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10 pages, 2106 KB

Open AccessProceeding Paper

Diachronic Analysis of Agro-Forestry Landscape in Latium Region

by Beatrice Petti, Marco Ottaviano, Claudio Di Giovannantonio, Massimo Paolanti, Cherubino Zarlenga and Marco Marchetti

Environ. Earth Sci. Proc. 2025, 36(1), 1; https://doi.org/10.3390/eesp2025036001 - 7 Nov 2025

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Abstract

Despite the growing demand for agricultural products, land abandonment is increasing in developed countries, leading to the recolonization of natural vegetation and affecting ecosystem services, biodiversity, and the economy. Understanding the drivers of land abandonment is crucial for the protection of historic rural [...] Read more.

Despite the growing demand for agricultural products, land abandonment is increasing in developed countries, leading to the recolonization of natural vegetation and affecting ecosystem services, biodiversity, and the economy. Understanding the drivers of land abandonment is crucial for the protection of historic rural landscapes. This study assessed land use in the Latium region during the mid-twentieth century, analyzing the transitions of agro-forestry landscapes starting from areas that are now classified as natural and semi-natural formations. The analysis revealed that much of today’s wilderness derives from agricultural land, mostly arable land, and complex cultivation patterns. Extensive grasslands, once widespread, have largely transitioned into woodland or shrubland, with significant impacts. The resulting simplification of the landscape contributes to agro-biodiversity loss and a decline in ecosystem services, presenting major challenges for meeting future habitat restoration targets set by environmental policies. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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49 pages, 653 KB

Open AccessConference Report

Abstracts of the 2nd International Electronic Conference on Land

by Hossein Azadi

Environ. Earth Sci. Proc. 2025, 36(1), 2; https://doi.org/10.3390/eesp2025036002 - 13 Nov 2025

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Abstract

Sk [...] Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

9 pages, 2358 KB

Open AccessProceeding Paper

Generation of Synthetic Hyperspectral Image Cube for Mapping Soil Organic Carbon Using Proximal Remote Sensing

by Rajan G. Rejith, Rabi N. Sahoo, Tarun Kondraju, Amrita Bhandari, Rajeev Ranjan and Ali Moursy

Environ. Earth Sci. Proc. 2025, 36(1), 3; https://doi.org/10.3390/eesp2025036003 - 18 Nov 2025

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Abstract

The advent of hyperspectral remote sensing represented a breakthrough in the accurate, fast, and non-invasive estimation of important soil fertility parameters. The present study utilizes non-imaging hyperspectral data in the spectral range of 350–2500 nm for estimating soil organic carbon (SOC) content. When [...] Read more.

The advent of hyperspectral remote sensing represented a breakthrough in the accurate, fast, and non-invasive estimation of important soil fertility parameters. The present study utilizes non-imaging hyperspectral data in the spectral range of 350–2500 nm for estimating soil organic carbon (SOC) content. When partial least squares (PLS) scores were taken as independent variables, support vector machine (SVM) outperformed artificial neural network (ANN) and partial least squares regression (PLSR), achieving an R² value of 0.83. After pre-processing, the proximal spectral values were spatially interpolated to construct a synthetic hyperspectral image of the experimental fields. By applying the regression model to this synthetic hyperspectral imagery, a high-resolution SOC map showing the variability of organic carbon content in the soil was generated. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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7 pages, 2224 KB

Open AccessProceeding Paper

Temporal Analysis of Groundwater Quality in the Harran Plain: Linking Land Use Change to Water Contamination (2005–2025)

by Benan Yazici Karabulut and Abdullah İzzeddin Karabulut

Environ. Earth Sci. Proc. 2025, 36(1), 4; https://doi.org/10.3390/eesp2025036004 - 18 Nov 2025

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Abstract

This study evaluates groundwater quality dynamics in the Harran Plain (∼1500 km²), a key agricultural zone within Türkiye’s Southeastern Anatolia Project (GAP). Satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used to assess land-use changes over the years [...] Read more.

This study evaluates groundwater quality dynamics in the Harran Plain (∼1500 km²), a key agricultural zone within Türkiye’s Southeastern Anatolia Project (GAP). Satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used to assess land-use changes over the years 1990, 2000, 2010, and 2020, with the GIS employed for classification and analysis. In this study, groundwater samples collected from twenty different locations in 2005, 2015 and 2025 were analyzed. For each sample, pH, EC, and various ion concentrations (Na, K, Cl, SO₄, NO₃, Ca, Mg, HCO₃) were measured. All analyses were performed using standard hydrogeochemical methods. Data from 20 wells (2005–2015) revealed significant reductions in EC (8235 to 2510 µS/cm) and NO₃⁻ (720 to 327 mg/L), due to drainage systems, improved irrigation, and fertilizer management. Nonetheless, localized pollution persisted. Land-use shifts toward high-value crops improved water efficiency, while urban and industrial expansion introduced new pressures. Results emphasize integrated water–land policies for sustainable groundwater management in arid agroecosystems. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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12 pages, 1770 KB

Open AccessProceeding Paper

Assessing Industrial Land Suitability for Sustainable Urban Planning in Dhaka Region Using Geospatial Techniques

by Sk. Tanjim Jaman Supto, Dewan Reza Hamid Karzai and Ettahad Islam Adib

Environ. Earth Sci. Proc. 2025, 36(1), 5; https://doi.org/10.3390/eesp2025036005 - 19 Nov 2025

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Abstract

The Dhaka District is experiencing rapid industrial growth alongside uncontrolled urban expansion, leading to significant land-use conflicts and environmental pressures. This study investigates how to identify the optimal sites for industrial development that support sustainable urban growth by leveraging Geographic Information Systems (GISs), [...] Read more.

The Dhaka District is experiencing rapid industrial growth alongside uncontrolled urban expansion, leading to significant land-use conflicts and environmental pressures. This study investigates how to identify the optimal sites for industrial development that support sustainable urban growth by leveraging Geographic Information Systems (GISs), combined with a structured decision-making approach. The analysis incorporates key environmental and infrastructural factors to guide responsible planning aligned with global sustainability objectives. This study integrates spatial variables such as transport accessibility, land use, environmental sensitivity, and infrastructure presence. Up-to-date satellite imagery and land-use information from recent years ensure relevant and precise analysis. The findings indicate that roughly 10–15% of Dhaka District is suitable for industrial activities, predominantly the western and northwestern edges of the district. However, a considerable portion of existing industries are situated outside the officially designated zones, with nearly 9% infringing on protected environments, pointing to gaps in land management policies. Additionally, industrial expansion resulted in the conversion of over thousands of hectares of natural land, underscoring urgent ecological concerns. Scenario modeling further demonstrates how strategic land allocation can balance industrial growth with environmental conservation. This research highlights the value of integrating a GIS with multi-criteria evaluation using Analytical Hierarchy Process (AHP) to provide a flexible, data-driven framework for sustainable industrial land-use planning. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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8 pages, 1367 KB

Open AccessProceeding Paper

Wildfire Damage Assessment over Eaton Canyon, California, Using Radar and Multispectral Datasets from Sentinel Satellites and Machine Learning Methods

by Jacques Bernice Ngoua Ndong Avele and Viktor Sergeevich Goryainov

Environ. Earth Sci. Proc. 2025, 36(1), 6; https://doi.org/10.3390/eesp2025036006 - 20 Nov 2025

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Abstract

Eaton Canyon in California serves as the focal point for a comprehensive post-wildfire ecological impact assessment. This study employs an approach integrating satellite imagery from the European Space Agency’s Sentinel constellation to study an area of 271.49 km². The data encompasses [...] Read more.

Eaton Canyon in California serves as the focal point for a comprehensive post-wildfire ecological impact assessment. This study employs an approach integrating satellite imagery from the European Space Agency’s Sentinel constellation to study an area of 271.49 km². The data encompasses both radar and multispectral data, offering a multi-dimensional view of the affected landscape. The analysis leverages the power of the random forest algorithm. Firstly, three widely used indices—the difference normalized burn ratio (dNBR), relative burn ratio (RBR), and relative difference normalized burn ratio (RdNBR)—were calculated and compared based on their accuracy and Kappa index. Secondly, we developed a fusion approach by using all the fire indices to obtain a precise severity map by classifying the affected area into distinct severity classes. Thirdly, a separate fusion approach was developed utilizing the normalized difference vegetation index (NDVI), radar vegetation index (RVI), and modified normalized difference vegetation index (MNDVI) to analyze the distribution of vegetation before and after the wildfire. The merger proposals were developed using a combination of index values to obtain better information on the fire severity map and post-fire vegetation distribution. The results indicated an accuracy of 78% when employing the dNBR index. A higher accuracy of 81% was observed with the RBR index, while the RdNBR demonstrated an accuracy of 95%. Our approach, which combines all fire indicators, offers optimal accuracy of 99%. A percentage of 56.76% did not burn due to the topography of the canyon creating natural firebreaks. Areas classified as low severity (7.83%) showed minimal damage with minimal tree mortality. Moderate- to low-severity areas (5.83%) represented regions with partial crown burns and some tree mortality. Moderate- to high-severity areas (7.22%) showed significant tree mortality. Finally, high-severity areas (22.36%), characterized by complete tree mortality and significant loss of vegetation cover, were largely concentrated in specific sections of the canyon, likely influenced by factors such as slope and fuel type. These findings provide valuable information for post-fire ecological recovery efforts and future land management strategies in Eaton Canyon and similar fire-prone landscapes. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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9 pages, 4799 KB

Open AccessProceeding Paper

Rainfall Runoff Simulation for Climate-Resilient Watershed Management: A Case Study of the Mangla Watershed, Pakistan

by Saffi Ur Rehman, Tingting Chang, Muhammad Zaman and Abdullah Bin Jaweed

Environ. Earth Sci. Proc. 2025, 36(1), 7; https://doi.org/10.3390/eesp2025036007 - 24 Nov 2025

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Abstract

Due to climate change, runoff simulations and understanding the relationship between rainfall and runoff are crucial for watershed management. This study combined a Geographic Information System (GIS) and the Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) to simulate rainfall-based runoff for the Mangla Watershed. [...] Read more.

Due to climate change, runoff simulations and understanding the relationship between rainfall and runoff are crucial for watershed management. This study combined a Geographic Information System (GIS) and the Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) to simulate rainfall-based runoff for the Mangla Watershed. We used freely available satellite-based topography, soil and land use and land cover data, along with daily rainfall and discharge data for the hydrological modeling. For model generation, key parameters include the Curve Number method, the Unit Hydrograph method, the recession baseflow method, and the Muskingum routing method. The model was manually calibrated from 1991 to 2000 and validated from 2001 to 2010 and a sensitivity analysis was performed to check the model behavior and hydrological response of the watershed upon changing model parameters. The model’s efficiency was tested based on its statistical parameters, like the root mean square error (RMSE), standard deviation, Percent Bias, and Nash–Sutcliffe Efficiency. The Nash–Sutcliffe Efficiency for calibration and validation was 0.919 and 0.945, respectively. The findings demonstrate that HEC-HMS is an effective tool for rainfall-based runoff modeling in the Mangla Watershed and providing valuable insights for flood risk management and climate-resilient planning by using hydrological modeling to predict runoff dynamics, optimize reservoir operations, and inform adaptive strategies for managing water resources under changing climate conditions. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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16 pages, 25819 KB

Open AccessProceeding Paper

Conservancies: A Demonstrable Local-Level Action for the Sustainable Development Goals in an African Indigenous Frontier

by Alexander Omondi Imbo, Uta Wehn and Kenneth Irvine

Environ. Earth Sci. Proc. 2025, 36(1), 8; https://doi.org/10.3390/eesp2025036008 - 25 Nov 2025

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Abstract

This paper examines an approach to local-level community action for the global Sustainable Development Goals (SDGs), amid the growing importance of context-specific implementations to accelerate progress. Land-use governance is critical for contributions to the SDGs, as it shapes a wide range of environmental, [...] Read more.

This paper examines an approach to local-level community action for the global Sustainable Development Goals (SDGs), amid the growing importance of context-specific implementations to accelerate progress. Land-use governance is critical for contributions to the SDGs, as it shapes a wide range of environmental, social, and economic outcomes. Wildlife conservancies provide an innovative community-driven land-stewardship model that has proliferated across rangelands in various African countries as a sustainable development strategy. This study explores the potential contribution and capacity of conservancies, as a form of land-use governance, in advancing the SDGs at local levels. Using case studies from Kenya’s Maasai Mara, the research draws on qualitative primary data collected through in-depth interviews, a focus group discussion, observation, and document review, supplemented by secondary data obtained from a literature review. The data was analyzed thematically. The results show that conservancies address key socio-ecological challenges corresponding with multiple SDGs, particularly those related to poverty reduction, food security, climate action, and life on land. However, significant segments of local communities remain marginalized in decision making and benefit sharing, a situation rooted in pre-existing social hierarchies and weak governance institutions, raising concerns about social justice. Other major limitations are related to the conservancies’ over-reliance on tourism, and local people’s high dependence on natural resources. To resolve these limitations, the study recommends improving local governance via institutional strengthening, capacity building, gender empowerment, and stakeholder partnerships; diversifying income sources to reduce financial vulnerability; and adopting strategies to alleviate high dependence on natural resources in the long term. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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13 pages, 4244 KB

Open AccessProceeding Paper

Soil Moisture Mapping Using Sentinel-1 SAR Data and Cloud-Based Regression Modeling on Google Earth Engine

by Tarun Teja Kondraju, Selvaprakash Ramalingam, Rajan G. Rejith, Amrita Bhandari, Rabi N. Sahoo and Rajeev Ranjan

Environ. Earth Sci. Proc. 2025, 36(1), 9; https://doi.org/10.3390/eesp2025036009 - 27 Nov 2025

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Abstract

Soil moisture is an essential environmental parameter affecting hydrological cycles, agricultural productivity, and climate systems. Conventional in situ measurements are precise but do not provide the spatiotemporal coverage for large applications. This research provides an extensive framework for estimating and mapping surface soil [...] Read more.

Soil moisture is an essential environmental parameter affecting hydrological cycles, agricultural productivity, and climate systems. Conventional in situ measurements are precise but do not provide the spatiotemporal coverage for large applications. This research provides an extensive framework for estimating and mapping surface soil moisture by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with machine learning in the Google Earth Engine (GEE) cloud platform. The study area is the agricultural region of Perambalur district in Tamil Nadu State, India. The research took place between September 2018 and January 2019. The dual-polarized (VV and VH) Sentinel-1 C-band images were collected in tandem with ground truth soil moisture data collected through the gravimetric method. A set of SAR indices and engineered features were extracted from the backscattering coefficients (σ°). A random forest (RF) machine learning model was used in this study to estimate soil moisture. The RF model incorporating the complete set of engineered features showed a coefficient of determination (R²) of 0.694 and a root mean square error (RMSE) of 1.823 (Soil moisture %). The complete processing and modeling workflow was encapsulated in the GEE-based software tool (version 1) providing an accessible, user-friendly platform for generating near-real-time maps of soil moisture. This research proves that the combination of Sentinel-1 data with clever machine-learning algorithms in the GEE cloud platform provides a scalable, efficient, and potent tool for operational soil moisture mapping serving applications in precision agriculture and in the management of the water resource. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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5 pages, 484 KB

Open AccessProceeding Paper

Immediate and Residual Effects of Cattle Corralling and Mineral Fertilizer in Maize Cropping Systems in the Sub-Humid Zone of Northern Benin: Yields, Resource-Use Efficiency, Economic Profitability, and Post-Harvest Soil Fertility

by Pierre G. Tovihoudji, Sourogou Anasse Gounou, Mouiz W. I. A. Yessoufou, Sissou Zakari, André Adjogboto and P. B. Irénikatché Akponikpè

Environ. Earth Sci. Proc. 2025, 36(1), 10; https://doi.org/10.3390/eesp2025036010 - 9 Dec 2025

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Abstract

Effective management of organic and inorganic fertilizers is vital for sustaining productivity in intensive cropping systems. This four-year study (2012–2015) assessed the immediate and residual effects of cattle corralling combined with mineral fertilizer on maize in northern Benin using a strip-plot design with [...] Read more.

Effective management of organic and inorganic fertilizers is vital for sustaining productivity in intensive cropping systems. This four-year study (2012–2015) assessed the immediate and residual effects of cattle corralling combined with mineral fertilizer on maize in northern Benin using a strip-plot design with five corralling levels No corralling(NM), immediate application (C0) and residual effects one (C1), two (C2), and three (C3) years after the initial corralling and three fertilizer rates F0 (no fertilizer), F1 (50% of the recommended rate) and F2 (the recommended rate). Cattle corralling doubled maize yield from 2.0 to 4.0 t ha⁻¹ and increased net profitability from 384 to 1000 USD ha⁻¹ compared to non-manured plots. Water-use efficiency increased from 3.4 to 6.8 kg ha⁻¹ mm⁻¹, and soil organic carbon increased nearly fourfold (3.0 to 11.2 g kg⁻¹). Residual effects declined over time without mineral inputs (C0 > C1 > C2 > C3 > NM); however, these benefits were sustained or enhanced when combined with fertilizer (C3 > C2 > C1 > C0 > NM). Fertilizer responses were minor in C0 and C1 but significant in C2 and C3, demonstrating a strong organic–inorganic synergy. Nutrient recovery efficiency was initially lower in recently corralled plots but surpassed non-manured levels after two years. These results confirm that integrating livestock corralling with optimized fertilizer use strengthens soil fertility, resource efficiency, and profitability, providing a sustainable intensification pathway for maize systems in sub-humid, low-fertility regions. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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10 pages, 10963 KB

Open AccessProceeding Paper

The Strait Bridge as an Opportunity for Careful Strategic Architectural and Urban Regeneration

by Clara Stella Vicari Aversa and Celestina Fazia

Environ. Earth Sci. Proc. 2025, 36(1), 12; https://doi.org/10.3390/eesp2025036012 - 22 Dec 2025

Abstract

The Strait of Messina occupies a strategic position in the Mediterranean, representing an environmental and territorial peculiarity. The Strait area today is at the center of the political debate for the stable crossing project, a strategic infrastructure work for Italy and Europe. With [...] Read more.

The Strait of Messina occupies a strategic position in the Mediterranean, representing an environmental and territorial peculiarity. The Strait area today is at the center of the political debate for the stable crossing project, a strategic infrastructure work for Italy and Europe. With the Strait Bridge, territorial arrangements, sea fronts, infrastructure systems, and urban and architectural dimensions will change. It appears necessary to prepare the territories and take advantage of all the opportunities related to future scenarios. The Strait area is not only marked by the crossing, but the whole territorial and urban system—the coastal strip and inland areas—becomes an active part of the processes of territorial regeneration and development. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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7 pages, 850 KB

Open AccessProceeding Paper

Urban 3D Multiple Deep Base Change Detection by Very High-Resolution Satellite Images and Digital Surface Model

by Alireza Ebrahimi and Mahdi Hasanlou

Environ. Earth Sci. Proc. 2025, 36(1), 13; https://doi.org/10.3390/eesp2025036013 - 22 Dec 2025

Abstract

Timely and accurate urban change detection is vital for sustainable urban development, infrastructure management, and disaster response. Traditional two-dimensional approaches often overlook vertical and structural variations in dense urban areas. This study proposes a three-dimensional (3D) change detection framework that integrates high-resolution optical [...] Read more.

Timely and accurate urban change detection is vital for sustainable urban development, infrastructure management, and disaster response. Traditional two-dimensional approaches often overlook vertical and structural variations in dense urban areas. This study proposes a three-dimensional (3D) change detection framework that integrates high-resolution optical imagery and Digital Surface Models (DSMs) from two time points to capture both horizontal and vertical transformations. The method is based on a deep learning architecture combining a ResNet34 encoder with a UNet++ decoder, enabling the joint learning of spectral and elevation features. The research was carried out in two stages. First, a binary classification model was trained to detect areas of change and no-change, allowing direct comparison with conventional methods such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) with thresholding, K-Means clustering, and Random Forest classification. In the second stage, a multi-class model was developed to categorize the types of structural changes, including new building construction, complete destruction, building height increase, and height decrease. Experiments conducted on a high-resolution urban dataset demonstrated that the proposed CNN-based framework significantly outperformed traditional methods, achieving an overall accuracy of 96.58%, an F1-score of 96.58%, and a recall of 96.7%. Incorporating DSM data notably improved sensitivity to elevation-related changes. Overall, the ResNet34–UNet++ architecture offers a robust and accurate solution for 3D urban change detection, supporting more effective urban monitoring and planning. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)

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