Biophysical Indicators Research Articles

Urban Heat Island and Environmental Degradation Analysis Utilizing a Remote Sensing Technique in Rapidly Urbanizing South Asian Cities

Rapid urbanization in South Asian cities has triggered significant changes in land use and land cover (LULC), degrading natural biophysical components and intensifying urban heat islands (UHIs). This study investigated the impact of LULC changes on land surface temperature (LST) and the role of biophysical indicators in enhancing urban resilience to thermal extremes. We used Landsat satellite imageries from 1993 to 2023, conducted a comprehensive analysis of LULC changes, and estimated LST variations at 6-year intervals in the Dhaka, Gazipur, and Narayanganj districts in Bangladesh. Afterward, we performed statistical analysis upon employing correlation, regression, and principal component analysis (PCA) techniques to summarize information. The results reveal that 339.13 km2 worth of urban expansion has occurred in last 30 years, with an average annual growth rate of 3.5%, accompanied by a substantial reduction in water bodies (−139.17 km2) and vegetation cover. Consequently, summer temperatures exceeded approximately 36.52 °C in dense urban areas. Also, the results highlighted the strong influence of built-up areas (BSI and SAVI) on LST, while vegetation (NDVI) and water indices (NDWI) exhibited a negative association. The findings emphasize the urgency of integrating green infrastructure and deploying sustainable urban planning policies to mitigate the potential adverse impacts of scattered urbanization in the face of climate change.

Сравнительный анализ методов диагностики состояния органа дыхания у экспериментальных животных

Summary. The purpose of the study is to comparison of methods for diagnosing the state of the respiratory organ of experimental animals under the influence of physical factors, the intensity and exposure times of which are close to extreme. Materials and methods of research. The experiments used 45 male rabbits of the “Soviet Chinchilla” breed with a body weight of 2,7-3,4 kg, randomly divided into a control group, a group exposed to the lungs, a group exposed to the hearing organ and a group exposed to thermal effects on different areas of skin. To differentiate structural and functional disorders of the external respiratory system, three methods for diagnosing the state of the respiratory organ of experimental animals (rabbits) were used: acoustic impedance measurement of the respiratory tract, analysis of biophysical parameters of the lungs and pathological examination. A general conclusion about the state of the respiratory organ was given on the basis of a comparative statistical analysis of the characteristics of animals in the experimental and control groups. The relative size of the area of hemorrhages on the pleural surface of the lungs was used as a criterion indicator. The results of the study and their analysis. It has been shown that methods for diagnosing the state of the respiratory system based on impedance, biophysical indicators and the results of a pathological examination complement each other, providing an objective diagnosis of the state of the respiratory organ of experimental animals under the influence of physical factors, the intensity and exposure times of which are close to extreme, and are comparable in objectivity (sensitivity and diagnostic specificity) with methods used in medical clinical practice (impulse oscillometry).

Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve

This present study aims to evaluate land use and land cover changes using five machine-learning algorithms in Google Earth Engine. The performance of these machine learning algorithms was evaluated using user accuracy, producer accuracy, overall accuracy, and kappa coefficient. Additionally, it seeks to understand the evolving pattern of land surface temperature and its correlation with biophysical characteristics in the Brahmani-Dwarka interfluve region from 1991 to 2021. The results demonstrate that RF algorithms outperform other algorithms in terms of performance, with RF algorithms averaging an 86 % overall accuracy and a Kappa coefficient of 0.82, while GTB comes in second with an 85 % overall accuracy and a Kappa value of 0.81. SVM performed moderately, while CART and MD algorithms struggled to perform in this study. The analysis of land transformation from 1991 to 2021 indicates that the stone crushing industry, built-up, and waterbodies have shown an upward trend, while vegetation and fallow land have decreased in their geographical extent. The results of the LST analysis indicated the study area had an LST rise of 8.72 °C over the last 30 years, or 0.29 °C per year, with the stone crushing and mining activities exhibiting the highest increase of 11.67 °C. Further, correlation analysis reveals LST has a highly significant positive correlation with NDBI and NDBaI and a very significant negative correlation with NDVI, MNDWI, and NDLI throughout the study period. The findings emphasize the importance of long-term planning and environmentally friendly development, ensuring responsible stone crushing activities, sustainable techniques, biodiversity conservation, and sustainable utilization of natural resources.

Mapping and assessing marine ecosystem services supply in the Baltic Sea

Coastal and marine ecosystems supply multiple Ecosystem Services (ES). Nevertheless, these ecosystems are among the most impacted by human activities, harming the ES sustainable supply. Since ES are a spatial phenomenon, mapping can contribute to understand ES supply. For this, we use quantitative spatio-temporal frameworks to map and assess the supply of one provisioning (food from fisheries) and two regulating ES (nursery habitats and nutrient regulation), considering two periods: Baltic Sea Holistic Assessment (HOLAS) 2 (2011–2016) and 3 (2016–2021). The ES supply was assessed following a process-based modelling approach, using bio-physical indicators as proxies. The three ES models were applied and validated, showing moderate results. For fisheries and nursery ES the results showed a significantly higher supply in HOLAS 3 than in 2, and for nutrient ES the opposite. This indicates that the assessed ES changed due to environmental activities. The Anselin Local Moran's results showed that most ES index values aggregate in the High-High cluster; Moran's I and semi-variogram results showed a clustered pattern; and the Getis Ord* analysis showed that hot and cold spots corresponded to high and low supply areas. For fisheries, high ES supply areas were located in the central-southern part of the Baltic Sea, while low-supply regions were located in the northern part. For nursery ES, high supply areas were located in the southwestern Finnish and western Estonian coasts. For nutrient ES, high supply areas occurred in the central- and eastern-southern parts close to the coast. Correlations showed a statistically significant negative correlation between fisheries and nursery ES and a significant positive correlation between fisheries and nutrient ES. No statistically significant correlations were observed between nursery and nutrient ES supply. The results obtained are essential to support coastal and marine management and planning in the Baltic Sea as well as international environmental policies and directives.

Advanced remote sensing technologies for tracking landscape changes and environmental conditions

The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract biophysical indices like leaf area index and canopy chemistry from satellite imagery. Long-term remote sensing archives (e.g., Landsat, AVHRR) facilitate retrospective studies of landscape changes and trajectories. Recent advancements in sensors and analysis techniques, such as sub-pixel classifications and continuous fields, have improved the accuracy of variable retrieval (e.g., Albedo, chlorophyll concentration). These developments enable powerful monitoring tools for land use/cover change detection, leading to a better understanding of landscape dynamics and the mapping of previously unexplored features. However, a trade-off exists between high spatial and high temporal resolution depending on the platform used.

Size Matters: Rethinking Hertz Model Interpretation for Cell Mechanics Using AFM.

Cell mechanics are a biophysical indicator of cell state, such as cancer metastasis, leukocyte activation, and cell cycle progression. Atomic force microscopy (AFM) is a widely used technique to measure cell mechanics, where the Young modulus of a cell is usually derived from the Hertz contact model. However, the Hertz model assumes that the cell is an elastic, isotropic, and homogeneous material and that the indentation is small compared to the cell size. These assumptions neglect the effects of the cytoskeleton, cell size and shape, and cell environment on cell deformation. In this study, we investigated the influence of cell size on the estimated Young's modulus using liposomes as cell models. Liposomes were prepared with different sizes and filled with phosphate buffered saline (PBS) or hyaluronic acid (HA) to mimic the cytoplasm. AFM was used to obtain the force indentation curves and fit them to the Hertz model. We found that the larger the liposome, the lower the estimated Young's modulus for both PBS-filled and HA-filled liposomes. This suggests that the Young modulus obtained from the Hertz model is not only a property of the cell material but also depends on the cell dimensions. Therefore, when comparing or interpreting cell mechanics using the Hertz model, it is essential to account for cell size.

Implementation of a predictive strategy in the diagnosis of inflammatory periodontal diseases

Relevance. The diagnosis of periodontal diseases, considering their severity, prevalence, progression, and staging, can be achieved by determining the levels of biomarkers or molecular imaging biomarkers in biofluids such as crevicular or sulcular fluid (GCF or GSF), saliva, and oral fluid. GCF is currently regarded as one of the diagnostically significant biological fluids for assessing the condition of periodontal tissues, not only in clinical diagnostic laboratories but also in dental offices. The implementation of sensitive, highly accurate, non-invasive, and specific methods for rapid GCF diagnosis, based on the qualitative analysis of biomarkers of cytokine imbalance, immunological disorders, changes in non-specific defence factors, and biophysical indicators, will allow for an objective assessment of the condition of periodontal tissues.Purpose. To improve the efficiency of periodontitis prevention using a developed mathematical model for personalized prediction of the course of inflammatory periodontal diseases based on the investigated biomarkers in GCF.Material and methods. The study included 101 patients: Group I consisted of 22 patients diagnosed with K05.10 (gingivitis), Group II included 31 patients diagnosed with K05.31 (mild periodontitis), and Group III comprised 18 patients diagnosed with K05.31 (moderate periodontitis). The comparison group consisted of 30 individuals with clinically healthy periodontium. All subjects underwent clinical and instrumental examination, determination of periodontal indices, GCF collection, and quantitative analysis of immune regulatory mediators (IL-1β, IL-6, TNF-α, IL-8, MCP-1, IL-17, VEGF, IL-1RA).Results. The study of immune regulatory mediators confirmed the significance of increased levels of pro- and anti-inflammatory cytokines/chemokines, as well as the reduction of the anti-inflammatory biomarker IL-1RA in GCF at the early stages of inflammatory changes in periodontal tissues. This is accompanied by the appearance of signs indicating the destruction of the dentogingival junction. Using logistic regression and training a multiclass classifier based on the support vector machine method, a model was developed to predict the risk of dentogingival junction loss in patients, potentially leading to periodontitis.Conclusion. The results of logistic regression modelling and training a multiclass classifier based on the support vector machine method demonstrate that in diagnosing the initial stages of periodontal tissue damage with the loss of the dentogingival junction (DGJ), the most effective approach is the comprehensive use of inflammatory process biomarkers and the development of multi-marker algorithms based on a computer program.

LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B < 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D’IVOIRE

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained.

The impact of migration on culturally-embedded and subjective perceptions of habitability in a context of environmental change

Environmental change is increasingly challenging the habitability of places around the world, particularly with regard to resource-dependent rural areas in the Global South. Apart from objectively measurable, bio-physical indices, it is likewise important to look at individual and group-specific perceptions of habitability, which are embedded in their respective socio-cultural context(s). Migration as a well-established household risk diversification strategy has the potential to increase people’s adaptive capacity, their well-being, and can shape the way people perceive the habitability of places. This study utilizes a human-centered approach in order to unravel the impacts of migration on culturally-embedded and subjective perceptions of habitability in a rural community in Northern Ghana which faces increasing pressure of environmental changes. Based on qualitative empirical research, we utilize place attachment, social status, and community cohesion as exemplary socio-cultural dimensions with particular relevance in this specific local context to showcase 1) the subjectivity and cultural embeddedness of habitability perceptions and 2) the respective potential of migration to influence such perceptions to both positive and negative ends. Positive migration impacts on the underlying socio-cultural context(s) can serve to undergird (collective) responsibility and adaptive action towards improving local habitability in parallel to encouraging efforts that strive to maintain cultural integrity. Integrating this knowledge in future habitability assessments can pave the way for context-sensitive and locally-adjusted resilience-building strategies that take the potential benefits and disadvantages of migration into account.

Geomorphological changes and landscape ecosystem services dynamics of the Bhasan Char Island in the Northern Bay of Bengal, Bangladesh

Geomorphological changes have had numerous environmental impacts on landscape features, natural resources, landform stability, and ecosystem services on Bhasan Char Island in the Northern Bay of Bengal, Bangladesh. Therefore, the present study examines the morphological change of the island and its impacts on landscape features, landform stability, and coastal mangrove forest resources since its evolution. Optical satellite images from Landsat (TM and OLI) spanning from 2003 to 2023 have been used to assess geomorphological parameters using the bio-physical indices NDWI and MNDWI. A hybrid classification method involving unsupervised classification, supervised machine learning, and post-classification refinement was employed to precisely delineate landscape features and evaluate ecosystem services. Indeed, to evaluate forest cover health dynamics, the leaf area index (LAI) was estimated using the Google Earth Engine (GEE) cloud platform. The study results reveal that the geomorphology of the island was changed by fluvial and marine processes and responses, whereas only 3% of the landscape ages older than 19 years. Results also disclosed that built-up area construction for Rohingya nations drastically lost ∼ 7% of mangrove plantations over the last 8 years, from 2015 to 2023. The current research delivers crucial management strategies regarding further structural development by evading unplanned construction, hastening reforestation, and evolving the green belt along the existing embankment. The study findings will help policymakers, environmental managers, coastal and marine resource planners, ecologists, and geomorphologists take an eco-friendly sustainable development plan and conserve the coastal island and its landscape resources from the risk of fluvial and marine processes.

Strategies for monitoring and evaluation of climate change adaptation: localizing global approaches into Andean realities

While there is increasing progress made at the global scale on understanding climate change (CC) impacts and implementing adaptation solutions, there is still a major gap in documenting and assessing the effectiveness of monitoring and evaluation (M&E) systems for CC adaptation. This is particularly challenging if we consider the diversity of local perceptions and interpretations of what CC adaptation concretely means and how to measure it. We evaluate how global approaches on M&E to CC adaptation are being localized in the design and implementation of public policies and territorial strategies for CC adaptation in the Andes in four national and local case studies in Colombia, Ecuador, Peru and Bolivia. The analysis is based on a review of the status of national programs for M&E of CC adaptation and the implementation in those countries of the project “Adaptation to Climate Change Impacts in Water Resources in the Andes” (AICCA). The M&E systems in the four case studies differed widely in the way in which global M&E approaches are localized, reflecting the diversity of conceptions, goals, strategies and contexts in which adaptation is being implemented as well as power relations among actors and scales. Despite the diversity of implementation contexts and the complexity of monitoring the responses of socio-ecological systems, the M&E proposals share a focus on biophysical indicators over sociopolitical and institutional indicators. In addition, this study emphasizes the need to further implement participatory M&E systems from the community, to improve the territorial articulation of M&E proposals in the Andean region.

Quantifying mangrove carbon assimilation rates using UAV imagery

Mangrove forests are recognized as one of the most effective ecosystems for storing carbon. In drylands, mangroves operate at the extremes of environmental gradients and, in many instances, offer one of the few opportunities for vegetation-based sequestering of carbon. Developing accurate and reproducible methods to map carbon assimilation in mangroves not only serves to inform efforts related to natural capital accounting, but can help to motivate their protection and preservation. Remote sensing offers a means to retrieve numerous vegetation traits, many of which can be related to plant biophysical or biochemical responses. The leaf area index (LAI) is routinely employed as a biophysical indicator of health and condition. Here, we apply a linear regression model to UAV-derived multispectral data to retrieve LAI across three mangrove sites located along the coastline of the Red Sea, with estimates producing an R2 of 0.72 when compared against ground-sampled LiCOR LAI-2200C LAI data. To explore the potential of monitoring carbon assimilation within these mangrove stands, the UAV-derived LAI estimates were combined with field-measured net photosynthesis rates from a LiCOR 6400/XT, providing a first estimate of carbon assimilation in dryland mangrove systems of approximately 3000 ton C km−2 yr−1. Overall, these results advance our understanding of carbon assimilation in dryland mangroves and provide a mechanism to quantify the carbon mitigation potential of mangrove reforestation efforts.

The effects of stocking rate, residual sward height, and forage supplementation on forage production, feeding strategies, and productivity of milking dairy cows

The intensification process in Uruguayan dairies over the last 30 years has involved increases in stocking rate (SR) and individual milk production. This research aimed to compare biophysical indicators along with the associated feeding strategies for Holstein–Jersey crossbred dairy cow systems. The comparison was conducted in farmlets representing a typical Uruguayan pasture-based dairy system on a grazing area. The study spanned from 2017 to 2019, combining 1.5 or 2.0 milking cows per hectare (SR), with two different residual sward heights (RH)—low (LR) and high (HR). These combinations resulted in four treatments: 1.5 LR, 1.5 HR, 2.0 LR, and 2.0 HR. A total of 96 cows were randomly allocated to each treatment based on parity, body weight (BW), and body condition score (BCS) for the years 2017, 2018, and 2019. The response variables per hectare were analyzed using a linear mixed model, including SR, RH, their interaction effect, year, and paddock as a repeated measurement. Results show that forage production did not differ between treatments, and forage directly harvested by cows was affected by SR, as well as milk and solids productivity. An interaction effect was detected between SR and RH on milk and milk solids production where 2.0 HR was higher than 2.0 LR, but treatments on SR 1.5 were not different between them. The consumption of concentrate, forage, and conserved forage per hectare was influenced by the level of SR. However, individual milk production was not influenced by SR or RH. Dry matter intake were affected by SR where 2.0 HR had higher consumption than 1.5 LR and 1.5 HR but was not different from 2.0 LR. The concentrate DMI per cow was not different between treatments, while the conserved forage DMI per cow was affected by SR and higher for SR 2.0 than that for SR 1.5. These combinations of feed determined varying proportions of time allocated for grazing, which were influenced by the SR and RH. This research highlights different approaches to enhance the competitiveness of Uruguayan grazing systems through improvements in forage harvest.

Livelihood vulnerability of Borana pastoralists to climate change and variability in Southern Ethiopia

PurposeThe purpose of this study results and recommendations will have a paramount significance for policymakers, policy advocates, development planners and practitioners who may be in need of such information for reconsideration, evaluation and inclusion into their respective development and humanitarian programming and operational strategies. Above all, the study result has further provided the local community with viable adaptation strategies to climate-induced changes in the study area.Design/methodology/approachThis study was conducted to measure the livelihood vulnerability of Borana pastoralists to climate change and variability in southern Ethiopia. Pastoralists’ households were sampled using multistage sampling techniques. A total of 27 socio-economic and biophysical indicators were used to reflect vulnerability components: adaptive capacity, exposure and sensitivity. Principal component analysis was used to develop weights for indicators and to produce livelihood vulnerability index to classify households according to their level of vulnerability. Ordinal logistic regression was used to identify the determinants of vulnerability to climate-induced stresses.FindingsThe results showed that 24.4% of households were highly vulnerable, 60.3% were moderately vulnerable and 15.3% of households were less vulnerable to climate-induced stresses. Factor estimates of the logistic model further revealed that early warning information, bush encroachment, coping strategy, temperature, drought frequency, provision of humanitarian services and food shortage during the normal season of the year have a significant influence on vulnerability in the study area.Social implicationsThe study’s results and recommendations will be of great significance to policymakers, development planners, and practitioners who require such information for reconsideration, evaluation, and inclusion in their respective development and humanitarian program and operational strategies. Most importantly, the study’s findings have provided the local community with practical adaptation strategies to climate-induced changes in the study area.Originality/valueThe study explored pastoralist perception of climate change and variability and measured the livelihood vulnerability of pastoralists’ households to climate change and variability and finally investigated viable adaptation and coping strategies in the study area.

THE EFFECT OF PARGILINE, SPERMINE AND THEIR COMBINATIONS ON ANDROGEN-DEPENDENT AND ANDROGEN-INDEPENDENT HUMAN PROSTATE CANCER CELLS in vitro

Summary. A series of studies on enhancing the cytotoxic effect of spermine on human prostate cells by affecting the enzymes of its metabolism has been continued. Aim: to investigate effects of pargyline, spermine and their combinations the survival, ζ-potential and surface charge and cytomorphological properties of human prostate cancer cells in culture. Object and methods: studies were conducted on cell cultures: differentiated androgen-dependent LNCaP cell line and low-differentiated androgen-independent DU-145 cell line. Cell survival was determined in the trypan blue assay. The effect of the investigated compounds, both individually and in combination, on the viability and proliferative activity of cells was assessed by the colorimetric method – by staining living cells with a solution of crystal violet in methanol. Biophysical parameters (ζ-potential and surface electric charge density) were determined according to the method cellular microelectrophoresis. Cytomorphological characteristics were studied on preparations of cells grown on coverslips, fixed and stained with hematoxylin and eosin (magnification 1000×). Results: for LNCaP cells, the IC50 of pargyline was 2.2 mM, and the IC50 of spermine was 1.5 mM. For DU-145 cells, the IC50 of pargyline was 2.35 mM, the IC50 of spermine was 5.0 mM. According to biophysical indicators, spermine caused an inversion of the sign of the electric charge in cells of the LNCaP line. Pargyline did not exert such an effect, the charge remained negative, demonstrating an inverse dependence on its concentration. The combination of pargyline and spermine increased the cytotoxic effect of spermine on LNCaP cells, but did not affect the value of their ζ-potential and surface charge density, the positive sign of which, caused by the effect of spermine, was preserved. At the same time, pargyline completely eliminated spermine-induced inversion of the sign of the total surface charge of DU-145 cells, returning it to the normal negative values. Conclusions: it is shown that the differentiated androgen-dependent cells of the LNCaP line with the combined use of pargyline and spermine demonstrated an enhancement of the cytotoxic effect of spermine. At the same time, low-differentiated cells of the DU-145 line, which do not have androgen receptors, were more resistant to the effects of both pargyline and pargyline in combination with spermine. Pargyline also reversed spermine-induced inversion of the sign of the total surface charge of DU-145 cells, returning it to normal negative values. Cytomorphological changes that occurred under the influence of both pargyline alone and in its combination with spermine demonstrate mainly the apoptotic pathway of cell death in the androgen-independent DU-145 line, while in the androgen-dependent LNCaP line, a smaller proportion of cells were in a state of apoptosis.

Assessment of the success of canopy cover revegetation of former coal mine lands with Forest Canopy Density (FCD) Model in Kutai Kartanegara, East Kalimantan

Coal mining plays a vital role in Indonesia's economic growth. However, these activities negatively impact the environment. To minimize this, the Indonesian government requires ex-mining land to be reclaimed, with one of the success criteria being canopy cover. Until now, there has been no measurable method that can determine the success rate of canopy cover on reclaimed land. This research was conducted to develop a measurement method based on remote sensing data using the Forest Canopy Density (FCD) Model, which is applied in Company X, Kutai Kertanegara. The FCD Model consisted of four biophysical indices, including AVI, BSI, SI, and TI, obtained from Landsat 8 OLI TIRS imagery from 2013–2021. The Kolmogorov-Smirnov normality test was performed before testing the relationship between FCD values and canopy cover using linear regression to obtain the canopy cover success value based on the FCD value. The FCD showed an increasing trend yearly, especially in the first two years after planting. Regression analysis showed a strong relationship between FCD values and canopy cover values, with R2=0.775, and revealed that 75.35 is the FCD value threshold for a successful canopy cover in the reclamation area. This study shows that the FCD approach can be applied to determine the success rate of reclamation in post-mining areas.

Analysis of deforestation in the Matahuayo-Tahuayo sector (Loreto) using images from TM, ETM+ and OLI sensors during the period 2007-2015

This project presents a methodology to identify deforestation in the Matahuayo sector. Tahuayo, which arises from the problem of the expansion of deforestation in tropical forests from Peru. The main objective of this study is to implement a methodology that allows detecting and quantify the deforested areas in the aforementioned sector, located in the department of Loreto. 36 Landsat 5, 7 and 8 L1T level image data were used for the time 2007 - 2015 with spatial resolution of 30 m x 30 m. The implemented methodology consists of three phases: (i) The preprocessing stage, the radiometric correction was developed, the TOA reflectance was calculated and atmospheric correction to obtain the reflectance of the ground surface. (ii) In processing Spatial and temporal distribution maps were produced from different biophysical indices (NDVI, RVI) and transformations (Tasseled Cap, PCA) that constitute the compound of 18 synthetic bands and Through spectral separability we select the synthetic bands that are used as inputs in unsupervised K-means classification. (iii) In the post processing the contours were generated of deforested areas for each year of study. From the results obtained it can be indicate a reduction of extensive areas of Amazonian forests in the study sector for the period 2007-2015. Deforestation obtained in hectares per year presents a linear upward trend of 9635 ha/year.

Multidimensional assessment of agricultural drought vulnerability based on socioeconomic and biophysical indicators

Agricultural drought threatens Türkiye's food security and economy, revealing a lack of multidimensional provincial vulnerability studies. Therefore, the primary objective of this study is to evaluate Türkiye's vulnerability to drought utilizing a multidimensional strategy incorporating biophysical and socio-economic indicators. In this context, an initial step involved the development of a conceptual framework for drought vulnerability, drawing upon the Intergovernmental Panel on Climate Change recommended elements of exposure, sensitivity, and adaptive capacity. Subsequently, this conceptual framework was applied at the provincial level, encompassing parameters such as temperature, precipitation, soil characteristics, water resources, agricultural earnings, demographic traits, educational levels, innovation, insurance coverage, and road density.The Aridity Index was used as an indicator for drought exposure, representing the physical dimension of drought. Under the sensitivity component, a total of 14 variables were defined under the indicators of “soil”, “water and land resources”, “economy”, and “population”, while under the adaptive capacity component, a total of five variables were identified. The weights of these indicators and variables were determined based on expert opinions using the Analytic Hierarchy Process method. As a result, five different indices were obtained by combining the indicators: Exposure Index, Sensitivity Index, Adaptation Capacity Index, Potential Drought Impact Index, and Agricultural Drought Vulnerability Index. In addition, spatial distribution models of these indices were also generated. Research results showed that the climatic regions most exposed to drought are also the most vulnerable. The Central, Southeastern, and Eastern Anatolian regions constitute the regions where the most vulnerable provinces are concentrated spatially.

Environmental and Agro-Economic Sustainability of Olive Orchards Irrigated with Reclaimed Water under Deficit Irrigation

Increasing the economic and environmental sustainability of irrigated agriculture is a vital challenge for the Mediterranean crop production sector. This study explores the effects of the adoption of reclaimed water (RW) as source of irrigation in conjunction with the application of deficit irrigation strategies in an olive orchard (different genotypes) located within the “Valle dei Margi” farmhouse (Eastern Sicily). Specifically, the RW was obtained in situ by treating the wastewater coming from the farmhouse throughout a nature-based treatment wetland system (TW). The effects of RW on crop water status (CWS) was assessed by conducting plant-based measurements (i.e., leaf water potential, Ψ; and leaves’ relative water content, RWC) and determining satellite-based biophysical indicators. An economic and environmental evaluation of the proposed sustainable irrigation practices was carried out using the life cycle assessment (LCA) approach. The RW quality showed high variability due to fluctuations in the number of customers at the farmhouse during the COVID-19 pandemic period. A strong impact on the variation in Ψ was observed among the olive orchard under the different water regimes, evidencing how CWS performances are conditioned by the genotype. However, no differences in leaves’ RWC and in satellite-based biophysical indicators were detected. Finally, the results of the LCA analysis underlined how the use of RW may permit us to obtain important economic and environmental gains, representing an added value for olive growing for operating in accordance to more sustainable development models.

Doughnuts for strategies: A tool for an emerging sustainable welfare paradigm

The Degrowth Doughnut (a modification of the original Doughnut visualization of boundaries and foundations created by economist Kate Raworth) can be considered a visualization tool for assessing the current environmental and social capacity of a country to transform into an ecologically and socially sustainable modus operandi. Its sufficiently rich set of criteria, including cultural, socio-economic and biophysical indicators, gives us an overall picture of the problems to be dealt with and the strengths to build on in the immediate future. Its simple boundary-threshold structure presents limiting and aspirational targets in a single image. As such, it is also a tool that can aid eco-social policymaking to prioritize decisions and seek synergies between choices made. This research will present the Hungarian Degrowth Doughnut and use it as a case study when applied to the aims and expected impacts of Hungary’s operational National Clean Development Strategy. We will illustrate the degrowth-relevant priorities and assess the adequacy of the responses proposed by the Strategy, providing a critical analysis of the national policy options. Behind such climate and sustainability strategies are always a wealth of important value choices and moral considerations. Is there a safe and just operating space in the minds of the Hungarian policymakers? To what extent, if at all, are the fundamental principles of post-growth theories incorporated into a Hungarian sustainability strategy? We believe that a case study like this can also provide inspiration for further practical application of the Degrowth Doughnut elsewhere.