Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.

TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban planning.The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data.In this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery.Existing geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of generalization.To address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data.The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing rates.Concurrently,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing data.Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Geospatial Variability of Cholera Cases in Malawi Based on Climatic and Socioeconomic Influences

Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.

Leveraging Geospatial Technologies for Resource Optimization in Livestock Management

Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.

Identifying the uneven distribution of health and education services in China using open geospatial data

Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes.Health and education are critical domains of the Unites Nations’Sus-tainable Development Goals(SDGs),yet existing research on the accessibility of corresponding services focused mainly on detailed but small-scale studies.This means that such studies lack accessibility metrics for large-scale quantitative evaluations.To address this deficiency,we evaluated the accessibility of health and education ser-vices in China's Mainland in 2021 using point-of-interest data,OpenStreetMap road data,land cover data,and WorldPop spatial demographic data.The accessibility metrics used were the least time costs of reaching hospital and school services and population coverage with a time cost of less than 1 h.On the basis of the road network and land cover information,the overall average time costs of reaching hospital and school were 20 and 22 min,respectively.In terms of population coverage,94.7%and 92.5%of the population in China has a time cost of less than 1 h in obtaining hospital and school services,respectively.Counties with low accessibility to hospitals and schools were highly coupled with poor areas and ecological function regions,with the time cost incurred in these areas being more than twice that experienced in non-poor and non-ecological areas.Furthermore,the cumulative time cost incurred by the bottom 20%of counties(by GDP)from access to hospital and school services reached approximately 80%of the national total.Low-GDP counties were compelled to suffer disproportionately increased time costs to acquire health and education services compared with high-GDP counties.The accessibil-ity metrics proposed in this study are highly related to SDGs 3 and 4,and they can serve as auxiliary data that can be used to enhance the evaluation of SDG outcomes.The analysis of the uneven distribution of health and education services in China can help identify areas with backward public services and may contribute to targeted and efficient policy interventions.

Characterization of saline soil for the halophytes of largest inland saline wetland of India using geospatial technology

About 23%of the surface area and 44%of the volume of all the lakes are occupied by saline lakes in the world.Importantly,agricultural diversion,illegal encroachment,pollution,and invasive species could cause these lakes to dry up completely or partially by 2025.Illegal saltpan encroachment is causing Sambhar,India’s largest saline lake,to shrink by 4.23%every decade.This study aims to characterize the soil parameters where halophytes are growing.A literature survey was conducted for halophytes and soil characteristics.The study area was divided into four zones for stratified random sampling.Soil sampling was conducted in February 2021.The soil indicators for halophyte selected were pH,electrical conductivity,moisture,salinity,organic carbon,and organic matter.The obtained results were interpolated in the geospatial platform for soil characteristic mapping.It is found that no research is conducted on halophytes of the lake.Studies on soil are also inconsistent and only six common parameters could be identified.Results show that the pH ranged 9.37-7.66,electrical conductivity was 16.1-0.38,moisture 23.37%-1.2%,organic carbon 3.29%-0.15%,organic matter 5.6%-0.2%,and salinity 8.86%-0.72%.Though these results show improved condition as compared to last few years,in long term,the lake is desiccating.During the UN Decade of Ecosystem Restoration(2021-2030),if these causes are not addressed,the ecosystem may completely dry up.

Check dam extraction from remote sensing images using deep learning and geospatial analysis:A case study in the Yanhe River Basin of the Loess Plateau,China

Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams.Thus,this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis.First,we made a sample dataset of check dams using GaoFen-2(GF-2)and Google Earth images.Next,we evaluated five popular deep-learning-based object detectors,including Faster R-CNN,You Only Look Once(version 3)(YOLOv3),Cascade R-CNN,YOLOX,and VarifocalNet(VFNet),to identify the best one for check dam detection.Finally,we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results.Precision,recall,average precision at intersection over union(IoU)threshold of 0.50(AP_(50)),IoU threshold of 0.75(AP_(75)),and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step(AP_(50-95)),and inference time were used to evaluate model performance.All the five deep learning networks could identify check dams quickly and accurately,with AP_(50-95),AP_(50),and AP_(75)values higher than 60.0%,90.0%,and 70.0%,respectively,except for YOLOv3.The VFNet had the best performance,followed by YOLOX.The proposed framework was tested in the Yanhe River Basin and yielded promising results,with a recall rate of 87.0%for 521 check dams.Furthermore,the geographic analysis deleted about 50%of the false detection boxes,increasing the identification accuracy of check dams from 78.6%to 87.6%.Simultaneously,this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets.The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.

Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021

In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.

Kenyan Counties Geospatial Data Knowledge to Monitor Crop Production

Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.

Leveraging Geospatial Technology for Smallholder Farmer Credit Scoring

According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.

Geospatial-Based Analysis of Balance in Elementary Education—Taking Elementary Schools in Changsha Five Core Districts as Examples

The balanced development of the elementary education sector has been a long goal pursued by the education departments of various places, and is also an outcome expected by the people. Based on a study of the equilibrium of the spatial distribution of the capacity saturation models of all the primary schools in Changsha’s (China) five core districts, the results show that the overall geographical distribution of the primary schools in Changsha is relatively balanced, based on the natural characteristics of Changsha, such as human geography, and the moderate gradient between the central urban area and the primary schools in the suburbs and outer suburbs. Then the Theil index model was introduced, and the results of the model analysis show that the differences between elementary schools in Changsha urban area are relatively small, and the main differences originate from between districts rather than within districts, and subtle differences among regions mainly reflect in the teacher strength.

A Data-Intensive FLAC^3D Computation Model:Application of Geospatial Big Data to Predict Mining Induced Subsidence

Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.

Earthquake risk assessment in NE India using deep learning and geospatial analysis

Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and industries.Traditionally,the risk assessment approaches have used various traditional and machine learning models.However,deep learning techniques have been rarely tested for earthquake probability mapping.Therefore,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE India.Then conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators.Prediction classification results and intensity variation were then used for probability and hazard mapping,respectively.Finally,earthquake risk map was produced by multiplying hazard,vulnerability,and coping capacity.The vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster management.The CNN model for a probability distribution is a robust technique that provides good accuracy.Results show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of 0.91.These indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.

Designing Thought,Technical Line and Some Theoretical Issues on Geospatial Digital Framework of China

Based on the developing tendency of present China’s basic GIS,this paper discusses the designing idea for scales of 1∶10 000,1∶50 000, 1∶250 000 and 1∶1 000 000 pyramid_like multi_layer and multi_resolution of the basic GIS.A technical line for the construction of basic GIS of the whole country and various provinces for sustainable development is put forward.And some important theoretical GIS issues touched by the technical process are discussed.

Statistical and Geospatial Assessment of Groundwater Quality in the Megacity of Karachi

Inserting Groundwater quality variability and sources potentially contributing to aquifer recharge was evaluated in metropolitan Karachi. Selected sampling sites were characterized by large waste dumping sites, industrial zones, and the presence of open streams receiving heavy loads of industrial and domestic wastes. Levels of pH, electrical conductivity (EC), fluoride (F-), chloride (Cl-), bromide (Br-), nitrate-N (NO-3-N), sulfate (SO2-4), sodium (Na+), potassium (K+), calcium (Ca2+), magnesium (Mg2+), and ammonium (NH+4) were determined and compared with the WHO permissible limits. Concentrations of the measured ions were in the order of Cl- > Na+ > SO2-4 > Mg2+ > Ca2+ > NO-3-N > K+ > F- > Br-. EC values were above the WHO guidelines, representing the presence of high ionic concentration in the groundwater. The health risk index (HRI) for indicated that inhabitants of Karachi are at risk of high exposure. Ingestion of high concentrations of NO-3-N in water can cause methemoglobinemia and birth defects. Results of multivariate statistical analysis, principal component analysis (PCA), cluster analysis (CA), and geographic information system (GIS) map analysis revealed that human activities are leading to adverse effects on the existing groundwater quality in Karachi.

Geospatial Analytics for COVID-19 Active Case Detection

Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.

A Geospatial Analysis of Wetland Cultivated Areas in Ile-lfe, Osun State, Nigeria

Wetlands are among the world＇s most productive environment. They are cradles of bio-diversity, providing the water and primary productivity upon which large numbers of plant and animal species depend for survival. Unfortunately, they are also among the world＇s most threatened ecosystems, owing mainly to continued drainage, urbanization, pollution, over-exploitation or other unsustainable uses of their resources. The lack of baseline wetland inventory and limited accessibility to the available ones have been identified as major limitations for sustainable use and management of wetland resources. This study therefore utilized multispectral remote sensing data and global positioning system （GPS） for mapping and assessing the spatial pattern of wetlands, particularly the cultivated ones. The images were processed using ILWIS 3.2 Academic. The combined digital image processing and visual image interpretation were used to identify and segment wetlands in the image data. The coordinates of all identified wetlands and various anthropogenic activities on them were taken using GPS. Questionnaire forms were designed and randomly administered on the households residing around the wetland areas to enable the study to assess the anthropogenic factors, which are capable of destroying the ecosystem. The result indicated that the original size of wetlands in the study area has been modified as there has been reduction in their area extent. Various anthropogenic factors such as, the conversion of wetlands to agricultural, residential and commercial land uses were majorly responsible for the modification. In conclusion, remote sensing data and GIS technology were found useful in mapping and assessing wetlands for sustainable use.

Geospatial Mapping of Fisheries Profile of West Bengal of India through GIS

In the age of information and technological advancements, location-awareness is becoming a key feature in management of natural resources. Geospatial mapping is a location based study and is a part of intelligence GIS which is expected to be useful tool for fisheries scientists, aquatic resource managers and policy planners in developing and planning strategies for fisheries resources of the country. In this context, a study was performed where mapping of fisheries profile of West Bengal was carried out using GIS tool having critical geographic dimensions. For this purpose, at the core of the system fisheries data of West Bengal were accessed and integrated from different sources at district level. Data were tabulated using Microsoft Excel and then joined to digitize Map of West Bengal to enable mapping using Arc info 9.3 GIS software. This was further synchronized and integrated to generate four thematic maps based on different criteria. The map dealing with fisher folk population and their occupation contains the searchable criteria as regards to the fishermen population as well as their classified categories according to their occupation. The map dealing with West Bengal fish production contains fish production, fish seed production district-wise and growth rate for 2004-2008. The third map contains district-wise water resources and reservoir areas along with brackish water. With this mapped information, planners and various stakeholders will have readily accessible district level data on various components of fisheries of West Bengal, thereby facilitating better planning, management and development of the fisheries sector.

Geospatial Evaluation for Ecological Watershed Management: A Case Study of Some Chesapeake Bay Sub-Watersheds in Maryland USA

Geospatial technology is increasingly being used for various applications in environmental management as the need for sustainable development becomes more evident in today’s rapidly-developing world. As a decision tool, Geographic Information system (GIS) and Global positioning System (GPS) can support major decisions dealing with natural phenomena distributed in space and time. Such is the case for land use/cover known to impact ecosystems health in very direct ways. Our study examined one such application in managing land use of some sub-watersheds in the eastern Shore of Maryland, USA. We conducted a 20-year historical land use/cover evaluation using Landsat-TM remotely sensed images and GIS analysis and water monitoring data acquired during the period by Maryland Department of Natural Resources, including sewage discharge of some municipalities in the area. The results not only showed general trends in land use patterns, but also detailed dynamics of land use-land cover classes, impact on water quality, as well as other useful information for guiding both terrestrial and aquatic ecosystems management decisions of the sub-watersheds. The use of this technology for evaluating trends in land use/cover on a decade-by-decade basis is recommended as standard practice for managing ecosystem health on a sustainable basis.