The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.

The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions.

Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time.

Natural capital is the wealth of nations that determine their economic status. Worldwide, vulnerable people depend on natural capital for employment, salaries, wealth, and livelihoods and, in turn, this determines the developmental index of the nation to which they belong. In this short review, we have tried to sum up the ideas and discussions over natural capital’s role in ascribing economic status to countries as well as the need for natural resource management and sustainability. This paper aimed to discuss how humanity’s prosperity is intertwined with the services that ecosystems provide, and how poor natural resource management (NRM) has adversely affected human well-being. Our preselected criteria for the review paper led us to evaluate 96 peer-reviewed publications from the SCOPUS database, which is likely the most comprehensive archive of peer-reviewed scientific literature as well as WoS, PUBMED, and Google Scholar databases. Our review revealed that the availability of ecological services is crucial for clean water and air, food and fodder, and agricultural development. We further discussed important concepts regarding sustainability, natural capital and economics, and determinants of human well-being vis-à-vis the intergenerational security of natural wealth. To ensure current and future human well-being, we conclude that an in-depth understanding of the services that ecosystems provide is necessary for the holistic management of the Earth system.

Urban floods are very destructive and have significant socioeconomic repercussions in regions with a common flooding prevalence. Various researchers have laid down numerous approaches for analyzing the evolution of floods and their consequences. One primary goal of such approaches is to identify the areas vulnerable to floods for risk reduction and management purposes. The present paper proposes an integrated remote sensing, geographic information system (GIS), and field survey-based approach for identifying and predicting urban flood-prone areas. The work is unique in theory since the methodology proposed finds application in urban areas wherein the cause of flooding, in addition to heavy rainfall, is also the inefficient urban drainage system. The work has been carried out in Delhi’s Yamuna River National Capital Territory (NCT) area, considered one of India’s most frequently flooded urban centers, to analyze the causes of its flooding and supplement the existing forecasting models. Research is based on an integrated strategy to evaluate and map the highest flood boundary and identify the area affected along the Yamuna River NCT of Delhi. In addition to understanding the causal factors behind frequent flooding in the area, using field-based information, we developed a GIS model to help authorities to manage the floods using catchment precipitation and gauge level relationship. The identification of areas susceptible to floods shall act as an early warning tool to safeguard life and property and help authorities plan in advance for the eventuality of such an event in the study area.

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Urbanization is an unavoidable process of social and economic growth in modern times. However, the speed with which urbanization is taking place produces complex environmental changes. It has affected the surface albedo and roughness of the soil, thereby modulating hydrological and ecological systems, which in turn has affected regional and local climate systems. In developing countries of South Asia, rampant and unplanned urbanization has created a complex system of adverse environmental scenarios. Similar is the case in India. The state of the urban environment across India is degrading so quickly that the long-term sustainability of its cities is endangered. Many metropolitan cities in India are witnessing the harmful impacts of urbanization on their land ecology. In this context, remote sensing and geographic information system (GIS) based assessments provide a comprehensive and effective analysis of the rate and the impact of urbanization. The present study focuses on understanding the spatiotemporal characteristics of urban growth and its implications on the geomorphology of the Panchkula District, Haryana, one of the fastest-growing urban centers in India. The study links the changes in land use/land cover (LULC) with the changing geomorphology of the study area using satellite remote sensing and GIS. The results showed that between 1980 and 2020, agricultural (+73.71%), built-up (+84.66%), and forest (+4.07%) classes of land increased in contrast to that of the fallow land (−76.80%) and riverbed (−50.86%) classes that have decreased in spatial extents. It has been observed that the hill geomorphological class had decreased in the area owing to conversion to industrial and built-up activities. Assessment of the environmental quality of cities involves multiple disciplines that call for a significant amount of scientific evaluation and strong decision making, and the present study shall lay down the baseline analysis of the impact of changing LULC on the geomorphological setup of the selected urban center.

Excavation of clay soil is one of the most important economic branches in the northern part of Croatia. The impact of clay soil in Croatia compared to the global exploitation fields of clay soil is negligible. Modern methods of clay excavation during winter months due to negligible amounts are not profitable. Therefore, it is important to optimize clay soil excavation throughout the year to increase the efficiency of exploitation and increase profits. In the case of large amounts of precipitation (rain), clay absorbs water and becomes grain. For this reason, access to the exploitation field and excavation itself becomes impossible. Air temperature also plays an important role in excavation. Long-lasting low air temperatures below 0 °C during the winter months result in clay frost. As a result, excavation cannot occur at that time. The paper describes a new method of modeling the precipitation and air temperature on the exploitation fields of clay in Northwest Croatia on the exploitation fields of Cukavec and Cukavec II. The method involves the calculation of the drought index and use of the rescaled adjusted partial sums (RAPS) statistical method and its application on a time series of total daily precipitation and average daily temperatures as a climatic indicator of any observed area. Using this process, it is possible to determine the time period of the year when clay soil can be excavated.