Forest Land Research Articles

Applying fluorescence and 2D-correlation spectroscopy to elucidate changes in the chemical composition of DOM from soils with different vegetation types

ABSTRACT To understand the decomposition of the dissolved organic matter (DOM), four different soils (from grassland, spruce forest, oak forest, and agricultural land) were incubated at 15 °C, and the DOM changes were monitored using UV-Vis, synchronized fluorescence and 2D-correlation spectroscopy. The DOM fraction of the agricultural and grassland soils showed a significant increase in both SUVA values in the middle of the incubation ranging from an initial 3–5 L mg−1 m−1 to 40–50 L mg−1 m−1. Synchronous fluorescence spectroscopy revealed that at the beginning of incubation, on day 3, there was an increase in the proportion of low molecular-weight, readily biodegradable compounds (PLF, protein-like fluorescence), while from day 8 onwards, microbial degradation generated a large number of medium-molecular-weight molecules (MHLF, microbial humus-like fluorescence). At further sampling dates, the proportion of compounds of microbial origin decreased systematically, due to a decrease in microbial activity caused by the lack of substrate. 2D-correlation measurements revealed further details of the changes in the quality of DOM, revealing two distinct patterns in the DOM dynamics of the soils, the sequence MHLF→PLF being observed for grassland and arable land and PLF→HLF for forest soils, probably due to the different N supplies of the soils.

Effect of Land Cover Variations on the Fertility Status of Soils of Umuhu in Ngor-Okpala Area of Imo State

The study was conducted to ascertain the effect of four different land covers (cassava cultivated field (CCF), oil palm plantation (OPP), plantain plantation (PP) and forest land (FL)) on the fertility status of soils of Umuhu in Ngor-Okpala area of Imo State, Nigeria. Random sampling technique was used in the collection of soil samples from the four different land covers. The sampling was done at the beginning, middle and end of each land cover. Twelve (12) composite samples were collected from all the land cover types with 3 per land cover. The composite samples were prepared for routine laboratory analyses. Data generated were analyzed statistically using analysis of variance. The textural classes of all the sampled soils were of loamy sand. The soil which had plantain plantation as its cover had the highest pH (6.4), Total N (0.229 %), Soil organic matter 4.70%, Available P (27.05 mg kg-1), Exch. Ca (6.492 cmolc kg-1), Exch. Mg (3.942 cmolc kg-1), Exch. K (0.953 cmolc kg-1), Exch. Na (0.06 cmolc kg-1), Exch. H (1.2 cmolc kg-1) and CEC (28.4 cmolc kg-1) with its Exch. Al in trace amount (<0.001 cmolc kg-1). The soil under CCF, despite being under low cover, did not show much degeneration due to the shifting cultivation being practiced in the study location. However, the result obtained from the study indicated that the different land covers especially that under plantain plantation (PP) have affected the soils fertility at different rates. This observed factor is strongly controlled by high content of organic materials, a dense vegetative cover which mitigates erosion effects and the addition of ash as a means of fertilizing the soils under plantain plantation being practiced in the study area.

Climate Profile Over Jeldu Woreda of West Shewa Zone, Oromia, Ethiopia

Identifying the climate profile of an area is essential for effective water resource management, agricultural planning and disaster preparedness. This study focuses on characterizing rainfall patterns and identifying the variability of Jeldu woreda in Oromia, Ethiopia. Previously and recently, little study tried to show the woreda profile, which did not bases on station data properly. Forty years (1981-2020) of stations’ rainfall and temperature data were utilized. The study area had natural forest, highland and mid land with varying topography. Based on the annual rainfall cycle, the woreda had bi-modal rainfall types, with the peak of the year being July. Statistical methods and python script were applied to identify the climate profile. The results show that main rainy seasonal rainfall starts on average from 12-14 June and ceases averagely around 10-15 October. During second rainy period, the rain starts from 25 February to 10 March. The mean annual rainfall of woreda is 1395.1 mm, and the average maximum temperature is 19.5°. Kiremt contributes the highest percent, which is more than 64 % of the annual total rainfall. The 1987 and 2015 were the driest years, and 1996 and 2010 is the wettest during kiremt, 1987 and 2010 the wettest, and 1999 and 2015 the driest during Belg. Seasonal rainfall had a regular to moderate precipitation concentration over woreda. Identifying the causes of unseasonal rainfall that happened during dry season over the study area can help to decrease weather hazard during the season.

Evaluation of soil erosion in the Changhua River Basin on Hainan Island based on the Chinese soil loss equation model

Soil erosion is one of the most serious ecological and environmental problems facing southern China. The Changhua River Basin on Hainan Island is affected by soil erosion, which is causing the soil environment to become more fragile. Compared with the Revised Soil Erosion Equation (RUSLE), the Chinese Soil Loss Equation (CSLE) is based on a large amount of Chinese local data and research results, which more accurately reflect the actual situation of soil erosion in China and therefore have better accuracy and applicability in the Chinese region. By combining GIS and RS technologies, this study establishes the CSLE model of the Changhua River Basin, quantifies the soil erosion data via image elements from 2020 to 2022 using the spatial interpolation method, classifies the erosion intensity, and analyzes the spatial and temporal change characteristics of soil erosion. The statistical results show that, during the period from 2020 to 2022, the area of slight erosion in the Changhua River Basin increased by 553.25 km2, with a rate of change of 15.83 %, and the areas of mild erosion, moderate erosion, intense erosion, very intense erosion, and severe erosion decreased by 446.42 km2, 64.4 km2, 25.73 km2, 11.25 km2, and 5.45 km2, respectively, with rates of change of −31.05 %, −30.08 %, −36.58 %, −18.02 %, and −13.85 %, respectively. Slight erosion is defined as soil erosion less than the permissible soil loss and is not regarded as soil erosion, and the other erosion intensities showed a yearly decreasing trend, indicating that the soil erosion control was effective during this three-year period. In the work of soil and water conservation, it is especially necessary to determine the main factors influencing soil erosion and predict the areas that may be prone to such erosion. Therefore, on the basis of establishing a characteristic model using land use type, slope and soil type, and through superposition analysis, we obtained the spatial and temporal change characteristics of soil erosion. The research results are as follows: (1) slight erosion is mainly concentrated in forested areas, and forested land has a better capacity for soil and water conservation; (2) mild, moderate, and strong erosion mainly occur in cultivated areas and areas with a slope of 0–5°; (3) areas of built land and areas with a slope of 8°–15° are more prone to intense erosion, although they cover a smaller area; (4) when the slope is greater than 15°, the overlap range with the forest area is larger and the slope is no longer the main factor leading to soil erosion. Thus, it can be seen that forest land significantly reduces the impact of soil erosion. (5) among the different soil types, Technosol, Ferralsol and Fluvisol all have less than 55 per cent uneroded area and are generally less erosion-resistant, while Lixisol and Acrisol are relatively more susceptible to a high degree of erosion hazard (Extremely strong erosion, severe erosion).

Decision dynamics and landscape approach by individuals and collective groups: the case of Japanese rural land use in agricultural, forest, and boundary areas in an era of population decline

Establishing a decision-making scheme for multiple land use types in a shrinking society is an urgent and persistent task. In particular, residents and governments face the complexity of cross-sectoral visions over concerned landscapes. This study examined the preference of residents for future visions and the effects of group deliberation in a Japanese rural community to enhance the current understanding of decision and learning processes on cross-sectoral land use types (i.e., agricultural land, forest land, and boundary areas) across ages and groups. This study posed the following questions: (i) what types of interactions do residents find among options on cross-sectoral land uses? and (ii) how do group deliberation influence the individual preferences of residents for land use? In the survey, the residents disclosed their preferences for the prepared visions over three land use types. The survey also enabled the comparison of choices before and after group deliberation. Regarding question (i), the residents’ choice prior to the deliberation demonstrated thematic coherence through land use categories. Smart technology and potential productivity improvement were selected across agriculture and forests. However, regarding question (ii), thematic coherence decreased after the group deliberation. The residents could communicate disagreements during group deliberation and consider the issues relatively independently over land use types, which separately views agriculture lands and forests. The choice after group discussions became increasingly embedded and influenced by residential areas and land ownership of residents. These findings provide unique insights into the group discussion on how individual decisions are affected. In summary, the preferences of cross-sectoral land use types did not converge but tended to diverge.

Sustainable Tribal Livelihood Generation through Tribal Cooperatives and the Effective Use of Minor Forest Products in Jharkhand

A significant part of India’s tribal community, which forms 11.3% of its rural demographic, relies heavily on Minor Forest Products (MFPs) for their livelihood. Reports indicate that around 100 million people are engaged in the collection and trade of MFPs, supporting nearly 275 million rural Indians, many of whom are tribal. Despite these natural resources, tribal groups in India face substantial socio-economic challenges, lagging behind in access to essential services such as healthcare, education, and financial opportunities. This gap is where Tribal co-operatives can make a significant impact, focusing on poverty reduction, environmental sustainability, and community development by creating sustainable business models that address unmet societal needs. Jharkhand, known as the "land of forests," is among the top three Indian states with a high tribal population, where approximately 60% depend on forest resources for their sustenance. Diverse MFPs like tamarind mainly enrich the state's economy. Yet, the full economic potential of these resources is underutilized due to limited market access and inadequate livelihood opportunities for tribal populations. The present study, titled " Sustainable Tribal livelihood generation through tribal cooperatives and the effective use of minor forest products in Jharkhand," examines how forming cooperatives can not only increase villagers' incomes by producing higher-value products but also strengthen communities and promote sustainable economic development. Educating tribes on available government schemes could significantly enhance their livelihoods and contribute to India's economic growth.

Landscape pattern prediction method based on ANN-CA-Markov coupling model

The simulation and prediction of landscape patterns can provide a reference for the formulation of ecological space security policies and management and control measures. Therefore, it is necessary to use objective and accurate scientific methods to make accurate simulation predictions. In this study, the quantitative prediction of the Markov model is combined with the advantages of the space-time simulation of the CA model, and the artificial neural network (ANN) module is used for calculation and coupling correction. The Kappa coefficient and the FoM (figure of merit) index are used to test the simulation accuracy. Based on this process, the changes of the landscape pattern in Mudanjiang City at three time points in 2000, 2010 and 2020 were analyzed, and the simulation prediction was made based on this. The experimental results show that the CA-Markov model using the ANN module for coupling correction can be tested for accuracy in the study area, and its Kappa coefficient is 0.834 and the FoM index is 0.001. The model meets the requirements of the landscape pattern for the accuracy of simulation and prediction, and the prediction and analysis of the study area is carried out, and the accurate prediction results are obtained.The results of the study were: Looking at the change in landscape pattern over the 20 years from 2000 to 2020, significant changes occurred in the areas of arable land and artificial surface. The total area of arable land decreased by nearly 130 km2, while the area of artificial surface (i.e., built-up land) increased by about 167 km2. The forest area initially decreased and then increased, with an overall increase of approximately 56 km2, while the water and grassland areas first increased and then decreased. In terms of spatial distribution, the changes in agricultural and forest land are mainly concentrated in the central area where urban development is intensive and in the northern forest area. The results of this study are quite important for policy formulation, according to which it was found that human activities, especially urbanization, have led to a decrease in the area of forested land, and therefore the implementation of the natural forest protection policy can lead to a sustained increase in the area of forested land.

Measuring Well-Being Index with Environmental in Mind: Evidence Forest Land Use in Indonesia

This project aims to create an objective composite wellbeing index from the point of view of the whole by using a complete welfare methodology and suggested weightings to take into account the differences between the components. Forestry total productivity (TFP) was also compared because of the importance of the environmental component in preparing the well-being index. This study examined 64 social, economic, environmental, and institutional indicators from the BPS-Statistics Indonesia, the Ministry of Environment and Forestry, and the National Disaster Management Agency. Three primary analysis elements were highlighted in this investigation. First, PCA created a weighted index of eleven important domains. Second, it creates a well-being index model for Indonesia's environmental sustainability. Third, comparing forestry's environmental dimension to its TFP. This study found that the Indonesian wellbeing model under construction weighs environmental quality, living conditions, including housing, and happiness. Indonesia's disaster-prone locations make environmental quality important, unlike other wellbeing indices. Forest degradation has decreased the composite wellbeing index, notwithstanding other socio-economic improvements. This study stands out from past research by being the first to compare the environmental dimension with forestry total factor productivity (TFP). Deforestation significantly affects the well-being index in Indonesia.

Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland.

Driving the Evolution of Land Use Patterns: The Impact of Urban Agglomeration Construction Land in the Yangtze River Delta, China

The rapid increase in population and economic activities has greatly influenced land use and spatial development. In urban agglomerations where socioeconomic activities are densely concentrated, the clash between ecological protection and economic growth is becoming more evident. Therefore, a thorough quantitative assessment of spatial changes driven by land use dynamics, alongside an examination of temporal and spatial driving factors, is crucial in offering scientific backing for the long-term and sustainable growth of urban agglomerations. This paper focuses on the major urban agglomerations in China’s Yangtze River Delta region, examining the spatiotemporal evolution of land use and landscape patterns from 2000 to 2020. By employing the standard deviation ellipse technique, coupled with multiple linear regression and the geographical detector model, we conduct a quantitative assessment of the directional trends in urban construction land expansion as well as the diverse impacts of temporal and spatial factors on this expansion across various periods and regions. The findings indicate that over the past 20 years, construction land in the Yangtze River Delta Urban Agglomeration expanded in concentrated patches, showing significant scale effects with relatively intact farmland and forest land being increasingly encroached upon. Landscape-type transitions predominantly occurred in cities around Taihu Lake and Hangzhou Bay, with the most significant transition being farmland converted to construction land, resulting in a greater number of patches and more pronounced land fragmentation. Throughout the 20 years, the standard deviation ellipse of construction land in the Yangtze River Delta Urban Agglomeration expanded and shifted, with the predominant expansion trending from the northwest toward the southeast, and the EN orientation being the most intense expansion area, covering 1641.24 km2. The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions. This study thoroughly examines the driving factors that affect the evolution of urban construction land in the region, offering valuable scientific evidence and references for future planning and development of the Yangtze River Delta Urban Agglomeration, aiding in the formulation of more precise and efficient urban management and land use strategies.

Land Use Simulation and Ecological Network Construction around Poyang Lake Area in China under the Goal of Sustainable Development

The pivotal aspects of enhancing regional ecosystem services and augmenting socioeconomic growth lie in optimizing the land-space development and protection strategies, coupled with the establishment of a robust ecological network (EN). This article examines the Poyang Lake area and employs the MOP model, NSGA-II, and PLUS model to determine the best sustainable land use strategy. Subsequently, the MSPA, InVEST model, circuit theory, complex network, and others are employed to construct and analyze the land-space EN across three time periods. Ultimately, the EN is optimized based on spatial protection priority, ecological obstacle areas, and ecological nodes. The results show the following: (1) From 2005 to 2035, more construction land will be developed around the Greater Nanchang area and other urban centers. In the BAU scenario, construction land will expand faster, while cultivated land, forest, grassland, and bare land will continue to decline. In the SD scenario, the alteration to comparable land is minimal, the growth rate of construction land will slow, cultivated land, forest, grassland, and bare land will all decline little, and the water area will increase slightly; (2) While the area of ecological sources is decreased and ecological corridors become longer and narrower in the BAU scenario, the spatial distribution of ENs in different periods is small, and the quantitative structure and spatial distribution of ecological sources and corridors are essentially unchanged in the SD scenario; (3) Based on the topological structure of ENs, it is found that the clustering of nodes in the SD scenario is more obvious, the importance of ecological sources is enhanced, the efficiency of information transmission is improved, and the radiation range is wider and more stable; (4) The greatest priority ecological sources in each period are concentrated around Poyang Lake. In the SD scenario, the priority of ecological sources improves, and 7025 km2 of ecological obstacle restoration area is identified, with 41, 31, and 36 ecological breakpoints in the first, second, and third levels. The study’s findings can assist and shape theoretical and practical approaches to land governance and sustainable development in great lake areas.

Eco–Tourism and its Development in Tribal Regions of Jharkhand

Jharkhand, a state nestled in the lap of nature and often called the land of forests, is a haven for those seeking solace and spiritual connection amidst natural beauty. Its rich and abundant natural splendour offers many opportunities for visitors to immerse themselves in the wonders of the natural world. Whether it's observing the diverse flora and fauna thriving in its verdant forests, marvelling at the numerous enchanting waterfalls, trekking through breathtaking hills, encountering the diverse wildlife in its renowned national parks, or embarking on a profound spiritual journey through its famous temples, Jharkhand promises an unforgettable and reflective experience. Therefore, exploring ecotourism in the state of Jharkhand has the potential for significant economic growth, offering hope to marginalised communities and fostering optimism in sustainable tourism and economic sustainability. The study delves into ecotourism by exploring the rich cultural practices and traditions of tribal communities in Jharkhand. It envisions ecotourism as a unifying force, aiming to unite people from all walks of life under the shared belief of “Vasudhaiva Kutumbakam”— One Earth, One Family, One Future. The involvement of tribals in ecotourism has the potential to promote environmental sustainability and drive social, economic, and cultural development. Key Words: Tribal, Sustainable, Ecotourism, Flora and Fauna, Non-Timber Forest Products, Forest Bath.

Assessing sediment CO2 effluxes in the coastal ecosystem of North Sumatra, Indonesia

Coastal wetlands including mangrove play a vital role in regulating the local and global carbon cycle. Coastal areas contribute greatly to the carbon exchange process due to the complex interactions that occur between the atmosphere, land, and oceans. One of the important components in coastal carbon dynamics is CO2 gas exchange between soil, water and the atmosphere. This study aims to assess CO2 efflux across various land covers (namely natural mangrove, restored mangrove, and converted mangroves to oil palm and aquaculture pond) in the coastal areas of North Sumatra Province, and analyze the effect of sea tides and ebbs on the rate of CO2 efflux and their connection with the number and area of macrozoobenthos burrows. We applied direct sampling by using the static closed chamber method attached to portable CO2 analyzer. The mean of CO2 efflux in natural mangrove forest land covers was 866±585 mgCO2/m2/h during low tide conditions and 1137±792 mgCO2/m2/h during high tide conditions, followed by oil palm plantations at 760.71±341 mgCO2/m2/h, restored mangroves during low tide of 575.24±326 mgCO2/m2/h and 597.11±180 mg CO2/m2/h during high tide conditions, and the lowest was recorded in ponds at 588.55±358 mgCO2/m2/h. Further, we observed that tidal conditions affect the magnitudes of CO2 efflux in natural and restored mangrove forests, and we did not observe similar pattern in oil palms and ponds since these land covers were not influenced by regular tidal input. We also observed that no significant relationship between the number and area of macrozoobenthos burrows and CO2 efflux. Our findings suggest that CO2 effluxes in coastal wetlands are highly dynamic and presumably driven by complex factors and therefore, understanding their magnitudes and drivers requires extensive measurement covering large spatial and temporal scales.

Ultra-lightweight convolution-transformer network for early fire smoke detection

BackgroundForests are invaluable resources, and fire is a natural process that is considered an integral part of the forest ecosystem. Although fire offers several ecological benefits, its frequent occurrence in different parts of the world has raised concerns in the recent past. Covering millions of hectares of forest land, these fire incidents have resulted in the loss of human lives, wild habitats, civil infrastructure, and severe damage to the environment. Around 90% of wildland fires have been caused by humans intentionally or unintentionally. Early detection of fire close to human settlements and wildlife centuries can help mitigate fire hazards. Numerous artificial intelligence-based solutions have been proposed in the past decade that prioritize the detection of fire smoke, as it can be caught through remote sensing and provide an early sign of wildland fire. However, most of these methods are either computationally intensive or suffer from a high false alarm rate. In this paper, a lightweight deep neural network model is proposed for fire smoke detection in images captured by satellites or other remote sensing sources.ResultsWith only 0.6 million parameters and 0.4 billion floating point operations per second, the hybrid network of convolutional and vision transformer blocks efficiently detects smoke in normal and foggy environmental conditions. It outperforms seven state-of-the-art methods on four datasets, including a self-collected dataset from the “Moderate Resolution Imaging Spectroradiometer” satellite imagery. The model achieves an accuracy of more than 99% on three datasets and 93.90% on the fourth dataset. The t-distributed stochastic neighbor embedding of extracted features by the proposed model demonstrates its superior feature learning capabilities. It is remarkable that even a tiny occurrence of smoke covering just 2% of the satellite image area is efficiently detected by the model.ConclusionsWith low memory and computational demands, the proposed model works exceedingly well, making it suitable for deployment in resource constrained devices for forest surveillance and early fire smoke detection.

Does the Comprehensive Commercial Logging Ban Policy in All Natural Forests Affect Farmers’ Income?—An Empirical Study Based on County-Level Data in China

The Comprehensive Commercial Logging Ban Policy (CCLBP) in natural forests as the strictest forest conservation measure brings uncertainty to the income of farmers engaged in forest land management. Therefore, clarifying the impact and heterogeneity of the CCLBP on farmers’ income has become a significant issue of current concern. Based on county-level panel data from China covering the period 2000–2020, this study uses Regression Discontinuity Design (RDD) to identify the impact of the CCLBP on farmers’ income. The empirical results show that (1) the CCLBP has a significantly positive effect on farmers’ income, with the policy leading to an increase in farmers’ income of approximately RMB 411–582; (2) the impact of the CCLBP on farmers’ income exhibits regional heterogeneity, with significant positive effects observed in Hebei, Shandong, Hubei, and Shaanxi, significant negative effects observed in Guangxi, and insignificant effects observed in other provinces; and (3) the CCLBP not only promotes the development of non-agricultural industries and labor mobility but also effectively reduces capital outflow, thereby increasing farmers’ income. This study contributes to the understanding of the underlying mechanisms between the CCLBP and farmers’ income, and it has significant practical implications for promoting the increase in farmers’ income, narrowing the income gap among farmers, and achieving common prosperity. It can also provide valuable insights and guidance for global forest protection.

Investigating the Relationship between Urbanization and Air Pollution Using Google Earth Engine Platform: A Case Study of Istanbul

Rapid population growth in megacities such as Istanbul has led to various effects, such as industrialization, urbanization, loss of green areas, increasing vehicle traffic, and higher consumption of fossil fuels. These reasons, along with many other environmental factors, contribute to the rise of air pollution in urban life. This study aimed to examine the relationship between urbanization and air pollution in Istanbul. For this purpose, land cover maps covering Istanbul province were produced using Landsat-5 (TM), Landsat-8 (OLI), and Sentinel-2 (MSI) images for the years 1996 to 2021 at three-year intervals on the Google Earth Engine platform. Land cover for classification purposes was divided into five different classes: forest, water surface, urban area, and bare land, and classified using a random forest machine learning algorithm. To examine the impact of this urban area growth on air pollution, in the second step of the study, the column number density values of Sentinel 5P (TROPOMI) data for SO2, NO2, CO, and O3 gases for 2019, 2020, and 2021 were analyzed. The averages of the data from 39 air pollution monitoring stations across Istanbul were also examined. According to this classification, the urban area expanded from 491 km2 in 1996 to 1222 km2 by 2021. Considering the total surface area of Istanbul province, the urban area, which was 9% in 1996, reached 23% by 2021. The TROPOMI values were calculated as follows: the average column number density values for SO2, NO2, CO, and O3 were 0.0003538 mol/m², 0.0339514 mol/m², 0.0000984 mol/m², and 0.1453515 mol/m², respectively. Similarly, the gas concentrations of SO2, NO2, CO, and O3 measured from the ground stations were calculated as 6.603 µ/m3, 786,815 µ/m3, 43.763 µ/m3 and 45.773 µ/m3, respectively. Correlations between urbanization and TROPOMI values revealed a positive correlation of 0.39, 0.02, and 0.80 for SO2, NO2, and CO gases, while a negative correlation of 0.25 was found for O3 gas. The study also examined correlations between TROPOMI and ground station measurements, resulting in positive correlations of 0.55, 0.66, and 0.16 for SO2, NO2, and CO gases, respectively, while a negative correlation of 0.05 was found for O3 gas. Based on these findings, among the air pollutants studied both through TROPOMI and ground station data, the highest correlation was observed for CO gas.

Land use & land cover dynamics and its relationship with nitrate pollution in groundwater around inactive mine areas using geospatial techniques, SW part of Cuddapah basin, Southern India

Geospatial maps can show how the ineffective operations of inactive mines affect water and aquifer quality. As such, the purpose of this study is to assess the impact of mining and irrigation on the aquifer ecosystem through the evaluation of LULC and slope maps through the application of Landsat 8 OLI/TIRS and DEM data. A total of 50 groundwater samples were prepared from villages in the close proximity to inactive mines during pre and post monsoon periods in 2021. The results of the analysis revealed alarming statistics, that 14% of groundwater samples exceeded the WHO nitrate limit in pre & post monsoon season, indicating a high-risk in the study area. According to guidelines (USEPA, 2014), 34% in pre-monsoon and 26% post-monsoon of samples exceeded the THI levels for adults and children respectively, indicating non-carcinogenic health risks. In addition, 80% of the samples in both seasons exhibited high NPI values, indicating nitrate contamination associated with blue baby syndrome. From the Geospatial analysis the findings from the LULC classification indicate that there has been a significant increase in cropland area from 2016 to 2021 due to changes in forest land, fallow land, and water resources. These problems have been exacerbated by the expansion of cultivated land, which has increased from 71.1 square kilometers in 2016 to 118 square kilometers in 2021, accounting for 13.1% of the total area. This expansion, coupled with elevated water body resource availability, has compounded the nitrate pollution including in intensely irrigated regions. The slope map analysis revealed that the inactive mines occur at low slope, high rainfall areas and these are compounded by runoff from other sources such as domestic and agricultural wastes. For these matters, sealing and remediating these inactive mines is essential so as to prevent further nitrate leakage.

Land use to agriculture and planted forests strongly affect the genetic diversity of Baccharis crispa Spreng., a native herb of South America

Abstract Human population growth constantly requires an increase in the production of food and products from the timber industry. To meet this demand, agriculture and planted forests are advancing over natural areas. In view of this, it is necessary to know the effects of land use for different purposes (grain production, pastures, planted forests, fruit production, among other uses) on the genetic diversity of populations of native species. This knowledge can assist in land use planning as well as in the development of conservation strategies for native species. In this study, we evaluated the effect of land use for agriculture (mainly for cereal production) and planted forests on the genetic diversity of Baccharis crispa Spreng., a herb native to South America. To achieve our goals, we compared population genetic data obtained with three molecular markers (microsatellites, ISSR [inter-simple sequence repeat] and isoenzymes) with data on land use for agriculture and planted forests from 15 different locations. Our results showed that regardless of the molecular marker used, the greater the use of land for agriculture and planted forests, the lower was the genetic diversity of B. crispa populations. Baccharis crispa is a semi-perennial species that needs at least one year to reach its reproductive period, which is prevented in agricultural areas due to the land being turned over or dissected with herbicides every six months. In the studied regions, the planted forests are of eucalypt and/or pine, which besides being species with a high production of allelopathic substances, produce strong shading and B. crispa is a species that inhabits open grassland that needs high incidence of sunlight for development. The data obtained in our study can assist in the decision-making to use land in order to reconcile the production of supplies for humanity and for the conservation of nature.

Assessing sediment dynamics and retention services in the vulnerable mountain ecosystem of the Indian Himalayas.

Sediment loss and export pose significant global environmental issues, profoundly affecting water quality, soil fertility, and ecosystem stability, particularly in vulnerable mountain ecosystems like the Indian Himalayas. The present study used remote sensing data and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) sediment delivery ratio (SDR) model to analyze spatial-temporal variations in soil loss (SL), sediment export (SE), and sediment retention (SR) capabilities in the South Shimla watershed, Himachal Pradesh, India, from 1993 to 2023. The findings showed significant changes in land use and land cover (LULC): evergreen forest and scrub land decreased sharply by 11.53% and 36.43%, respectively, while agricultural areas and built-up areas increased notably by 71.16% and 215.76%, respectively. Despite a decline of 19.18% in SL and 24.43% in SE, sediment loss and export varied across the study area, highlighting the heterogeneous nature of sediment dynamics. The overall retention capacity increased by 2.59%, with scrub forests playing a critical role in SR, while built-up areas showed the lowest retention. Northern and central sub-watersheds (SWs) experienced a significant decrease in retention capacity (from - 1.92 to - 11.6%), whereas those in the southern and eastern regions saw an increase in SR (from 3.69 to 28.24%). These results underscore the complex interactions between LULC changes, sediment dynamics, and retention services, highlighting the importance of preserving natural ecosystems and informing policy for landscape-based conservation and development planning in the vulnerable Himalayan region.

Estimation of carbon stocks and CO2 emissions resulting from the forest destruction in West Kalimantan, Indonesia

The issue of climate change has become a global concern, and tropical regions are strategically positioned to mitigate its effects through their forest resources. By maintaining the function of forest ecosystems to support life at local and regional levels, carbon stocks can be maintained, and emissions can be reduced. Above-ground biomass can bind and store carbon stocks. This study aims to analyse the CO2 emissions caused by deforestation and forest degradation in Kubu Raya District, West Kalimantan Province. Primary data was obtained through field observations, and secondary data was obtained through maps of forest areas and land cover for 1996–2021 from the Geospatial Information Agency and the Indonesian Ministry of Environment and Forestry. This study uses a non-destructive approach to calculate Above Ground Biomass. The results showed that Kubu Raya District, West Kalimantan Province, Indonesia, experienced deforestation of 235,597 ha and forest degradation of 9,860 ha from 1990 to 2021. This resulted in CO2 emissions of 163,702,815.53 tons. The highest level of CO2 emissions is in Batu Ampar District, and the lowest is in Sungai Kakap District. There is a positive correlation between the amount of deforestation and forest degradation and the level of CO2 emissions caused by these activities. This means that the amount of CO2 emissions depends on the area's deforestation and forest degradation. To reduce the concentration of CO2 in the atmosphere and its effects on climate change, deforestation and forest degradation activities in Kubu Raya Kalimantan should be stopped.