A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.
{"title":"Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management","authors":"Weynshet Tesfaye, Eyasu Elias, Bikila Warkineh, Meron Tekalign, Gebeyehu Abebe","doi":"10.1186/s40068-024-00366-3","DOIUrl":"https://doi.org/10.1186/s40068-024-00366-3","url":null,"abstract":"A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1186/s40068-024-00360-9
Rachida Elbarghmi, Mohammad Ghalit, Mostapha Abourrich, Soukaina El khalki, Shehdeh Jodeh, Khalil. Azzaoui, Abdellatif Lamhamdi
For many reasons, water from wells and natural springs is still widely used in Morocco. 90 groundwater samples were analyzed to assess the health risks associated with its quality in the Ketama region, including physicochemical analyses such as pH, electrical conductivity, total dissolved solids, bicarbonates, and nitrates using standardized methods, as well as bacteriological analyses covering total coliforms, fecal coliforms, Escherichia coli and fecal streptococci utilizing the membrane filtration method. Assessment of groundwater physicochemical quality showed that 13.41% of samples had nitrate concentrations exceeding the maximum value set by the World Health Organization (45 mg/ L). In comparison, 12.16% of samples were slightly acidic (pH < 6.5). Bacteriological analyses of the groundwater showed that the water points studied are contaminated with total coliforms, faecal coliforms, Escherichia coli, and faecal streptococci at rates of 80%, 50%, 35%, and 36%, respectively. In conclusion, groundwater in the Ketama region presented potential risks for users, particularly regarding waterborne diseases.
{"title":"Assessment of groundwater quality and health risks in Ketama region (intrarif), Morocco","authors":"Rachida Elbarghmi, Mohammad Ghalit, Mostapha Abourrich, Soukaina El khalki, Shehdeh Jodeh, Khalil. Azzaoui, Abdellatif Lamhamdi","doi":"10.1186/s40068-024-00360-9","DOIUrl":"https://doi.org/10.1186/s40068-024-00360-9","url":null,"abstract":"For many reasons, water from wells and natural springs is still widely used in Morocco. 90 groundwater samples were analyzed to assess the health risks associated with its quality in the Ketama region, including physicochemical analyses such as pH, electrical conductivity, total dissolved solids, bicarbonates, and nitrates using standardized methods, as well as bacteriological analyses covering total coliforms, fecal coliforms, Escherichia coli and fecal streptococci utilizing the membrane filtration method. Assessment of groundwater physicochemical quality showed that 13.41% of samples had nitrate concentrations exceeding the maximum value set by the World Health Organization (45 mg/ L). In comparison, 12.16% of samples were slightly acidic (pH < 6.5). Bacteriological analyses of the groundwater showed that the water points studied are contaminated with total coliforms, faecal coliforms, Escherichia coli, and faecal streptococci at rates of 80%, 50%, 35%, and 36%, respectively. In conclusion, groundwater in the Ketama region presented potential risks for users, particularly regarding waterborne diseases.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1186/s40068-024-00362-7
Yuxiu Chen, Shiqi Yang, Jian Yu
Because of its distinct function and geographic conditions, the impact of climate change on the operation, safety, and income of airports in coastal areas is becoming increasingly significant. The measurement of climate resilience can help identify priority needs and measures to adapt to climate change, which is a crucial step in developing an aviation adaptation plan. At present, the concept of climate resilience is relatively complex and lacks a clear uniformity of composition, which has made it challenging to effectively support the development of adaptation strategies. Based on the definition of climate resilience, our first step was to construct an evaluation system for coastal airports to visually represent the level of climate resilience. Next, in this study, we introduced a coupling coordination and obstacle degree model to analyze the coordinated development and key drivers of climate resilience, which could be used to develop a targeted improvement strategy based on the calculation results. In the future, additional measures can be combined from the natural environment, socioeconomics, governance capacity, and climate change risk to enhance the capacity development of the aviation industry to address climate change and foster the establishment of a sustainable development model between the industry and the environment.
{"title":"A quantitative research on climate resilience in coastal airports from the perspective of adaptation","authors":"Yuxiu Chen, Shiqi Yang, Jian Yu","doi":"10.1186/s40068-024-00362-7","DOIUrl":"https://doi.org/10.1186/s40068-024-00362-7","url":null,"abstract":"Because of its distinct function and geographic conditions, the impact of climate change on the operation, safety, and income of airports in coastal areas is becoming increasingly significant. The measurement of climate resilience can help identify priority needs and measures to adapt to climate change, which is a crucial step in developing an aviation adaptation plan. At present, the concept of climate resilience is relatively complex and lacks a clear uniformity of composition, which has made it challenging to effectively support the development of adaptation strategies. Based on the definition of climate resilience, our first step was to construct an evaluation system for coastal airports to visually represent the level of climate resilience. Next, in this study, we introduced a coupling coordination and obstacle degree model to analyze the coordinated development and key drivers of climate resilience, which could be used to develop a targeted improvement strategy based on the calculation results. In the future, additional measures can be combined from the natural environment, socioeconomics, governance capacity, and climate change risk to enhance the capacity development of the aviation industry to address climate change and foster the establishment of a sustainable development model between the industry and the environment.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1186/s40068-024-00363-6
Özkan Çapraz
İstanbul is the largest city located in the Mediterranean Basin and has a medium to high risk of climate change and future climate risks. Changes in temperature and other weather variables have had significant impacts on İstanbul. In this context, there is a need for studies on the issues of climate monitoring and climate change vulnerability to reduce the adverse impacts. The aim of this study is to investigate the temperature trends of synoptic weather station in İstanbul Atatürk Airport between 1973 and 2023 to have a general idea about how the temperature has changed over the last half-century and to establish statistically whether a trend is significant or not. The values of minimum (Tmin), maximum (Tmax) and mean (Tmean) temperature related parameters were estimated. Annual, monthly and seasonal temperature trends are also analyzed. The findings of this study indicate a significant (p < 0.001) rise in the mean air temperature (Tmean) of İstanbul over the past 51 years (1973–2023), with an annual warming trend of 0.06 °C. The strongest increasing trend in seasonal mean air temperatures has been observed in the summer season, with an increase of 0.08 °C per year. The trend analysis also shows a statistically non-significant increase in yearly average minimum temperature (Tmin) between 1973 and 2023, with a rate of 0.04 °C per year. However, the annual maximum temperature (Tmax) has shown no changes.
{"title":"Trend analysis of air temperature in a megacity between two continents: the synoptic weather station in İstanbul Atatürk Airport","authors":"Özkan Çapraz","doi":"10.1186/s40068-024-00363-6","DOIUrl":"https://doi.org/10.1186/s40068-024-00363-6","url":null,"abstract":"İstanbul is the largest city located in the Mediterranean Basin and has a medium to high risk of climate change and future climate risks. Changes in temperature and other weather variables have had significant impacts on İstanbul. In this context, there is a need for studies on the issues of climate monitoring and climate change vulnerability to reduce the adverse impacts. The aim of this study is to investigate the temperature trends of synoptic weather station in İstanbul Atatürk Airport between 1973 and 2023 to have a general idea about how the temperature has changed over the last half-century and to establish statistically whether a trend is significant or not. The values of minimum (Tmin), maximum (Tmax) and mean (Tmean) temperature related parameters were estimated. Annual, monthly and seasonal temperature trends are also analyzed. The findings of this study indicate a significant (p < 0.001) rise in the mean air temperature (Tmean) of İstanbul over the past 51 years (1973–2023), with an annual warming trend of 0.06 °C. The strongest increasing trend in seasonal mean air temperatures has been observed in the summer season, with an increase of 0.08 °C per year. The trend analysis also shows a statistically non-significant increase in yearly average minimum temperature (Tmin) between 1973 and 2023, with a rate of 0.04 °C per year. However, the annual maximum temperature (Tmax) has shown no changes.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1186/s40068-024-00354-7
Afrose Sultana Chamon, Md. Abrar Hasin Parash, Jannatul Islam Fahad, S. M. Nazmul Hassan, Santo Kabir Ahmed, Maesha Mushrat, Nafisha Islam, Taukir Hasan, Zarin Atiya, Md. Nadiruzzaman Mondol
Constantly eaten foods (such as fruits, vegetables, cereal, etc.) that contain excessive concentrations of heavy metals pose a major risk to human health and deplete the food supply. The amounts of heavy metals in different date varietiies were measured after they were collected from three wholesale markets in the major cities of Dhaka, Bangladesh, and Medina. In order to look at the health risks associated with heavy metal consumption after intake of dates, the Average Daily Intake (ADI), Hazard Quotient (HQ), and Hazard Index (HI) were also calculated. Copper (Cu), Cadmium (Cd), Chromium (Cr), Iron (Fe), Lead (Pb), Manganese (Mn), Nickel (Ni), and Zinc (Zn) levels were evaluated. Several analyses of date fruit exhibited levels of Pb and Cd in different date varieties that beyond the Maximum Permissible Limit (MPL). In the majority of the samples, ADI was below the upper authorized tolerated daily consumption. The likelihood of a health risk from the regular eating of the investigated date fruits is revealed by the hazardous indexes of samples taken from New Market and Badamtali that surpassed unit value as a result of excessive air pollution brought on by greater industrial and vehicle traffic. According to the study, the majority of the analyzed heavy metals were identified in date samples and those from later samples at levels that were less harmful than the maximum acceptable threshold (MAL). Some samples included higher levels of Pb and Cd. As a result, eating dates that contain more metal has a higher chance of harming your health. Additionally, it has been recommended that regular testing for heavy metals in date fruits may be useful in preventing health risks associated with eating fruits that are contaminated with heavy metals.
{"title":"Heavy metals in dates (Phoenix dactylifera L.) collected from Medina and Dhaka City markets, and assessment of human health risk","authors":"Afrose Sultana Chamon, Md. Abrar Hasin Parash, Jannatul Islam Fahad, S. M. Nazmul Hassan, Santo Kabir Ahmed, Maesha Mushrat, Nafisha Islam, Taukir Hasan, Zarin Atiya, Md. Nadiruzzaman Mondol","doi":"10.1186/s40068-024-00354-7","DOIUrl":"https://doi.org/10.1186/s40068-024-00354-7","url":null,"abstract":"Constantly eaten foods (such as fruits, vegetables, cereal, etc.) that contain excessive concentrations of heavy metals pose a major risk to human health and deplete the food supply. The amounts of heavy metals in different date varietiies were measured after they were collected from three wholesale markets in the major cities of Dhaka, Bangladesh, and Medina. In order to look at the health risks associated with heavy metal consumption after intake of dates, the Average Daily Intake (ADI), Hazard Quotient (HQ), and Hazard Index (HI) were also calculated. Copper (Cu), Cadmium (Cd), Chromium (Cr), Iron (Fe), Lead (Pb), Manganese (Mn), Nickel (Ni), and Zinc (Zn) levels were evaluated. Several analyses of date fruit exhibited levels of Pb and Cd in different date varieties that beyond the Maximum Permissible Limit (MPL). In the majority of the samples, ADI was below the upper authorized tolerated daily consumption. The likelihood of a health risk from the regular eating of the investigated date fruits is revealed by the hazardous indexes of samples taken from New Market and Badamtali that surpassed unit value as a result of excessive air pollution brought on by greater industrial and vehicle traffic. According to the study, the majority of the analyzed heavy metals were identified in date samples and those from later samples at levels that were less harmful than the maximum acceptable threshold (MAL). Some samples included higher levels of Pb and Cd. As a result, eating dates that contain more metal has a higher chance of harming your health. Additionally, it has been recommended that regular testing for heavy metals in date fruits may be useful in preventing health risks associated with eating fruits that are contaminated with heavy metals.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"126 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.
{"title":"Evaluation and comparison of infiltration models for estimating infiltration capacity of different textures of irrigated soils","authors":"Halefom Mesele, Berhane Grum, Gebremeskel Aregay, Gebremeskel Teklay Berhe","doi":"10.1186/s40068-024-00356-5","DOIUrl":"https://doi.org/10.1186/s40068-024-00356-5","url":null,"abstract":"Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-14DOI: 10.1186/s40068-024-00355-6
Henry M. Zimba, Kawawa E. Banda, Stephen Mbewe, Imasiku A. Nyambe
This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.
{"title":"Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation","authors":"Henry M. Zimba, Kawawa E. Banda, Stephen Mbewe, Imasiku A. Nyambe","doi":"10.1186/s40068-024-00355-6","DOIUrl":"https://doi.org/10.1186/s40068-024-00355-6","url":null,"abstract":"This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-10DOI: 10.1186/s40068-024-00351-w
Benjamin Makobe, Paidamwoyo Mhangara, Eskinder Gidey, Mahlatse Kganyago
The proliferation of non-native plant species has caused significant changes in global ecosystems, leading to a surge in international interest in the use of remote sensing technologies for both local and global detection applications. The Greater Cradle Nature Reserve, a UNESCO World Heritage Site, is facing a decline in its global status due to the spread of pompom weeds, affecting its biodiversity. A significant reduction in grazing capacity leads to the displacement of game animals and the replacement of native vegetation. We used Sentinel-2A multispectral images to map the distribution of pompom weeds. At the nature reserve from 2019 to 2024, which allowed us to distinguish it from other land cover types and determine the appropriateness of the habitat. The SVM model provided 44% and 50.7% spatial coverage of pompom weed at the nature reserve in 2019 and 2024, respectively, whereas the RF model yielded 31.1% and 39.3%, respectively. The MaxEnt model identified both soil and rainfall as the most important environmental factors in fostering the aggressive proliferation of pompom weeds at the nature reserves. The MaxEnt predictive model obtained an area under curve score of 0.94, indicating outstanding prediction model performance. Classification of above 75%, indicating that they could distinguish pompom weeds from existing land cover types. For sustainable environmental management, this study suggests using predictive models to effectively eradicate the spatial distribution of invasive weeds in the present and future.
{"title":"Monitoring the invasion of Campuloclinium macrocephalum (less) DC plants using a novel MaxEnt and machine learning ensemble in the Cradle Nature Reserve, South Africa","authors":"Benjamin Makobe, Paidamwoyo Mhangara, Eskinder Gidey, Mahlatse Kganyago","doi":"10.1186/s40068-024-00351-w","DOIUrl":"https://doi.org/10.1186/s40068-024-00351-w","url":null,"abstract":"The proliferation of non-native plant species has caused significant changes in global ecosystems, leading to a surge in international interest in the use of remote sensing technologies for both local and global detection applications. The Greater Cradle Nature Reserve, a UNESCO World Heritage Site, is facing a decline in its global status due to the spread of pompom weeds, affecting its biodiversity. A significant reduction in grazing capacity leads to the displacement of game animals and the replacement of native vegetation. We used Sentinel-2A multispectral images to map the distribution of pompom weeds. At the nature reserve from 2019 to 2024, which allowed us to distinguish it from other land cover types and determine the appropriateness of the habitat. The SVM model provided 44% and 50.7% spatial coverage of pompom weed at the nature reserve in 2019 and 2024, respectively, whereas the RF model yielded 31.1% and 39.3%, respectively. The MaxEnt model identified both soil and rainfall as the most important environmental factors in fostering the aggressive proliferation of pompom weeds at the nature reserves. The MaxEnt predictive model obtained an area under curve score of 0.94, indicating outstanding prediction model performance. Classification of above 75%, indicating that they could distinguish pompom weeds from existing land cover types. For sustainable environmental management, this study suggests using predictive models to effectively eradicate the spatial distribution of invasive weeds in the present and future.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Random forests (RF) have been widely used to predict spatial variables. Several studies have shown that spatial cross-validation (CV) methods consistently cause RF to yield larger prediction errors compared to standard CV methods. This study examined the impact of species characteristics and data features on the performance of the standard RF and spatial CV approaches for predicting species abundance distribution. It compared the standard 5-fold CV, design-based validation, and three different spatial CV methods, such as spatial buffering, environmental blocking, and spatial blocking. Validation samples were randomly selected for design-based validation without replacement. We evaluated their predictive performance (accuracy and discrimination metrics) using artificial species abundance data generated by a linear function of a constant term ( $$beta _0$$ ) and a random error term following a zero-mean Gaussian process with a covariance matrix determined by an exponential correlation function. The model was tuned over multiple simulations to consider different mean levels of species abundance, spatial autocorrelation variation, and species detection probability. Here we found that the standard RF had poor predictive performance when spatial autocorrelation was high and the species probability of detection was low. Design-based validation and standard K-fold CV were found to be the most effective strategies for evaluating RF performance compared to spatial CV methods, even in the presence of high spatial autocorrelation and imperfect detection for random samples. For weakly or moderately clustered samples, they yielded good modelling efficiency but overestimated RF’s predictive power, while they overestimated modelling efficiency, predictive power, and accuracy for strongly clustered samples with high spatial autocorrelation. Globally, the checkerboard pattern in the allocation of blocks to folds in blocked spatial CV was found to be the most effective CV approach for clustered samples, whatever the degree of clustering, spatial autocorrelation, or species abundance class. The checkerboard pattern in spatial CV was found to be the best method for random or systematic samples with spatial autocorrelation, but less effective than non-spatial CV approaches. Failing to take data features into account when validating models can lead to unrealistic predictions of species abundance and related parameters and, therefore, incorrect interpretations of patterns and conclusions. Further research should explore the benefits of using blocked spatial K-fold CV with checkerboard assignment of blocks to folds for clustered samples with high spatial autocorrelation.
{"title":"Random forest and spatial cross-validation performance in predicting species abundance distributions","authors":"Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï","doi":"10.1186/s40068-024-00352-9","DOIUrl":"https://doi.org/10.1186/s40068-024-00352-9","url":null,"abstract":"Random forests (RF) have been widely used to predict spatial variables. Several studies have shown that spatial cross-validation (CV) methods consistently cause RF to yield larger prediction errors compared to standard CV methods. This study examined the impact of species characteristics and data features on the performance of the standard RF and spatial CV approaches for predicting species abundance distribution. It compared the standard 5-fold CV, design-based validation, and three different spatial CV methods, such as spatial buffering, environmental blocking, and spatial blocking. Validation samples were randomly selected for design-based validation without replacement. We evaluated their predictive performance (accuracy and discrimination metrics) using artificial species abundance data generated by a linear function of a constant term ( $$beta _0$$ ) and a random error term following a zero-mean Gaussian process with a covariance matrix determined by an exponential correlation function. The model was tuned over multiple simulations to consider different mean levels of species abundance, spatial autocorrelation variation, and species detection probability. Here we found that the standard RF had poor predictive performance when spatial autocorrelation was high and the species probability of detection was low. Design-based validation and standard K-fold CV were found to be the most effective strategies for evaluating RF performance compared to spatial CV methods, even in the presence of high spatial autocorrelation and imperfect detection for random samples. For weakly or moderately clustered samples, they yielded good modelling efficiency but overestimated RF’s predictive power, while they overestimated modelling efficiency, predictive power, and accuracy for strongly clustered samples with high spatial autocorrelation. Globally, the checkerboard pattern in the allocation of blocks to folds in blocked spatial CV was found to be the most effective CV approach for clustered samples, whatever the degree of clustering, spatial autocorrelation, or species abundance class. The checkerboard pattern in spatial CV was found to be the best method for random or systematic samples with spatial autocorrelation, but less effective than non-spatial CV approaches. Failing to take data features into account when validating models can lead to unrealistic predictions of species abundance and related parameters and, therefore, incorrect interpretations of patterns and conclusions. Further research should explore the benefits of using blocked spatial K-fold CV with checkerboard assignment of blocks to folds for clustered samples with high spatial autocorrelation.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1186/s40068-024-00339-6
Marian Asantewah Nkansah, Dominic Adrewie, Ida Sandra Quarm, Seth Obiri -Yeboah, Matt Dodd
The presence of metals in milled shrimps sold on some major markets in Kumasi were investigated to ascertain their levels and the potential health risk they may pose to humans when ingested, due to the level of pollution in the marine environment where these shrimps are obtained from. The samples, which comprised of 30 composites, were analysed using x-ray florescence spectrometry and found to contain Co, Cr, Cu, Fe, K, Mo, Ca, Zn, As, Sr, and Zr with average concentrations of 4.09 mg kg− 1, 5.17 mg kg− 1, 25.14 mg kg− 1, 351.47 mg kg− 1, 9050.74 mg kg− 1, 4.08 mg kg− 1, 21984.48 mg kg− 1, 696.89 mg kg− 1, 8.99 mg kg− 1, 328.54 mg kg− 1, and 9.86 mg kg− 1 respectively. Non-carcinogenic risk indicators analysed suggested a likelihood of health hazard when the milled shrimps are ingested, particularly concerning is the levels of arsenic determined. The arsenic may, however, be in organic form which will make it less of a concern. The levels of the metals could not be linked statistically to the milling process after comparing them to procured controls, which may suggest that these metals may have been picked up in the aquatic environment and/or prior to milling. There is a need, therefore, for action to reduce pollution and remediate the aquatic environment.
{"title":"Metals profile of milled shrimps and the potential risk associated with their consumption","authors":"Marian Asantewah Nkansah, Dominic Adrewie, Ida Sandra Quarm, Seth Obiri -Yeboah, Matt Dodd","doi":"10.1186/s40068-024-00339-6","DOIUrl":"https://doi.org/10.1186/s40068-024-00339-6","url":null,"abstract":"The presence of metals in milled shrimps sold on some major markets in Kumasi were investigated to ascertain their levels and the potential health risk they may pose to humans when ingested, due to the level of pollution in the marine environment where these shrimps are obtained from. The samples, which comprised of 30 composites, were analysed using x-ray florescence spectrometry and found to contain Co, Cr, Cu, Fe, K, Mo, Ca, Zn, As, Sr, and Zr with average concentrations of 4.09 mg kg− 1, 5.17 mg kg− 1, 25.14 mg kg− 1, 351.47 mg kg− 1, 9050.74 mg kg− 1, 4.08 mg kg− 1, 21984.48 mg kg− 1, 696.89 mg kg− 1, 8.99 mg kg− 1, 328.54 mg kg− 1, and 9.86 mg kg− 1 respectively. Non-carcinogenic risk indicators analysed suggested a likelihood of health hazard when the milled shrimps are ingested, particularly concerning is the levels of arsenic determined. The arsenic may, however, be in organic form which will make it less of a concern. The levels of the metals could not be linked statistically to the milling process after comparing them to procured controls, which may suggest that these metals may have been picked up in the aquatic environment and/or prior to milling. There is a need, therefore, for action to reduce pollution and remediate the aquatic environment.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"43 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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