PeerJ:Spatial and Geographic Information Sciencehttps://peerj.com/articles/index.atom?journal=peerj&subject=1445Spatial and Geographic Information Science articles published in PeerJLand potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolutionhttps://peerj.com/articles/169722024-03-132024-03-13Julia HackländerLeandro ParenteYu-Feng HoTomislav HenglRolf SimoesDavide ConsoliMurat ŞahinXuemeng TianMartin JungMartin HeroldGregory DuveillerMelanie WeynantsIchsani Wheeler
The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000–2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.
The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000–2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.Using vulnerability assessment to characterize coastal protection benefits provided by estuarine habitats of a dynamic intracoastal waterwayhttps://peerj.com/articles/167382024-02-192024-02-19Gregory M. VerutesPhilip F. YangScott F. EastmanCheryl L. DoughtyTherese E. AdgieKaitlyn DietzNicole G. DixAllix NorthGregory GuannelSamantha K. Chapman
The existence of coastal ecosystems depends on their ability to gain sediment and keep pace with sea level rise. Similar to other coastal areas, Northeast Florida (United States) is experiencing rapid population growth, climate change, and shifting wetland communities. Rising seas and more severe storms, coupled with the intensification of human activities, can modify the biophysical environment, thereby increasing coastal exposure to storm-induced erosion and inundation. Using the Guana Tolomato Matanzas National Estuarine Research Reserve as a case study, we analyzed the distribution of coastal protection services–expressly, wave attenuation and sediment control–provided by estuarine habitats inside a dynamic Intracoastal waterway. We explored six coastal variables that contribute to coastal flooding and erosion–(a) relief, (b) geomorphology, (c) estuarine habitats, (d) wind exposure, (e) boat wake energy, and (f) storm surge potential–to assess physical exposure to coastal hazards. The highest levels of coastal exposure were found in the north and south sections of the Reserve (9% and 14%, respectively) compared to only 4% in the central, with exposure in the south driven by low wetland elevation, high surge potential, and shorelines composed of less stable sandy and muddy substrate. The most vulnerable areas of the central Reserve and main channel of the Intracoastal waterway were exposed to boat wakes from larger vessels frequently traveling at medium speeds (10–20 knots) and had shoreline segments oriented towards the prevailing winds (north-northeast). To guide management for the recently expanded Reserve into vulnerable areas near the City of Saint Augustine, we evaluated six sites of concern where the current distribution of estuarine habitats (mangroves, salt marshes, and oyster beds) likely play the greatest role in natural protection. Spatially explicit outputs also identified potential elevation maintenance strategies such as living shorelines, landform modification, and mangrove establishment for providing coastal risk-reduction and other ecosystem-service co-benefits. Salt marshes and mangroves in two sites of the central section (N-312 and S-312) were found to protect more than a one-quarter of their cross-shore length (27% and 73%, respectively) from transitioning to the highest exposure category. Proposed interventions for mangrove establishment and living shorelines could help maintain elevation in these sites of concern. This work sets the stage for additional research, education, and outreach about where mangroves, salt marshes, and oyster beds are most likely to reduce risk to wetland communities in the region.
The existence of coastal ecosystems depends on their ability to gain sediment and keep pace with sea level rise. Similar to other coastal areas, Northeast Florida (United States) is experiencing rapid population growth, climate change, and shifting wetland communities. Rising seas and more severe storms, coupled with the intensification of human activities, can modify the biophysical environment, thereby increasing coastal exposure to storm-induced erosion and inundation. Using the Guana Tolomato Matanzas National Estuarine Research Reserve as a case study, we analyzed the distribution of coastal protection services–expressly, wave attenuation and sediment control–provided by estuarine habitats inside a dynamic Intracoastal waterway. We explored six coastal variables that contribute to coastal flooding and erosion–(a) relief, (b) geomorphology, (c) estuarine habitats, (d) wind exposure, (e) boat wake energy, and (f) storm surge potential–to assess physical exposure to coastal hazards. The highest levels of coastal exposure were found in the north and south sections of the Reserve (9% and 14%, respectively) compared to only 4% in the central, with exposure in the south driven by low wetland elevation, high surge potential, and shorelines composed of less stable sandy and muddy substrate. The most vulnerable areas of the central Reserve and main channel of the Intracoastal waterway were exposed to boat wakes from larger vessels frequently traveling at medium speeds (10–20 knots) and had shoreline segments oriented towards the prevailing winds (north-northeast). To guide management for the recently expanded Reserve into vulnerable areas near the City of Saint Augustine, we evaluated six sites of concern where the current distribution of estuarine habitats (mangroves, salt marshes, and oyster beds) likely play the greatest role in natural protection. Spatially explicit outputs also identified potential elevation maintenance strategies such as living shorelines, landform modification, and mangrove establishment for providing coastal risk-reduction and other ecosystem-service co-benefits. Salt marshes and mangroves in two sites of the central section (N-312 and S-312) were found to protect more than a one-quarter of their cross-shore length (27% and 73%, respectively) from transitioning to the highest exposure category. Proposed interventions for mangrove establishment and living shorelines could help maintain elevation in these sites of concern. This work sets the stage for additional research, education, and outreach about where mangroves, salt marshes, and oyster beds are most likely to reduce risk to wetland communities in the region.Geographic source of bats killed at wind-energy facilities in the eastern United Stateshttps://peerj.com/articles/167962024-02-052024-02-05Jamin G. WieringaJuliet NagelC.J. CampbellDavid M. NelsonBryan C. CarstensH. Lisle Gibbs
Bats subject to high rates of fatalities at wind-energy facilities are of high conservation concern due to the long-term, cumulative effects they have, but the impact on broader bat populations can be difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute migrants or more local individuals. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the most likely summer geographic origins of three different bat species (Lasiurus borealis, L. cinereus, and Lasionycteris noctivagans) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 41.6%, 21.3%, 2.2% of all individuals of L. borealis, L. cinereus, and L. noctivagans, respectively, had evidence of movement. In contrast, in Maryland 77.3%, 37.1%, and 27.3% of these same species were classified as migrants. Our results suggest bats killed at a given wind facility are likely derived from migratory as well as resident populations. Finally, there is variation in the proportion of migrants killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that the impact of wind-energy facilities on bat populations occurs across a large geographic extent, with the proportion of migrants impacted likely to vary across species and sites. Similar studies should be conducted across a broader geographic scale to understand the impacts on bat populations from wind-energy facilities.
Bats subject to high rates of fatalities at wind-energy facilities are of high conservation concern due to the long-term, cumulative effects they have, but the impact on broader bat populations can be difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute migrants or more local individuals. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the most likely summer geographic origins of three different bat species (Lasiurus borealis, L. cinereus, and Lasionycteris noctivagans) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 41.6%, 21.3%, 2.2% of all individuals of L. borealis, L. cinereus, and L. noctivagans, respectively, had evidence of movement. In contrast, in Maryland 77.3%, 37.1%, and 27.3% of these same species were classified as migrants. Our results suggest bats killed at a given wind facility are likely derived from migratory as well as resident populations. Finally, there is variation in the proportion of migrants killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that the impact of wind-energy facilities on bat populations occurs across a large geographic extent, with the proportion of migrants impacted likely to vary across species and sites. Similar studies should be conducted across a broader geographic scale to understand the impacts on bat populations from wind-energy facilities.Extending beyond individual caves: a graph theory approach broadening conservation priorities in Amazon iron ore caveshttps://peerj.com/articles/168772024-01-312024-01-31Marcus P. A. OliveiraRodrigo L. Ferreira
The Amazon is renowned worldwide for its biological significance, but it also harbors substantial mineral reserves. Among these, the ferruginous geosystems of the region are critical for iron ore extraction, accounting for 10% of Brazil’s export revenue. Additionally, this region holds a significant speleological heritage with more than 1,000 caves. However, cave conservation efforts are often in conflict with land use, necessitating mediation through environmental regulations. While conservation decisions typically consider only the caves’ characteristics, such an approach fails to account for the interactions among cave communities and their surrounding landscape. This poses a challenge to reserve design for cave conservation purposes. To address this issue, we assessed the predictors that influence the similarity among cave communities, suggesting the use of this parameter as a proxy for subterranean connectivity. Applying graph theory, we proposed a tool to aid in the selection of priority caves for conservation purposes. Our study involved the sampling of invertebrates in 69 iron ore caves and analyzing 28 environmental variables related to these subterranean habitats and adjacent landscape. Our analysis revealed that landscape and habitat characteristics are more important than geographical distance in determining patterns of similarity among caves. Our graph approach highlighted densely interconnected clusters based on similarity. However, specific caves stood out for harboring exclusive fauna and/or exhibiting habitat specificity, making them unique in the study area. Thus, we recommend prioritizing cave clusters for conservation, assembling both singular caves and others that influence them. It is crucial to note that protocols for the protection of subterranean biodiversity must consider measures that encompass both the caves and the surrounding landscape. Our methodology provides insights into the connectivity among caves, identifies existing groups, highlights singular (or unique) cavities that require preservation, and recognizes those influencing these unique habitats. This methodological advancement is crucial for the development of better conservation policies for the speleological heritage in areas under constant economic pressure.
The Amazon is renowned worldwide for its biological significance, but it also harbors substantial mineral reserves. Among these, the ferruginous geosystems of the region are critical for iron ore extraction, accounting for 10% of Brazil’s export revenue. Additionally, this region holds a significant speleological heritage with more than 1,000 caves. However, cave conservation efforts are often in conflict with land use, necessitating mediation through environmental regulations. While conservation decisions typically consider only the caves’ characteristics, such an approach fails to account for the interactions among cave communities and their surrounding landscape. This poses a challenge to reserve design for cave conservation purposes. To address this issue, we assessed the predictors that influence the similarity among cave communities, suggesting the use of this parameter as a proxy for subterranean connectivity. Applying graph theory, we proposed a tool to aid in the selection of priority caves for conservation purposes. Our study involved the sampling of invertebrates in 69 iron ore caves and analyzing 28 environmental variables related to these subterranean habitats and adjacent landscape. Our analysis revealed that landscape and habitat characteristics are more important than geographical distance in determining patterns of similarity among caves. Our graph approach highlighted densely interconnected clusters based on similarity. However, specific caves stood out for harboring exclusive fauna and/or exhibiting habitat specificity, making them unique in the study area. Thus, we recommend prioritizing cave clusters for conservation, assembling both singular caves and others that influence them. It is crucial to note that protocols for the protection of subterranean biodiversity must consider measures that encompass both the caves and the surrounding landscape. Our methodology provides insights into the connectivity among caves, identifies existing groups, highlights singular (or unique) cavities that require preservation, and recognizes those influencing these unique habitats. This methodological advancement is crucial for the development of better conservation policies for the speleological heritage in areas under constant economic pressure.Data science competition for cross-site individual tree species identification from airborne remote sensing datahttps://peerj.com/articles/165782023-12-212023-12-21Sarah J. GravesSergio MarconiDylan StewartIra HarmonBen WeinsteinYuzi KanazawaVictoria M. SchollMaxwell B. JosephJoseph McGlinchyLuke BrowneMegan K. SullivanSergio Estrada-VillegasDaisy Zhe WangAditya SinghStephanie BohlmanAlina ZareEthan P. White
Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods’ ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46–0.55, macro F1 = 0.09–0.32, cross entropy loss = 2.4–9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07–0.32, macro F1 = 0.02–0.18, cross entropy loss = 2.8–16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.
Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods’ ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46–0.55, macro F1 = 0.09–0.32, cross entropy loss = 2.4–9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07–0.32, macro F1 = 0.02–0.18, cross entropy loss = 2.8–16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.Temperature vegetation dryness index (TVDI) for drought monitoring in the Guangdong Province from 2000 to 2019https://peerj.com/articles/163372023-12-182023-12-18Ailin ChenJiajun JiangYong LuoGuoqi ZhangBin HuXiao WangShiqi Zhang
Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky–Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P < 0.001 and P < 0.05) compared to TVDIEVI, and the average Pearson’R correlation coefficient was slightly higher than that of TVDIEVI, indicating that TVDINDVI responded better to drought in Guangdong Province. Our conclusion reveals that drought-prone regions in Guangdong Province are concentrated in the Leizhou Peninsula in southern Guangdong and the Pearl River Delta in central Guangdong. We also analyzed the phenomenon of winter-spring drought in Guangdong Province over the past 20 years. The area coverage of different drought levels was as follows: mild drought accounted for 42% to 64.6%, moderate drought accounted for 6.96% to 27.92%, and severe drought accounted for 0.002% to 1.84%. In 2003, the winter-spring drought in the entire province was the most severe, with a drought coverage rate of up to 84.2%, while in 2009, the drought area coverage was the lowest, at 49.02%. This study offers valuable insights the applicability of TVDI, and presents a viable methodology for drought monitoring in Guangdong Province, underlining its significance to agriculture, environmental conservation, and socio-economic facets in the region.
Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky–Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P < 0.001 and P < 0.05) compared to TVDIEVI, and the average Pearson’R correlation coefficient was slightly higher than that of TVDIEVI, indicating that TVDINDVI responded better to drought in Guangdong Province. Our conclusion reveals that drought-prone regions in Guangdong Province are concentrated in the Leizhou Peninsula in southern Guangdong and the Pearl River Delta in central Guangdong. We also analyzed the phenomenon of winter-spring drought in Guangdong Province over the past 20 years. The area coverage of different drought levels was as follows: mild drought accounted for 42% to 64.6%, moderate drought accounted for 6.96% to 27.92%, and severe drought accounted for 0.002% to 1.84%. In 2003, the winter-spring drought in the entire province was the most severe, with a drought coverage rate of up to 84.2%, while in 2009, the drought area coverage was the lowest, at 49.02%. This study offers valuable insights the applicability of TVDI, and presents a viable methodology for drought monitoring in Guangdong Province, underlining its significance to agriculture, environmental conservation, and socio-economic facets in the region.Carbon mapping in pine-oak stands under timber management in southern Mexicohttps://peerj.com/articles/164312023-12-152023-12-15Ashmir Ambrosio-LazoGerardo Rodríguez-OrtizJoaquín Alberto Rincón-RamírezVicente Arturo Velasco-VelascoJosé Raymundo Enríquez-del ValleJudith Ruiz-Luna
The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel−1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha−1) were estimated for each ST and managed area. The total managed area (3,201 ha−1) had 126 t TB ha−1 and 57 t C ha−1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha−1) and C (63 t ha−1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha−1 and 58 t C ha−1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.
The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel−1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha−1) were estimated for each ST and managed area. The total managed area (3,201 ha−1) had 126 t TB ha−1 and 57 t C ha−1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha−1) and C (63 t ha−1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha−1 and 58 t C ha−1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS modelhttps://peerj.com/articles/164292023-11-202023-11-20Emma L. McDanielSamuel F. AtkinsonChetan Tiwari
Accidental releases of untreated sewage into the environment, known as sewage spills, may cause adverse gastrointestinal stress to exposed populations, especially in young, elderly, or immune-compromised individuals. In addition to human pathogens, untreated sewage contains high levels of micropollutants, organic matter, nitrogen, and phosphorus, potentially resulting in aquatic ecosystem impacts such as algal blooms, depleted oxygen, and fish kills in spill-impacted waterways. Our Geographic Information System (GIS) model, Spill Footprint Exposure Risk (SFER) integrates fine-scale elevation data (1/3 arc-second) with flowpath tracing methods to estimate the expected overland pathways of sewage spills and the locations where they are likely to pool. The SFER model can be integrated with secondary measures tailored to the unique needs of decision-makers so they can assess spatially potential exposure risk. To illustrate avenues to assess risk, we developed risk measures for land and population health. The land risk of sewage spills is calculated for subwatershed regions by computing the proportion of the subwatershed’s area that is affected by one modeled footprint. The population health risk is assessed by computing the estimated number of individuals who are within the modeled footprint using fine-scale (90 square meters) population estimates data from LandScan USA. In the results, with a focus on the Atlanta metropolitan region, potential strategies to combine these risk measures with the SFER model are outlined to identify specific areas for intervention.
Accidental releases of untreated sewage into the environment, known as sewage spills, may cause adverse gastrointestinal stress to exposed populations, especially in young, elderly, or immune-compromised individuals. In addition to human pathogens, untreated sewage contains high levels of micropollutants, organic matter, nitrogen, and phosphorus, potentially resulting in aquatic ecosystem impacts such as algal blooms, depleted oxygen, and fish kills in spill-impacted waterways. Our Geographic Information System (GIS) model, Spill Footprint Exposure Risk (SFER) integrates fine-scale elevation data (1/3 arc-second) with flowpath tracing methods to estimate the expected overland pathways of sewage spills and the locations where they are likely to pool. The SFER model can be integrated with secondary measures tailored to the unique needs of decision-makers so they can assess spatially potential exposure risk. To illustrate avenues to assess risk, we developed risk measures for land and population health. The land risk of sewage spills is calculated for subwatershed regions by computing the proportion of the subwatershed’s area that is affected by one modeled footprint. The population health risk is assessed by computing the estimated number of individuals who are within the modeled footprint using fine-scale (90 square meters) population estimates data from LandScan USA. In the results, with a focus on the Atlanta metropolitan region, potential strategies to combine these risk measures with the SFER model are outlined to identify specific areas for intervention.Genetic analysis of two species of Mnomen in the Kalamazoo Watershed reveal panmixia in Z. Aquatica, structure among Z. Palustris, and hybridization in areas of sympatryhttps://peerj.com/articles/159712023-11-022023-11-02Katarina A. KieleczawaKaylee LukeAndrew Gregory
Mnomen or wild rice of the genus Zizania is an important part of Native American culture, especially in Michigan for the Ojibwe nation. An oil spill in 2010 along the Kalamazoo River and the subsequent clean-up lead to renewed interest in management of Mnomen within the Kalamazoo watershed. The affected water bodies were surveyed for Zizania species to map existing populations, determine the existing genetic diversity and species present, and to identify potential hybridization. Using Traditional Ecological Knowledge of rice beds and opportunistic sampling of encountered plants, 28 rice beds were sampled. Two species of Zizania were identified Z. palustris and Z. aquatica. Genetic diversity was measured using 11 microsatellite loci and was moderately high for both species (Z. aquatica HE = 0.669, H0 = 0.672, n = 26 and Z. palustris HE = 0.697, H0 = 0.636, n = 57). No evidence of population bottle-necking was found. Z. palustris was found to have k = 3 populations on the landscape, while Z. aquatica was found to be a single panmictic population. Several individual hybrids were confirmed using genotyping and they were all found in areas where the two species co-occurred. Additionally, Z. aquatica was found to have expanded into areas historically with only Z. palustris downstream of the oil spill, potentially due to dredging and sediment relocation as part of the clean-up effort.
Mnomen or wild rice of the genus Zizania is an important part of Native American culture, especially in Michigan for the Ojibwe nation. An oil spill in 2010 along the Kalamazoo River and the subsequent clean-up lead to renewed interest in management of Mnomen within the Kalamazoo watershed. The affected water bodies were surveyed for Zizania species to map existing populations, determine the existing genetic diversity and species present, and to identify potential hybridization. Using Traditional Ecological Knowledge of rice beds and opportunistic sampling of encountered plants, 28 rice beds were sampled. Two species of Zizania were identified Z. palustris and Z. aquatica. Genetic diversity was measured using 11 microsatellite loci and was moderately high for both species (Z. aquatica HE = 0.669, H0 = 0.672, n = 26 and Z. palustris HE = 0.697, H0 = 0.636, n = 57). No evidence of population bottle-necking was found. Z. palustris was found to have k = 3 populations on the landscape, while Z. aquatica was found to be a single panmictic population. Several individual hybrids were confirmed using genotyping and they were all found in areas where the two species co-occurred. Additionally, Z. aquatica was found to have expanded into areas historically with only Z. palustris downstream of the oil spill, potentially due to dredging and sediment relocation as part of the clean-up effort.Land suitability assessment for wheat-barley cultivation in a semi-arid region of Eastern Anatolia in Turkeyhttps://peerj.com/articles/163962023-10-312023-10-31Bulut SarğınSiyami Karaca
The efficient use and sustainability of agricultural lands depend heavily on the characteristics of soil resources in a given area, as different soil properties can significantly impact crop growth and yield. Therefore, land suitability studies play a crucial role in determining the appropriate crops for a given area and ensuring sustainable agricultural practices. This study, conducted in Tusba District-Van, Turkey, represents a significant advancement in land suitability studies for wheat-barley cultivation. Using geographic information systems, the analytical hierarchical process method, and the standard scoring function, lands were determined based on the examined criteria for the suitability of wheat-barley cultivation. One of this study’s main findings is identifying critical factors that influence the suitability of land for wheat-barley cultivation. These factors include slope, organic matter content, available water capacity, soil depth, cation exchange capacity, pH level, and clay content. It is important to note that slope is the most influential factor, followed by organic matter content and available water capacity. A Soil Quality Index map was produced, and the suitability of wheat-barley production in the studied area was demonstrated. More than 28% of the study area was very suitable for wheat-barley production (S2), and more than was 39% moderately suitable (S3). A positive regression (R2 = 0.67) was found between soil quality index values and crop yield. The relationship between soil quality index values and crop yield is above acceptable limits. Land suitability assessment can minimize labor and cost losses in the planning and implementation of sustainable ecological and economic agriculture. Furthermore, land suitability classes play an active role in the selection of the product pattern of the area by presenting a spatial decision support system.
The efficient use and sustainability of agricultural lands depend heavily on the characteristics of soil resources in a given area, as different soil properties can significantly impact crop growth and yield. Therefore, land suitability studies play a crucial role in determining the appropriate crops for a given area and ensuring sustainable agricultural practices. This study, conducted in Tusba District-Van, Turkey, represents a significant advancement in land suitability studies for wheat-barley cultivation. Using geographic information systems, the analytical hierarchical process method, and the standard scoring function, lands were determined based on the examined criteria for the suitability of wheat-barley cultivation. One of this study’s main findings is identifying critical factors that influence the suitability of land for wheat-barley cultivation. These factors include slope, organic matter content, available water capacity, soil depth, cation exchange capacity, pH level, and clay content. It is important to note that slope is the most influential factor, followed by organic matter content and available water capacity. A Soil Quality Index map was produced, and the suitability of wheat-barley production in the studied area was demonstrated. More than 28% of the study area was very suitable for wheat-barley production (S2), and more than was 39% moderately suitable (S3). A positive regression (R2 = 0.67) was found between soil quality index values and crop yield. The relationship between soil quality index values and crop yield is above acceptable limits. Land suitability assessment can minimize labor and cost losses in the planning and implementation of sustainable ecological and economic agriculture. Furthermore, land suitability classes play an active role in the selection of the product pattern of the area by presenting a spatial decision support system.