Land Cover Area Research Articles

Satellite-observed relationships between land cover, burned area, and atmospheric composition over the southern Amazon

Abstract. Land surface changes can have substantial impacts on biosphere–atmosphere interactions. In South America, rainforests abundantly emit biogenic volatile organic compounds (BVOCs), which, when coupled with pyrogenic emissions from deforestation fires, can have substantial impacts on regional air quality. We use novel and long-term satellite records of five trace gases, namely isoprene (C5H8), formaldehyde (HCHO), methanol (CH3OH), carbon monoxide (CO), and nitrogen dioxide (NO2), in addition to aerosol optical depth (AOD), vegetation (land cover and leaf area index), and burned area. We characterise the impacts of biogenic and pyrogenic emissions on atmospheric composition for the period 2001 to 2019 in the southern Amazon, a region of substantial deforestation. The seasonal cycle for all of the atmospheric constituents peaks in the dry season (August–October), and the year-to-year variability in CO, HCHO, NO2, and AOD is strongly linked to the burned area. We find a robust relationship between the broadleaf forest cover and total column C5H8 (R2 = 0.59), while the burned area exhibits an approximate fifth root power law relationship with tropospheric column NO2 (R2 = 0.32) in the dry season. Vegetation and burned area together show a relationship with HCHO (R2 = 0.23). Wet-season AOD and CO follow the forest cover distribution. The land surface variables are very weakly correlated with CH3OH, suggesting that other factors drive its spatial distribution. Overall, we provide a detailed observational quantification of biospheric process influences on southern Amazon regional atmospheric composition, which in future studies can be used to help constrain the underpinning processes in Earth system models.

Variable spatiotemporal ungulate behavioral response to predation risk

AbstractPrey must balance resource acquisition with predator avoidance for survival and reproduction. To reduce risk of predation, prey may avoid areas with high predator use, but if they are unable to due to resource acquisition requirements, they may instead change their habitat use or movement speed to mitigate predation risk. Prey risk response may depend on spatially or temporally varying forage availability as well as seasonal variation in prey vulnerability and availability of alternate foods for predators. To quantify how prey respond to spatial and temporal variation in risk of brown bear predation, we examined Roosevelt elk (Cervus canadensis roosevelti) spatiotemporal behavior responses to brown bear (Ursus arctos) habitat use on Afognak and Raspberry islands, Alaska, using Global Positioning System location data during elk parturition (20 May–15 June), summer (16 June–20 September), and autumn (21 September–10 November). During parturition and summer, elk used forest and shrub landcover in areas of higher brown bear probability of use. During parturition, elk used areas with lower forage productivity in areas of higher bear probability of use, and movement speed decreased with higher bear probability of use, especially in shrub landcover. During summer, elk used areas with higher forage productivity in areas of higher brown bear probability of use. During autumn, elk were less likely to use areas with higher bear habitat probability of use across landcover categories and forage productivity. During summer and autumn, elk movement speed increased with higher brown bear probability of use. Elk behavioral response to risk of brown bear predation could increase energy expenditure and decrease their ability to acquire forage, therefore negatively impacting survival and reproduction with spatiotemporal variation in risk response potentially amplifying these impacts.

Exploring Stormwater Best Management Practices for Urban Flood Reduction in Khulna City of Bangladesh Using PCSWMM

Khulna City Corporation (KCC), located in a low-lying area in southwestern Bangladesh, has been suffering from frequent water logging in the rainy season almost every year. This situation is intensified due to rapid urbanization and the impact of climate change, which have consequently created numerous problems in KCC related to stormwater management issues that require immediate consideration. Therefore, the current study aims to explore stormwater management problems in KCC and find possible countermeasures through different best management practices. Stormwater management must be incorporated into urban design and development, particularly in densely populated urban areas like KCC. In Ward No. 30 of KCC, controlling stormwater is a vital concern due to heavy rainfall, a lack of proper drainage infrastructure, unplanned growth, extensive land cover areas, etc. In the current study, the most widely used Personnel Computer Storm Water Management Model (PCSWMM) software was adopted to evaluate the city's current stormwater management practices and develop a long-term stormwater management strategy. A reconnaissance investigation of the existing states of the soil, drainage, and outlet infrastructures was conducted. The study area (KCC Ward No. 30) was then divided into many small catchments in the ArcGIS platform, and areas of each sub-catchment were identified. To determine the percentage of impervious area and the elevation of the study area, the land use and land cover (LULC) map and the digital elevation model (DEM) were generated using the ArcGIS software. All the aforementioned parameters were then entered into the PCSWMM software, and simulations were performed for various rainfall durations using different best management practices. The PCSWMM simulation provided the results of runoff, infiltration, peak runoff, floods, or surcharges at every drainage node or discharge point in the existing and altered drainage system of the study area due to the occurrence of extreme rainfall. The results indicate that various best management practices, increasing the slope, reducing the roughness of the drainage systems, reducing imperviousness, permitting more infiltrations, and promoting green infrastructure, are highly effective in controlling urban flooding or surcharges in the study area caused by the occurrence of extreme rainfall in the future. The findings of this study are expected to be supportive to policymakers, urban planners, and water managers in implementing and promoting sustainable and climate-resilient urban drainage infrastructures to tackle stormwater management problems in the KCC area. Journal of Engineering Science 15(1), 2024, 107-118

Fluvial waters toxicology related to its land use and land cover

Due to uncontrolled population growth and intensified anthropic activities, numerous environmental impacts occur in water bodies, such as the toxicity of their waters. This study aimed to evaluate the water toxicity of the Antas River through ecotoxicological bioassays using neonates of the microcrustacean Daphnia magna, and to relate it to land use and land cover in areas near the river. Fourteen sampling points were selected along the entire assessed stretch of the river, and water samples were collected and subjected to acute toxicity analyses using neonates. Land use and land cover mapping of the Antas River Watershed was generated, and an analysis was conducted within approximately a 400-meter radius from each sampling point. The occupation rates for each evaluated area were quantified and subsequently correlated with the obtained toxicity data. The results revealed a high significance level (99%) indicating that land use and land cover in the vicinity of water bodies directly influence water toxicity, impacting it in urban areas or preserving it in native forest areas, depending on the land use, as assessed through acute assays using neonates of the microcrustacean Daphnia magna.

Assessing Tropical Cyclone Destruction Using Landsat Satellite Imagery: A Case Study in Bangladesh

Tropical cyclones are extreme natural catastrophes and are anticipated to intensify strength and recurrence under upcoming climate change consequences in our coastal regions through Bangladesh. This study has tested and developed methods for evaluating considerable damages affected in 2020 by cyclone Amphan in Satkhira district (3817.29 km2) in Bangladesh from 30m spatial-resolution satellite imageries. Iso-cluster unsupervised classification procedures are applied to prepare maps in pre- and post-cyclone Landsat 8 imagery. A difference between the pre- and post-cyclone image classification and NDVI analysis give us evidence about the significant landscape changes at the Cyclone Amphan impact on the study area. The total accuracies for pre- and post-cyclone images classification were 70% and 68% respectively. The outcomes indicate that 35.09% of the study region was remarkably influenced by cyclone Amphan. Statistical analysis indicates that water bodies have dramatically increased 17.65% in different land cover areas due to the strong water surge. On the contrary, the classified image showed that the vegetation and agricultural land decreased 4.13% and 10.82%. Thus, land covers have inundated due to heavy flash during the cyclone. Barren land also decreased 1.80%, and low land areas were also flooded 0.36% due to heavy water flashes. NDVI analysis indicated no destruction of any vegetation species, but vegetation damage is considerable. The developed method may be imposed in future to measure the impacts produced by cyclones in Bangladesh and to make policies for the commitments of cyclone disaster controlling and management.

Luxury Effect, Heritage Effect, and Land Use Hypotheses Revealing Land Cover Distribution in Hainan Island, China

Land cover analysis is a key method used to understand past land use patterns and explore the driving forces and processes behind them. This study focuses on land cover in 18 counties and cities of Hainan Island, delving into the driving factors of land cover in specific areas of Hainan Island, including the southern, northern, eastern, western, inland, and coastal regions. The effects of socio-economic factors, landscape pattern, and urban functional units on land cover are considered, and three hypotheses are proposed to explain the observed trends. The results indicate that house prices are positively correlated with construction area, woodlands land, and urban green space, thereby supporting the luxury effect hypothesis on land cover. In addition, construction age was negatively correlated with the woodlands area, confirming the role of the inverse legacy effect hypothesis in land cover. Other relationships between urban functional units and land cover emphasized the importance of the land use hypothesis in land cover planning. These results will help decision-makers and managers to better understand the current drivers of land cover, allowing for a more scientific basis when planning and managing urban land cover.

Soil Organic Carbon Prediction Based on Vis-NIR Spectral Classification Data Using GWPCA-FCM Algorithm.

Soil visible and near-infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil-forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large-scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c-means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA-FCM-Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA-FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large-scale spectral libraries.

Forest Encroachment and Agriculture Land Abandonment in the Context of Land Cover Change in Kailali

Land use land cover (LULC) area has been changing for many decades in the Kailali district but has little been researched. The study aims to examine the spatial and temporal changes of LULC from 1978 to 2021, carrying out major driving factors of forest cover change regarding forest encroachment and abandonment. The researcher employed a mixed-method approach to accomplish this study. Field observation, household survey with 45 participants, Key Informant Interviews (KII) with three participants, and five Focus Group Discussions (FGD) were used as research tools to collect the data. Moreover, the researcher used books, journal articles, official reports, and related previous empirical studies as secondary data [Land Resource Mapping Project (LRMP) data were used to screen LULC status of 1978 and 1990, and digitization on Google Earth Pro to prepare LULC data for 2000 and 2021]. The findings of the study revealed that the forest cover area of the Kailali district was in a decreasing trend [ for instance in 1978 the area of forest cover was 75.38%, in 1990 it was 66.51% in 2000, and was 65% in 2021]. In contrast to forest cover, the agricultural [settlements and infrastructures] land area was on an increasing trend which was followed by 21.77%, 30.28%, 31.75%, and 32.25% in each period. The study also found that the changing rate of forest cover and the agricultural land have been decreasing. Further, the research found the major drivers of forest cover change were encroachment and agricultural land abandonment. In addition, population growth, migration, poverty, development activities, natural hazards, resettlement, public utilities, weak implications of policy, and illegal activities were also the major causes of forest cover change. The research implies that both forest encroachment and land abandonment should be controlled, and are to be considered by the stakeholders.

Comparative analysis of multispectral data between GF-1 WFV4 and GF-6 WFV sensors

ABSTRACT In recent years, China has launched a number of Gaofen (GF) series earth observation satellites. It is crucial to understand the relationship between the data of the GF series of satellite sensors for the selection of sensor images for scientific research. Taking the same-day transit image pairs of three regions as the study data, this paper compares the consistency of the Top of Atmosphere (TOA) reflectance of GF-1 WFV4 and GF-6 WFV sensors by using the TOA mean comparison method, and discusses the differences in water body and vegetation extraction. The results indicate that the satellite signal intensity in different land cover types and areas differs significantly. In the bare soil-dominated region, GF-1 WFV4 has a larger signal strength than GF-6 WFV, while in the vegetation-dominated region, it turns out to be just the opposite. The difference between the two sensors is mainly related to the difference in the spectral response function and the radiometric resolution of the two satellites. In addition, the determination coefficient (R2) of the corresponding bands of the two sensors is all greater than 0.90, indicating that the two sensors have strong linear correlation and good complementarity.

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

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.

Land transformation across agroecological zones reveals expanding cropland and settlement at the expense of tree-cover and wetland areas in Nigeria

ABSTRACT Evaluating how land cover is being transformed is essential to identify patterns necessary to infer the change trajectories and the driving factors. This study considers the case of Nigeria, where various natural ecosystems are being converted and for which a current national scale assessment at high spatial resolution is lacking. Producing 30 m Landsat-based time-series data, we analyze change among land cover types (i.e. tree-covered area, grassland, wetland, waterbody, cropland, artificial surface, and otherland) across seven agroecological zones. The annual change intensity was assessed at multi-levels across two time-intervals (i.e. 2000–2013, 2013–2022). Distinguishing between natural land cover and human activity-related land-use, we estimate the extent of change signifying how humans have appropriated natural land cover. Insights from analysis at the interval level reveal that land transformation accelerated from 3.3% in 2000–2013 to 4.5% during 2013–2022 in all agroecological zones (e.g. rainforest, mangrove), except in Sudan savannah and Sahel savannah where speed was higher in 2000–2013 as grasslands were increasingly cultivated. Cropland expanded almost two-fold (22% to 37%), whereas tree-cover declined from 50% to 31% and wetland from 7% to 3.7% over the 23 years. Much loss of natural land cover (e.g. tree-cover, grassland, and wetland) to cropland occurred in 2000–2013 (22%) when most irrigation schemes in Nigeria were established. In contrast, the loss of natural land cover to settlement (0.9%) during 2000–2013 increased to 2.0% in 2013–2022. Of all agroecological zones, the mangrove zone was most disturbed as its persisting land cover areas reduced from 69% to 5% between 2000–2013 and 2013–2022. The amount of persisting land cover was highest in the Sudan savannah at 44% in 2000–2013 and 49% in 2013–2022. Processes of human-appropriated natural land cover in Nigeria are related to urbanization and cropland expansion into natural areas with some instances of natural regeneration, especially in croplands and abandoned settlement areas.

Considering the land-cover elasticity of ecosystem service value coefficients improves assessments of large land-use changes

Economic development often impacts on ecosystem services. Previous studies have raised public and political awareness of the costs associated with such impacts and the benefits of ecosystem services. In cases where empirical information on the value of ecosystem services is lacking, benefit transfer (BT) approaches that use value estimates from a previously studied site to estimate the economic values of a new target area have been established. One of the most popular BT approaches is unit value transfer, where constant ecosystem service value coefficients are used to assess a given land-use/land-cover (LULC) change. In several case studies assessing LULC changes, such unit value transfers with constant value coefficients are biased when nonmarginal changes are involved. Theoretical considerations suggest that large changes in land allocation should alter the opportunity costs of gaining or losing natural capital because the marginal costs of additional losses increase as some LULC types become scarcer (e.g. natural ecosystems). In contrast, marginal benefits shrink as other LULC types become more abundant (e.g. agricultural replacement systems).Here, we propose an improved method for assessing larger scale (i.e., at national levels and beyond) LULC changes using endogenous value coefficients that account for the size of the land cover allocated to each LULC type and derive an equation for calculating these coefficients. The extent to which the value coefficient changes with variations in the land cover area depends on the land-cover elasticity of the value coefficient. Using a hypothetical numerical example of an area of tropical forest converted into grassland, we show that the bias caused by neglecting this land-cover elasticity can be considerable. We also demonstrate how the elasticity needed to correct the value coefficient can be estimated empirically. Finally, we suggest some modifications for future studies assessing large LULC changes.

Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments

AbstractThe lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.

Using interpenetrating subsampling to incorporate interpreter variability into estimation of the total variance of land cover area estimates

Reference data obtained by interpreters is a key component of sample-based estimation of area of land cover and land cover change. However, interpreters may disagree when assigning the reference class label for a given sample unit and this inconsistency between interpreters contributes to the overall uncertainty of the estimated area. Interpenetrating subsampling (IPS) offers a practical way to incorporate interpreter variability into an unbiased estimator of the total variance. This method requires partitioning the full sample into g nonoverlapping groups with the sample units in each group then evaluated by a different interpreter and each interpreter determines the reference class data for only one group. The total variance is estimated by the among group variability of the g estimates of area. IPS was applied to estimate the total variance of land cover area estimates for a sample of 300 pixels selected from the Puget Sound region of the Northwest United States. The reference land cover data were obtained by seven interpreters who each labeled all 300 pixels. These data provided a unique opportunity to explore properties of IPS such as variability over different random partitions of the sample into groups and variability over different subsets of interpreters. IPS estimates of total variance were produced for each land cover class for group sizes of g = 2 through g = 6 and all possible combinations of the seven interpreters for each group size. The estimated total variance decreased with increasing number of groups. Incorporating interpreter variance increased the estimated total variance by a factor ranging from 1.08 (agriculture) to 7.06 (grass/shrub) in simple random sampling. The total variance estimates varied substantially over the random partitions of the sample into groups, but this variability decreased as the group size increased. Compared with other total variance estimators, the IPS estimator is simpler to compute and is more cost effective because it does not require repeat interpretations

Spatio-temporal dynamic analysis of greenness base on NDVI equation for change detection from 2004 to 2024 at River Nile State, Atbara City, Sudan by using remote sensing and geographical information system

Urban regions' ability to maintain high environmental standards is largely dependent on their vegetation. A decrease in the amount of vegetated area on land has resulted from city expansion and population growth. An examination of land cover change in urban settings is necessary, particularly for urban regional planning that takes green, open space into account. The purpose of this study was to examine changes in urban vegetation cover in two Medan sub-districts from 2004 to 2014. Change analysis and the Normalized Difference Vegetation Index (NDVI) were performed in the study. The range of plant diversity in these locations was Observed. The results showed changes in vegetation cover areas in the mentioned years. In 2004, most of the areas were under a highly dense vegetation class while in 2014, they were under a low-density vegetation class with new sub-class. This indicates a decrease in vegetation cover due to changes to non-vegetation cover or land cover areas with less vegetation.

Landscape metrics of the Brusy Commune before and after wind-storm: an assessment of the extent of changes based on Landsat-8 data

Monitoring the change in land cover in disaster-affected areas, such as forests, has become a conventional forest management practice, particularly in protected areas. Most change detection and fragmentation studies rely on single-dated satellite images even while investigating changes over a long temporal span. This study aims to move a step further to compare fragmentation before and after a derecho event that occurred in August 2017 using 23 Landsat-8 images of Brusy Commune within the Tuchola Forest Biosphere Reserve. The supervised classification was carried out in the Google Earth Engine using the machine learning algorithm of random forests within the summer months of 2017 and 2018. The high overall accuracy of 0.92 was obtained for the two images which were then analysed with landscape metrics such as mean patch size, number of patches, total edge and edge density using Patch Analyst. These landscape metrics facilitated the characterisation of landscape fragmentation at both the class and landscape levels. Shannon’s Diversity Index was employed to assess heterogeneity across the landscape. The findings indicate significant fragmentation, particularly in the forest and pasture classes, with overall low diversity. This study underscores the potential for future research to employ advanced machine learning techniques and non-parametric classifiers, such as neural networks, to enhance the prediction of fragmentation across various spatial scales.

Methodology for burned areas delimitation and fire severity assessment using Sentinel-2 data. A case study of forest fires occurred in Spain between 2018 and 2023

The objective of this article was to develop a methodology for the burned areas delimitation and fire severity assessment in forest fires occurred in Spain between 2018 and 2022. As input data, this study was based on the use of Sentinel-2 spectral indices, which are characterized by having spectral bands in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions, allowing a high distinction between burned and unburned areas, and between different fire severity degrees too. All possible combinations between Sentinel-2 bands applied to a spectral normalized difference index (SP) were analyzed, along with the most commonly used burn spectral indices in remote sensing as the Burned Area Index (BAI), the Burned Area Index for Sentinel-2 (BAIS2), the Mid-Infrared Burn Index (MIRBI), the Normalized Burn Ratio (NBR), the Relativized Burn Ratio (RBR) and the relative differential Normalized Burn Ratio (RdNBR). In addition, in order to delete confusions between burned area and the presence of other land cover areas, the Sentinel-2 Global Land Cover (S2GLC 2017) and the temporal differences between pre-fire and post-fire dates were obtained for each spectral index (dSP). The results were compared by: in the case of burned areas, the Emergency Mapping Service (EMS) and the Galicia forest service; in the case of fire severity, using field plots classified as in Ruiz-Gallardo et al. (2004) study (null, low, moderate and high severity). The final statistic results obtained showed that the dNBR2 spectral index (using B11 and B12 Sentinel-2 spectral bands) provided the highest results of burned area delimitation (7% of commission error and 3% omission error, respectively) whereas, the combination of the BAIS2, the NBR and the modified Normalized Burn Ratio (NBR2, using B7 and B12 Sentinel-2 spectral bands), used in areas with low, mix and full vegetation respectively, provided the highest results in fire severity assessment (kappa statistic, F1-score and Balanced Accuracy equal to 0.87, 0.86 and 0.92, respectively). The methodology developed in this work allows obtaining accurate maps of burned area and fire severity in Spain, contributing to the reinforcement of national forest fires statistics.

A Deep Learning Approach for Chromium Detection and Characterization from Soil Hyperspectral Data.

High levels of chromium (Cr) in soil pose a significant threat to both humans and the environment. Laboratory-based chemical analysis methods for Cr are time consuming and expensive; thus, there is an urgent need for a more efficient method for detecting Cr in soil. In this study, a deep neural network (DNN) approach was applied to the Land Use and Cover Area frame Survey (LUCAS) dataset to develop a hyperspectral soil Cr content prediction model with good generalizability and accuracy. The optimal DNN model was constructed by optimizing the spectral preprocessing methods and DNN hyperparameters, which achieved good predictive performance for Cr detection, with a correlation coefficient value of 0.79 on the testing set. Four important hyperspectral bands with strong Cr sensitivity (400-439, 1364-1422, 1862-1934, and 2158-2499 nm) were identified by permutation importance and local interpretable model-agnostic explanations. Soil iron oxide and clay mineral content were found to be important factors influencing soil Cr content. The findings of this study provide a feasible method for rapidly determining soil Cr content from hyperspectral data, which can be further refined and applied to large-scale Cr detection in the future.

Spatiotemporal Evolution of Land Cover and Landscape Ecological Risk in Wuyishan National Park and Surrounding Areas

Previous studies on national park conservation have focused mainly on their internal areas, and few studies have focused on national parks and their surrounding areas as unified entities. Therefore, the investigation of land cover and landscape ecological risks in national parks and surrounding areas is essential for overall ecosystem protection and regional sustainable development. This study examines the spatiotemporal evolution of land cover and its landscape ecological risk in Wuyishan National Park, China, and its surrounding areas (WNPSA) from 1990 to 2020. The results show that (1) the land cover of WNPSA from 1990 to 2020 predominantly exhibited a consistent decline in forested areas, paralleled by an augmentation in farmland and impervious surface areas. The center of standard deviation ellipse of impervious surfaces has been progressively moving further south in tandem with the expansion of these surfaces, primarily located within the county town of Fujian Province. (2) The Wuyishan National Park (WNP) areas were dominated by low values of landscape index, and the high value areas in the park were mainly located at the provincial boundary area, with a gradual narrowing during 1990–2020, suggesting a decrease in landscape heterogeneity within the park. High value areas in the surrounding areas mainly occurred in areas with clustered impervious surfaces (e.g., the county town), where part of them located in Wuyishan City have spread to the edge area southeast of WNP. (3) From 1990 to 2020, the lowest-risk areas continue to expand. However, as the medium-risk zone of the adjacent region extends into the edge of the national park, the low risk zone within the national park exhibits a trend from continuous to separate with the neighboring low risk zone. This led to an escalating stress effect on the ecological security of both the adjacent regions and the national park’s boundary areas due to land cover changes.

Implementation of geographic information system in relation to land cover changes in the Leuser National Park area from 1990 to 2020

In recent years, there have been pressing issues that need immediate attention regarding land cover change in Leuser National Park, particularly in BPTN Region III Stabat, Indonesia. The objective of this study is to analyze the land cover changes that have occurred between the years 1990 and 2020 in BPTN Region III Stabat, Leuser National Park, Indonesia. The methodology employed includes temporal and spatial analysis, with data processing carried out using ArcMap 10.4 software. The land cover categories considered in the analysis include primary dryland forest, secondary dryland forest, shrubland, plantation, open land, water body, dryland agriculture, and mixed dryland agriculture. The findings of this study indicate that the largest land cover area was recorded in 1990, with primary dryland forest covering approximately 192,188.92 hectares, while the smallest land cover area was open land, covering around 3.31 hectares. Over the entire period from 1990 to 2020, the largest decrease in land cover area was observed in secondary dryland forest, amounting to approximately 5,546.23 hectares, whereas the largest increase in land cover area was observed in dryland agriculture, totaling around 4,201.75 hectares.