Bare Soil Areas Research Articles

Analysis of Land Surface Performance Differences and Uncertainty in Multiple Versions of MODIS LST Products

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness.

A Novel Bare Soil Index for Enhancing the Mapping of Bare Soil Area – An Indicator of Urban Expansion

A Novel Bare Soil Index for Enhancing the Mapping of Bare Soil Area – An Indicator of Urban Expansion

Land cover is the main driver of the distribution patterns of larval Odonata assemblages in freshwater wetlands of the Brazilian Pampa

AbstractAquatic insects are key to wetland ecological functioning, and the distribution of amphibious insects such as Odonata jointly depends on environmental conditions in the aquatic and terrestrial settings. Therefore, untangling the relative effects of within‐wetland and landscape composition can help predict the responses of Odonata to environmental alterations in wetlands. Using data from 19 wetlands spanning over the southern Brazilian Pampa (center‐western Rio Grande do Sul state), we assessed the relative importance of water chemistry and land‐cover variables to the richness and composition of larval Odonata assemblages (and suborders Anisoptera and Zygoptera). Anisoptera richness decreased with pH and bare soil area. Water pH and areas of bare soil, mosaic of agricultural land use, and waterbodies land cover were the main drivers of Odonata and Zygoptera composition. Our results indicate that land cover is the main driver of the assemblage structure of larval Odonata, although a complex interplay of mechanisms associated with land conversion and water quality drive the distribution of larval Odonata in freshwater wetlands of the Brazilian Pampa. The major implication of our findings is that land cover modification is the major threat to Odonata distribution in Pampean wetlands, with potential impacts on the trophic structure and functioning of these ecosystems.

Urban Spatial Dynamics and Geo-informatics Prediction of Karachi from 1990–2050 Using Remote Sensing and CA-ANN Simulation

Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.

Assessing soil degradation in Brazilian agriculture by a remote sensing approach to monitor bare soil frequency: impact on soil carbon

In countries with extensive agricultural practices, there is a significant risk of soil degradation, making it essential to develop techniques for understanding and detecting these changes. In this study, we used an earth observation system to identify the temporal bare soil frequency and thus, relate it with soil tillage and its impact on soil carbon degradation. The work was performed in two important agricultural states of Brazil, São Paulo and Paraná. For that, historical field and Remote Sensing (RS) data were analyzed to identify the relation between bare soil areas and their degradation. The frequency of bare soil was detected by Landsat images, in the last 36 years using the Geospatial Soil Sensing System (GEOS3). Historical soil surface temperature data was produced using the same images. In addition, legacy pedological and crops (i.e., soil cover) maps were used. Finally, soil texture information was spatialized based on a synthetic soil image (SISY). A total of 28,000 sites with topsoil organic carbon (SOC) were used as the reference for degradation. The soils of the state of Paraná presented significantly lower bare soil areas when compared to the state of São Paulo, mainly due to the wide use of the No-Tillage system. The advancement of sugarcane harvesting technologies together with the "boom" of the commodities after 2000s was responsible for the considerable increases in soil cover conservation. It was noticed that the more exposed the soil remains, the less carbon it has, having a negative correlation (r≈ −0.5). Sandy soils in both states proved to be the ones that were subject to the highest exposure rates and thus, more degraded. This fact is of concern, given that sandy soils are more susceptible to degradation factors, such as erosion. We observed important historical public policies related to the temporal tillage systems adopted by agro-community, which had a significant impact on carbon dynamics. The remote technique was able to infer how the soil has been managed. This information is crucial as it provides a solid basis for developing future public policies aimed at sustainable production.

Unveiling soil microbial community dynamics in desertification: A case study from the tianshan mountains, xinjiang

Microorganisms constitute the primary ecological group in desertified soil, driving soil biogeochemical cycles. Understanding their structure and diversity is crucial for the ecological restoration of desertification areas. This study focuses on the typical desert slopes in the eastern section of the northern foothills of the Tianshan Mountain in Xinjiang, China. It investigates changes in the main prokaryotic microbial community structure, including bacteria and archaea, and their relationship with environmental factors. Results show generally low soil water content, increasing with depth, and a weak alkaline nature with no significant vertical variations. While microbial communities do not distinctly respond to soil water content, there are noticeable variations in microbial diversity with clear stratification, negatively correlated with total organic carbon. Molecular ecological network analyses of five bare soil profiles in Xinjiang’s Hami Tianshan Desert reveal positive interactions among soil microorganisms in vertical layers. Surprisingly, the topsoil, despite having complex networks, shows low diversity and weak interconnectivity. Intriguingly, subsurface soils exhibit distinct molecular ecological networks similar to those found in ecological transition zones (These zones are characterized by rapid environmental shifts from one type to another), further highlighting significant ecological disparities between surface and subsurface soils. By analyzing the structure of soil microbial communities and their relationship with environmental factors on typical desert slopes in the Tianshan Mountains of Xinjiang, significant microbial ecological differences are found between surface and subsurface soils. Notably, in subsurface soils, microbial networks similar to those in ecological transition zones are discovered. These areas have complex microbial community structures and diverse ecological functions, providing new insights into the mechanisms of ecological restoration in desertified soils. Consequently, this study suggests that the vertical distribution patterns of microbial communities in bare desert soil areas differ from those in vegetated areas such as forests and grasslands. The interactions among species, the unique distribution of soil organic matter, and various physical factors are likely pivotal in the distribution patterns of microbial communities in bare desert soils. This research provides foundational theories and empirical support for the ecological restoration of bare desert soils.

Controlled Traffic Farm: Fuel Demand and Carbon Emissions in Soybean Sowing

Soil compaction between crop rows can increase a machine’s performance by reducing rolling resistance and fuel demand. Controlled Traffic Farm (CTF) stands out among modern techniques for increasing agricultural sustainability because the machines continuously travel along the same path in the field, reducing plant crush and compacting the soil in the traffic line. This study evaluated fuel consumption and CO2 emissions at different CTF intensities in different soil management strategies for soybean crop. The experimental design involved randomized blocks in a split-plot scheme with four replications. The plots constituted the three types of soil management: conventional tillage, no-tillage with straw millet cover, and no-tillage with brachiária straw cover. The subplots constituted for agricultural tractors were passed over in traffic lines (2, 4, and 8 times). We evaluated agricultural tractor fuel consumption, CO2 emissions, and soybean productivity. The straw cover and tractor-pass significantly affected the fuel consumption and greenhouse gas emissions of the soybean cultivation. Fuel consumption and CO2 emissions were reduced due to the machine-pass increase, regardless of soil management. Thus, a CTF reduces rolling resistance and increases crop environmental efficiency. Bare-soil areas increased by 20.8% and 27.9% with respect to fuel consumption, compared to straw-cover systems. Brachiária straw and millet reduce CO2 emissions per hectare by 20% and 28% compared to bare soil. Lower traffic intensities (two passes) showed (13.72%) higher soybean yields (of 4.04 Mg ha−1). Investigating these effects in other types of soil and mechanized operations then becomes essential.

Statistical Analysis for Forest Fire Factors using Geography Information System (GIS) and Remote Sensing Imagery

About 33% of permanent forest reserve in Selangor is covered by peat swamp forest. The most important reason of peat swamp forests is to balance the ecosystem. Nevertheless, the evolution of technology and increasing population, with the needs of more space for land development has put peat swamp forest in Selangor under threat particularly because of forest fire. To overcome this risk problem, it is important to identifying and justifying these forest fires and a further study has been carried out to understand the problem. The study is carried out to find the factors that triggering forest fire at Kuala Langat South Forest Reserve (KLSFR) from 2013 until 2020. Temperature, rainfall, relative humidity, wind speed, NDVI, and LULC are choosing as to know the most triggered factors by measure their correlation value. The data are process using GIS and Remote Sensing (Landsat 8). The temperature, rainfall, relative humidity, wind speed data interpolate using Kriging method as a statistical analysis. While LULC is classify using Random Trees method. The value of correlation of temperature (0.4256), rainfall (-0.7613), relative humidity (-0.2484), wind speed (-0.8615), NDVI (0.1945) respectively. Furthermore, LULC is classify into five classes, which are high density forest, medium density forest, agriculture, bare soil, and waterbodies. Bare soil area shows highest correlation compare other classes, which is 0.6381. While rainfall and wind speed were identified as the most trigger factor to forest fire .

Montmorillonite combined with microbially induced carbonate precipitation for wind erosion control of bare surface soil in arid mining area

Open-pit mining will form a large area of bare soil. Under the dry and windy climate in northern China, the surface bare soil of mining area is prone to wind erosion and dust, posing a threat to the environment and human health. This study evaluated the performance of montmorillonite combined with microbially induced carbonate precipitation (MICP-M) to control bare soil wind erosion in mining area. Results showed that the average CaCO3 contents, surface strength, threshold friction velocity (TFV) and thickness of soil crust was the highest, and the mass loss was the lowest in MICP-M group, which significantly improved the wind erosion resistance. After MICP-M solidified the bare soil, the pH value only slightly increased. The consolidation layer could effectively lock in soil moisture, maintain a certain degree of permeability, and had good environmental application performance. Field emission scanning electron microscope (FESEM) tests with energy dispersive X-ray (EDX) and X-ray diffraction (XRD) confirmed more calcite appeared in the soil treated with MICP-M. Ultra-depth three-dimensional maps and particle size analysis indicated that biological bridging was the main consolidation mechanism. The above results indicate that MICP-M has great potential as a wind erosion mitigation strategy.

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.

Application of Soil Multiparametric Indices to Assess Impacts of Grazing in Mediterranean Forests

In this study, the effects of different stocking rates were quantified in three study areas in a Mediterranean forest (Cuenca, Spain) by applying a multiparametric soil quality index (SQI) developed from undisturbed forest soils (>40 years). The main objective was to advance the development and application of multiparametric indices that allow for soil condition assessment. To fulfill this objective, the effectiveness of the developed multiparametric soil quality index (SQI) was analyzed as an indicator of livestock impacts on soil in the Mediterranean forest. The control areas without livestock activity were forest stands of different ages (a thicket forest stand of <30 years; a high-polewood forest stand of 30–60 years; and an old-growth forest stand of >60 years), which were compared with areas subjected to various grazing intensities (areas with permanent livestock passage: a sheepfold that had been inactive for 2–3 years and an active sheepfold; areas with intermittent livestock passage: a bare-soil area, a pine stand and a scrubland). The applied multiparametric soil quality index (SQI) was sensitive to changes in forest ecosystems depending on the stocking rates. However, to obtain greater precision in the assessment of the effects of stocking rates, the multiparametric index was recalibrated to create a new index, the Soil Status Index by Livestock (SSIL). The correlation between the quality ranges obtained with both indices in different study areas suggests that the SSIL can be considered a livestock impact reference indicator in Mediterranean forest soils.

Creation of the Route of Bamboo, a Contribution to Net Zero by 2050 in Vietnam

The Phu An bamboo village, created in 1999, maintains over 200 taxa of living bamboo, collected in Viet Nam, Laos and Cambodia, and was awarded the UNDP Equatorial Prize in 2010. This ex situ conservation not only provides data for basic research and preserves biodiversity resources, but also contributes to actions on CO2 to reduce the greenhouse effect in climate change and create sustainable livelihoods for the community. From the Phu An Bamboo village, the "Bamboo Route" has been built since 2016 from the north to the south of Viet Nam, with the aim of multiplying bamboo in situ conservation sites, associated with research programs and public awareness actions on the importance and environmental benefits of bamboos. Along the bamboo route, an ex situ conservation site has been developed at Dong Thap with plants of 67 different vernacular species, another at Phan Rang, in the arid region, with 62 species, and a third at Dak Nong, where the climate is more favourable, with 12 different vernacular species from the in situ conservation site at Chieng Ban, Son La, where 19 vernacular species have been recorded. Thanks to its fast-growing characteristics and large biomass production, bamboo plays an important role in carbon sequestration. Research to mesure CO2 absorption was done in three different habitats on three different bamboo species. The CO2 absorption capacity of Bambusa blumeana Schultes in Gao Giong, Dong Thap province, indicates an important role of this thorny bamboo under climate change conditions. On the " Route of Bamboo", we studied also the surface area of bare soil from north to south, except in the Mekong Delta, we can plant more than 200,000 ha of thorny bamboo to complete the existing surface area of 1.5 million ha which could contribute 30% of Viet Nam’s CO2 absorption. This enables us to estimate how planting 50% of these areas could contribute to Viet Nam’s Net 0 2050 target.

Bare soil detecting algorithms in western iran woodlands using remote sensing

This study presents the Modified Bare Soil Index (MBI), an innovative remote sensing instrument that employs Short Wave Infrared (SWIR) and Near Infrared (NIR) wavelengths sourced from Landsat 8 satellite observations. Its purpose is to refine the identification and distinction of bare soil within woodland regions. Addressing the limitations of existing methods, MBI represents a significant advancement in land cover change detection. Through a focused study in the Sardasht forests of western Iran, the MBI demonstrates superior accuracy, achieving an overall accuracy of approximately 96 % with a Kappa coefficient exceeding 0.94, surpassing traditional bare soil indices. This innovation improves the classification of land surfaces and also aids in the efficient monitoring and safeguarding of changes in land cover. The MBI's robust performance underscores its potential in smart forestry, enabling more precise identification and surveillance of bare soil areas. This advancement is pivotal for ecological studies and resource management, offering a practical solution to the challenges of soil erosion and land degradation. By integrating advanced spectral analysis with satellite imagery, the MBI sets a new standard in remote sensing techniques, offering vital insights for environmental conservation and agricultural practices.

Consequences of plateau pika disturbance on plant-soil carbon and nitrogen in alpine meadows.

The presence of burrowing mammals can have extensive effects on plants and soils, creating bare soil patches in alpine meadows and potentially altering plant-soil carbon (C) and nitrogen (N). This study focuses on the plateau pika (Ochotona curzoniae) to examine the responses of plant-soil C and N to a small burrowing mammal from quadrat scale to plot scale. The density of active burrow entrances in disturbed plots was used as an indicator of the disturbance intensity of plateau pikas. The study found that the below-ground biomass (BGB) and its C and N, as well as soil C and N concentrations were significantly lower in bare soil areas than in vegetated areas and undisturbed plots. This shows that the quadrat scale limited the estimation of the C and N sequestration potential. Therefore, further research on the plot scale found that the disturbance by plateau pika significantly reduced plant biomass and BGB carbon stock. However, plateau pika did not affect soil C and N stocks or ecosystem C and N stocks. These findings suggest the bare soil patches formed by plateau pika caused plant and soil heterogeneity but had a trade-off effect on plant-soil C and N stocks at the plot scale. Nevertheless, moderate disturbance intensity increased the C and N sequestration potential in grassland ecosystems. These results provide a possible way to estimate how disturbance by small burrowing mammals affects C and N cycling in grassland ecosystems while accurately assessing the effects of small burrowing mammal densities on C and N in grassland ecosystems.

Quantifying the Effect of Soil Ameliorants on Soil Crusting by Means of Field Experiments in a Wildlife Protected Area, South Africa

Soil crusting (surface sealing) is widespread and serious in ecotouristic game parks and reserves in southern Africa, and especially South Africa. In this study, mineral soil crusts were found to be the problem in the Dinokeng Game Reserve (DGR), South Africa. Large areas of bare soil crusted areas were found in this reserve. The cause of this crusting was found to be historical agricultural practices such as cultivation with maize on non-arable soils and overgrazing by cattle. Negative impacts of soil crusting include reduction of water infiltration, leading to increased runoff and erosion and induced drought; inhibiting soil aeration; inhibiting germination and seedling emergence; and inhibiting root functioning and development. In this study in the DGR, a bare crusted area, where cultivation was abandoned 50 years before and there has been no recovery to rangeland since then, was selected for a field trial to determine the effectiveness of the application of various soil ameliorants on soil crust alleviation and improvement of water infiltration rate. The following ameliorants were evaluated: polyacrylamide (PAM) at levels of 5 and 20 kg/ha, gypsum at 2.5 t/ha, and molasses meal at 5 t/ha, as well as combinations of PAM and molasses meal, PAM and gypsum and gypsum and molasses meal. Brush packing, without any ameliorant applied, was also included as treatment, as well as a control with no treatments. PAM treatments increased final infiltration rate (FIR) by between 100–206%. The high efficacy of the lowest PAM treatment is at a cost of only USD 15 per hectare economically important. On the studied soil gypsum application reduced FIR by 81%, while molasses meal had minimal effect. These ameliorants can therefore not be recommended on such soil.

Monitoring and mapping of desertification process using geospatial data and GIS technologies in Mirzachul area

Desertification reduces the land’s ability to withstand changes in climate, including the availability of water and other resources. Remote sensing technology has the potential to monitor and assess land degradation over time. The aim of this study is to use remote sensing images to assess desertification in Uzbekistan and compare the results with formal land productivity monitoring. The Mirzachul area was selected as a case study for monitoring desertification. Landsat images from 1994 to 2024 and the Soil Map of Uzbekistan were used as secondary data to determine the types of soil present in the case study area. The analysis focused on NDVI, SAVI, and WDVI. The results showed a significant difference in sandy bare soil and steppe trends in 1994, with approximately 4.5 million hectares of sandy bare soil and 250,000 hectares of steppe. However, by 2024, the area of sandy bare soil had decreased sharply by about 50% to 1.5 million hectares, while the area of steppe had increased to 2 million hectares.

Land use land cover and land surface temperature changes and their relationship with human modification in Islamabad Capital Territory, Pakistan.

Human activities are altering the existing patterns of Land Use Land Cover (LULC) and Land Surface Temperature (LST) on a global scale. However, long-term trends of LULC and LST are largely unknown in many remote mountain areas such as the Karakorum. . The objective of our study therefore was to evaluate the historical changes in land use and land cover (LULC) in an alpine environment located in Islamabad Capital Territory, Pakistan. We used Landsat satellite pictures (namely Landsat 5 TM and Landsat 8 OLI) from the years 1988, 2002, and 2016 and applied the Maximum Likelihood Classification (MLC) approach to categorize land use classes. Land Surface Temperatures (LST) were calculated using the thermal bands (6, 10, and 11) of Landsat series data. The correlation between the Human Modification Index (HMI) and LULC as well as LST was evaluated by utilizing data from Google Earth Engine (GEE). Over the study period, the urbanized area increased by 9.94%, whilst the agricultural and bare soil areas decreased by 3.81% and 3.94%, respectively. The findings revealed a significant change in the LULC with a decrease of 1.99% in vegetation. The highest LST class exhibited a progressive trend, with an increase from 12.27% to 48.48%. Based on the LST analysis, the built-up area shows the highest temperature, followed by the barren, agricultural, and vegetation categories. Similarly, the HMI for different LST categories indicates that higher LST categories have higher levels of human alteration compared to lower LST categories, with a strong correlation (R-value = 0.61) between HMI and LST. The findings can be utilized to promote sustainable urban management and for biodiversity conservation efforts. The work also has the potential of utilizing it to protect delicate ecosystems from human interference and to formulate strategies and regulations for sustainable urban growth, including aspects of land utilization and zoning, reduction of urban heat stress, and urban infrastructure.

Evaluation and Analysis of Remote Sensing-Based Approach for Salt Marsh Monitoring

In the United States (US), salt marshes are especially vulnerable to the effects of projected sea level rise, increased storm frequency, and climatic changes. Sentinel-2 data offer the opportunity to observe the land surface at high spatial resolutions (10 m). The Sentinel-2 data, encompassing Cumberland Island National Seashore, Fort Pulaski National Monument, and Canaveral National Seashore, were analyzed to identify temporal changes in salt marsh presence from 2016 to 2020. ENVI-derived unsupervised and supervised classification algorithms were applied to determine the most appropriate procedure to measure distant areas of salt marsh increases and decreases. The Normalized Difference Vegetation Index (NDVI) was applied to describe the varied vegetation biomass spatially. The results from this approach indicate that the ENVI-derived maximum likelihood classification provides a statistical distribution and calculation of the probability (>90%) that the given pixels represented both water and salt marsh environments. The salt marshes captured by the maximum likelihood classification indicated an overall decrease in salt marsh area presence. The NDVI results displayed how the varied vegetation biomass was analogous to the occurrence of salt marsh changes. Areas representing the lowest NDVI values (−0.1 to 0.1) corresponded to bare soil areas where a salt marsh decrease was detected.

Drone-based assessment of microsite-scale hydrological processes promoted by restoration actions in early post-mining ecological restoration stages

A successful choice of post-mining restoration activities in dry climates may depend on relevant features related to topographic characteristics, hydrological processes and vegetation development, which will determine functional recovery in these ecosystems. The combination of different restoration techniques to reestablish vegetation, such as sowing and plantation, implies the interspersion of bare-soil areas with vegetated areas in early plant development stages, which may result in an associated mosaic of hydrologic functioning. In this study, we conducted a drone-based assessment to disentangle the role played by microsite-scale hydrological processes (i.e., planting hole slope, sink volume capacity, individual catchment area, Flow Length Index) promoted by restoration actions in soil protection and vegetation development on the hillside scale. Based on two contrasting restoration scenarios (Steep hillside and Smooth hillside), the different applied restoration treatments conditioned the microtopographic processes on the planting hole scale and, therefore, resource redistribution. The main results showed higher planting hole functionality on the smooth hillsides than on steep hillside, which resulted in greater water availability and bigger vegetation patches.By addressing the role of hydrological processes on the microsite scale, our study contributes substantially to prior knowledge on the relevant factors for ecosystem development and post-mining restoration success. It also demonstrates that high-resolution drone images can be a very useful tool for monitoring restoration actions, especially in large, inaccessible and unstable restored areas.

Semantic Segmentation of Portuguese Agri-Forestry Using High-Resolution Orthophotos

The Montado ecosystem is an important agri-forestry system in Portugal, occupying about 8% of the total area of the country. However, this biodiverse ecosystem is threatened due to factors such as shrub encroachment. In this context, the development of tools for characterizing and monitoring Montado areas is crucial for their conservation. In this study, we developed a deep convolutional neural network algorithm based on the U-net architecture to identify regions with trees, shrubs, grass, bare soil, or other areas in Montado areas using high-resolution RGB and near-infrared orthophotos (with a spatial resolution of 25 cm) from seven experimental sites in the Alentejo region of Portugal (six used for training/validation and one for testing). To optimize the model’s performance, we performed hyperparameter tuning, which included adjusting the number of filters, dropout rate, and batch size. The best model achieved an overall classification performance of 0.88 and a mean intersection of the union of 0.81 on the test set, indicating high accuracy and reliability of the model in identifying and delineating land cover classes in the Montado ecosystem. The developed model is a powerful tool for identifying the status of the Montado ecosystem regarding shrub encroachment and facilitating better future management.