial distribution figures of air temperature, LST, NDVI and NDWI could be used as a guideline for urban planning, strategies for quality improvement of urban environment and a smart solution to the reduction of LST.

Detection of Land Use Land Cover Changes Using Remote Sensing and GIS Techniques in a Secondary City in Bangladesh

This study aims at classifying land use land cover (LULC) patterns and detect changes in a 'secondary city' (Savar Upazila) in Bangladesh for 30 years i.e., from 1990 to 2020. Two distinct sets of Landsat satellite imagery, such as Landsat Thematic Mapper (TM) 1990 and Landsat 7 ETM+ 2020, were collected from the United States Geological Survey (USGS) website. Using ArcMap 10.3, the maximum likelihood algorithm was used to perform a supervised classification methodology. The error matrix and Kappa Kat were done to measure the mapping accuracy. Both images were classified into six separate classes: Cropland, Barren land, Built-up area, Vegetation, Waterbody, and Wetlands. From 1990 to 2020, Cropland, Barren land, Waterbody, and Wetlands have been decreased by 30.63%, 11.26%, 23.54%, and 21.89%, respectively. At the same time, the Built-up area and Vegetation have been increased by 161.16% and 5.77%, respectively. The research revealed that unplanned urbanization had been practiced in the secondary city indicated by the decreases in Cropland, Barren land, Wetland, and Waterbody, which also showed direct threats to food security and freshwater scarcity. An increase in Vegetation (mostly homestead vegetation) indicates some environment awareness programs that encourage people to maintain homestead and artificial gardens. The study argues for the sustainable planning of a secondary city for a developing country's future development.

Mapping Spatiotemporal Dynamics of Akure Industrial Layout for Sustainable Development

This research aimed at mapping the spatiotemporal dynamics of the Industrial Layout located in Akure Ondo State Nigeria. The dataset used are the administrative map of Ondo State, Akure Industrial Layout Boundary,various Landsat imageries of 32m resolution which are Thematic Mapper (TM) of 1986 &amp; 1991, Enhanced Thematic Mapper Plus (ETM)+ of 2002, Operational Land Imager / Thermal Infrared Sensor (OLI/TIRS) of 2014, 2017, 2020; and Worldview 3 image 2020 of 1.24m resolution. The Landsat data were used to extract the different Land use/Land cover (LULC) within the study area. GPS receiver and Worldview 3 image were used to obtain the coordinates of the different LULC classes, which aided in the classification of image, and also for accuracy assessment of the classified image. All the Landsat standard data products were processed, to ascertain that they are free of radiometric and geometric errors using the Level 1 Product Generation System (LPGS) and extracted to obtain the landsat image bands. The extracted Landsat images (bands) were used in the processing and calculating the Normalized Difference Vegetation Index (NDVI) and calculation of LULC changes. Evaluation the accuracy of the results produced from the land cover classification was carried out by comparing the results of ground coordinates with the coordinates obtained from a higher resolution image (Worldview 3 image) in order to determine the accuracy of the land cover classification in the study area. The trend of changes of land cover in these areas was assessed and also, the prediction for the future condition both in terms of development was determined based on the results obtained from the initial results. Results from various maps produced and numerical data generated showed that Akure Industrial Layout was mainly dominated by shrub and grass land in 1986 and has in 34 years experienced transformation of 604% in the built environment (18% /year), 119% of Bareland (3.5%/year), and -29% of Grassland (0.9%/year), -66% of Shrub (2%/year). The forecast of the probable spatial extent for the years 2025 and 2030 were estimated to be 175.3Ha and 214.8Ha respectively, which shows there will be a continuous increase in the future development in Akure Industrial layout. The research recommended a proactive action from the government and end-users that will ensure a sustained manageability of the layout.

Assessing Drought Vegetation Dynamics in Semiarid Grass- and Shrubland Using MESMA

Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May–September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico.

Evaluation of land use changes in Ardabil province using satellite image processing

By evaluating land use change and land cover, it is possible to plan and land manage to reduce the impact of alteration and degradation of exploitations. The purpose of this study was to evaluate land use changes in Ardabil province between 1987 and 2015 using Thematic Mapper (TM) sensor of Landsat 5 and Operational Land Imager (OLI) sensor of Landsat 8 satellite images. Firstly, the efficiency of the object-based classification method was evaluated using ground control points, error matrix, and comparing total accuracy and kappa coefficients for the resulting maps. Total accuracy and Kappa coefficients for 1987 were 82.3% and 0.70, and 2015 were 94% and 0.90, respectively. Change detection results showed that from 1987 to 2015, rangelands decreased by 4.2%, equivalent to 75039.3 hectares and forests decreased by 0.5%, equivalent to 9393.7 hectares compared to the total province area and irrigation farming increased by 3.1%, equivalent to 55276.8 hectares, residential areas increased by 0.8%, equivalent to 14989.7 hectares and dry farming increased by 0.7%, equivalent to 12632.4 hectares compared to the total province area. The most significant changes that occurred from 1987 to 2015 were the conversion of rangelands to dry farming with 13.8%, equivalent to 141794.2 hectares and irrigation farming lands with 4.8%, equivalent to 49836.9 hectares of rangelands, irrigation farming to dry farming with 7.7%, equivalent to 11464.8 hectares and forests to rangelands with 6.1%, equivalent to 1059.9 hectares which indicate that the requirement of a serious revision of the management policies.