Various Types Of Land Cover Research Articles

Research on Global Off-Road Path Planning Based on Improved A* Algorithm

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments.

Algorithm, Progresses, Datasets and Validation of GLC_FCS30D: the first global 30 m land-cover dynamic product with fine classification system from 1985 to 2022

Abstract. Land cover change information plays an indispensable role in environmental monitoring, climate change research, agricultural planning, urban development, biodiversity conservation, and natural disaster risk assessment. Recently, the free access of Landsat imagery and improvement of computation capacity especially supported by Google Earth Engine platform provides great chance in time-series land-cover change monitoring. We used the stratified land-cover monitoring strategy and time-series Landsat imagery to develop a novel global 30 m land-cover dynamic product with fine classification system from 1985 to 2022 (GLC_FCS30D). Firstly, we used the multitemporal classification to generate the time-series impervious surfaces, wetlands and tidal flat products. Then, we proposed to combine the continuous change detection algorithm and local adaptive updating model to capture the land-cover changes, and to generate a new global 30 m land-cover dynamic product (impervious surfaces, wetlands and tidal flat types were excluded in this step). Next, after overlapping the three multitemporal classification products and the time-series dynamical land-cover dataset, the novel GLC_FCS30D was developed, which contained 35 fine land-cover types. Lastly, using the global 84526 validation points in 2020, the GLC_FCS30D was validated to show the great performance with an overall accuracy of 80.88%, and had obvious advantages over other global land-cover products in diversity of land-cover types and mapping accuracy.

Implementation of Deep Learning Technique for Accurate Land Cover Classification

Detecting and modelling Land Cover are crucial for managing natural resources, assessing environmental impacts, and preserving ecological connectivity. Convolutional Neural Networks (CNNs) have become pivotal in advancing remote sensing applications, particularly in land cover classification. Therefore, this study introduces a CNN classifier for the prediction land cover efficiently. The pre processing is carried out based on Principle Component Analysis (PCA), which reduces the dimensionality of the input data while retaining essential information. This step aims to enhance computational efficiency and improve the CNN’s ability to extract relevant features during training. Following pre processing, a comprehensive training phase involves optimizing the CNN architecture using a labelled dataset. This process allows the network to learn intricate spatial patterns and spectral characteristics crucial for accurate land cover classification. Evaluation of the trained model occurs in the testing phase, where performance metrics such as accuracy, precision, recall, and F1 score are computed using an independent validation dataset. Results obtained from the python software demonstrate that the CNN PCA framework’s capability to achieve high classification accuracy across diverse land cover types.

Flower-bee versus pollen-bee metanetworks in fragmented landscapes.

Understanding the organization of mutualistic networks at multiple spatial scales is key to ensure biological conservation and functionality in human-modified ecosystems. Yet, how changing habitat and landscape features affect pollen-bee interaction networks is still poorly understood. Here, we analysed how bee-flower visitation and bee-pollen-transport interactions respond to habitat fragmentation at the local network and regional metanetwork scales, combining data from 29 fragments of calcareous grasslands, an endangered biodiversity hotspot in central Europe. We found that only 37% of the total unique pairwise species interactions occurred in both pollen-transport and flower visitation networks, whereas 28% and 35% were exclusive to pollen-transport and flower visitation networks, respectively. At local level, network specialization was higher in pollen-transport networks, and was negatively related to the diversity of land cover types in both network types. At metanetwork level, pollen transport data revealed that the proportion of single-fragment interactions increased with landscape diversity. Our results show that the specialization of calcareous grasslands' plant-pollinator networks decreases with landscape diversity, but network specialization is underestimated when only based on flower visitation information. Pollen transport data, more than flower visitation, and multi-scale analyses of metanetworks are fundamental for understanding plant-pollinator interactions in human-dominated landscapes.

Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency

The advancement of remote sensing technology has led to the development of sophisticated image processing methods that yield highly accurate land cover classification information, minimizing misinterpretations. The Normalized Difference Vegetation Index (NDVI) is a widely utilized method in remote sensing for measuring green vegetation. A significant portion of the Jember Regency area is covered by vegetation. This study aimed to identify various land cover types in the Jember Regency area, quantify the area for each classification, and establish the NDVI value ranges for each type of cover. Sentinel-2 was employed as the primary data source, and the NDVI method was utilized for land cover classification in the Jember Regency. The region exhibited diverse land cover types. Data from Sentinel-2A captured in June and October 2019 were chosen due to their accessibility, open-source nature, and adequate spectral, spatial, and temporal resolution. The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. The accuracy results for June indicated a Kappa Accuracy of 37.7% and Overall Accuracy of 54.5%. In October, the Kappa Accuracy increased to 39.9%, and the Overall Accuracy reached 56.5%. In conclusion, the NDVI method did not meet the criteria for accurately interpreting land cover classification.

An Object-Based Approach to Extract Aquaculture Ponds with 10-Meter Resolution Sentinel-2 Images: A Case Study of Wenchang City in Hainan Province

Coastal aquaculture has made an important contribution to global food security and the economic development of coastal zones in recent decades. However, it has also damaged these coastal zones’ ecosystems. Moreover, coastal aquaculture is poised to play a key role in the achievement of Sustainable Development Goals (SDGs). Consequently, extracting aquaculture has become crucial and valuable. However, due to the limitations of remote sensing image spatial resolution and traditional extraction methods, most research studies focus on aquaculture areas containing dikes rather than individually separable aquaculture ponds (ISAPs). This is not an accurate estimation of these aquaculture areas’ true size. In our study, we propose a rapid and effective object-based method of extracting ISAPs. We chose multi-scale segmentation to generate semantically meaningful image objects for various types of land cover, and then built a decision tree classifier according to the unique features of ISAPs. The results show that our method can remove small rivers and other easily confused features, which has thus far been difficult to accomplish with conventional methods. We obtained an overall precision value of 85.61% with a recall of 84.04%; compared to the support vector machine’s (SVM) overall precision value of 78.85% and recall rate of 61.21%, our method demonstrates greater accuracy and efficiency. We used this method to test the transferability of the algorithm to nearby areas, and the obtained accuracy exceeded 80%. The method proposed in this study could provide a readily available solution for the simple and efficient extracting of ISAPs and shows high spatiotemporal transferability.

Study on potential application of brightness temperature models in passive remote sensing

In the modern science and practice of remote sensing of any surface, including various types of land cover, radio systems are widely used. Due to low energy consumption and high stealth, the most promising direction of technical tools for remote sensing is the development of passive remote sensing systems. To estimate the specific parameters of the surface, information on the relation between the signals of the thermal radiation and these parameters is needed. Therefore, it is relevant to study and analyze the existing types of such relations that connect the brightness temperature and the parameters of the Earth’s surfaces; this is the subject matter of the study in this paper. The goal of this study is to analyze existing relation models and study the conditions of their application in terms of operating frequencies, geometric characteristics, type of surface coverage (with vegetation, without vegetation, etc.), as well as the method of obtaining mathematical relations (electrodynamic models or empirical models). Due to the large number of different types of earth surface coverings and different types of surface models, in order to simplify remote sensing tasks for passive systems, as well as to improve the results of modeling or experiments, the following tasks are formulated: to study and classify existing models of the relation between surface parameters and brightness temperature, and to calculate and analyze the potential accuracy of estimating surface parameters by passive remote sensing systems. The following results were obtained: the impact of the atmosphere on the surface’s brightness temperature, described by the electrodynamic flat model, for the operating frequencies 75–110 GHz of the radiometer was analyzed. The impact of the atmosphere on the brightness temperature appears at sighting angles greater than 50° at horizontal polarization and greater than 80° at vertical polarization. According to the results, it can be said that the estimation accuracy of the imaginary part of the complex permittivity decreases with an operating wavelength decrease, and the estimation accuracy of the real part is the opposite. Conclusions. The results of simulation modeling show that for radiometers with the above-mentioned operating frequencies, it is possible to estimate the real permittivity or only the real component separately in the case of describing the surface with a complex permittivity parameter. It is recommended that the study be conducted at vertical and horizontal polarizations, without and with the impact of the atmosphere. To estimate the surface conductivity (the imaginary part of the complex permittivity of a surface), systems with lower operating frequencies are required. Thus, this study presents an analysis of existing models, calculates the potential accuracy of parameters estimation when using these models, and provides recommendations for practical experiments or modeling.

Estimating Soil Erosion and the Effect of Changing Land Use and Land Cover in the Bringi Catchment’s Devalgam Watershed Using RS and GIS

Abstract: One of the main factors contributing to soil degradation worldwide is soil erosion. The geographical variability of erosion at the Devalgam watershed was evaluated using the Revised Universal Soil Loss Equation (RUSLE) model in a GIS environment. DEM ASTER (30 m × 30 m). The model was fed with annual rainfall data from 1990, 2000, 2010, and 2020 as well as soil and LULC maps. In the Devalgam watershed, the mean annual soil loss ranged from 0 to 127.32, 140.34, 146.49, and 187.23 t/ha/yr for the years 1990, 2000, 2010, and 2020. The mean annual sol loss was calculated to be 16.6, 18.3,19.1, and 22.08 t/ha/yr. According to a zonal statistical analysis of soil erosion for various types of land cover, open forests and barren areas were more likely to experience erosion with the least susceptible to erosion were vegetation, built-up areas, orchards, and agriculture, with estimates of 85.12 and 52.35 t/ha/yr, respectively. According to the current study, managing natural resources is increasingly dependent on the LULC change in the Devalgam Watershed. GIS and remote sensing technologies have shown to be useful tools for analyzing LU/LC changes on a watershed-by-watershed basis. Six LU/LC classes were used to categorize the study area: built-up, barren terrain, vegetation, farmland, woodland, and orchards. and it was discovered that, out of the total area, or 2058.618 hectares, the least area was covered by orchards (0.5%), while the highest area was covered by forest (30.5%), followed by barren ground (25.7%). The examination of the overlay of analysis of the changes from 1990 to 2020 was conducted using LANDSAT-5 1990 over LANDSAT-8 OLI 2020. The findings also indicate that whereas the area under agricultural, builtup areas, barren land, and orchards expanded by 123%, 36.5%, 0.5%, and 36.4%, respectively, the area under forests and vegetation dropped by 17.3% and 48%. With a kappa coefficient of 0.84, 0.71, 0.75, and 0.87, respectively, the overall accuracy for the categorized imageries LANDSAT-5 (1990), LANDSAT-7 (2000), LANDSAT-7 TM (2010), and LANDSAT-8 OLI (2020) was determined to be 86.6%, 76.6%, 80%, and 90%.respectively

Assessing Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 Data for Large-Scale Wildfire-Burned Area Mapping: Insights from the 2017–2019 Canada Wildfires

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR’s performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring.

Characteristics of ground surface heat flux for alpine vegetation in freeze-thaw cycles in the three river source region

Ground surface heat flux (G0) is a key component of surface energy flux and serves as a reliable parameter for assessing shallow geothermal energy. Using the observations from four sites and a novel method, we investigated the daily, monthly, and diurnal characteristics of G0 across various types of land cover in the Three River Source Region. The contribution of soil heat flux at 5 cm or 7.5 cm (Gsoil) to G0 was found to be only between 1/2 and 2/3, with the remaining portion being attributed to changes in heat storage of soil and liquid water (Δssoil), heat storage of soil ice (Δsice) and latent heat of ice phase change (ΔsLH). The characteristics of G0 exhibited significant variations in response to different land-covered vegetation during daily, monthly, and diurnal cycles, as well as two freeze-thaw stages. The alpine marsh-covered soil had the largest annual amplitude in G0 on both daily and monthly averages but showed the smallest diurnal amplitude in G0. In the frozen stage (FS), G0 played a significant role as a supplement to net radiation (Rn) in TRSR, particularly in the alpine marsh region where it accounted for approximately −22 % to −80 % of Rn from November to February.

Comparison of Soil Arthropod Diversity and Community Structure in Various Types of Land Cover in Malang Region, East Java, Indonesia

Land cover heterogeneity can affect the structure of biodiversity in the supplied niche, so it is necessary to know the taxa community. This study aimed to understand the effect of land cover variation on the diversity and community structure of soil arthropods. The types of habitats used include urban areas, agroforestry, gardens, and natural forests which are determined in the Malang Region, East Java, Indonesia. Hand sorting and hay bait traps were applied in this study to obtain a variety of arthropod soils and the Berlese-Tullgren funnel was used to extract them. As a result, there are 25 families from 15 orders collected based on their ecological roles. The abundance of Philoscidae in sites S1 and S2 (urban green space), Talitridae in site S6 (agroforestry), and Isotomidae in sites S3, S4, and S5 (highland mixed forest) was highest and dominant. Site S7 has the highest diversity (H' = 2.56; 1-D = 0.90; J' = 0.76) even though its family richness is lower. The site S3 counter-site had relatively high taxa richness (TR = 13), but low diversity (H' = 1.02; 1-D = 0.39; J' = 0.16). Based on clustering analysis and NMDS ordination, 3 classifications of habitat types were obtained, namely I (S6 & S7), II (S3, S4 & S5), and III (S1 & S2). Mixed forest habitats contain a more complex diversity of soil arthropods, which can serve as a model for improving the fertility of disturbed ecosystems.

Landscape features drive insectivorous bat activity in Indian rice fields

ContextInsectivorous bats have been shown to control a number of agricultural insect pests. As bats exhibit species-specific responses to the surrounding landscape, tied closely to their morphology and foraging mode, the activity and distribution patterns of bats, and consequently the ecosystem services they provide, are influenced by the landscape characteristics.ObjectivesThis study aims to determine which features in the landscape surrounding rice fields influence the activity levels of insectivorous bats, and at what scales they are most influential.MethodsWe collected acoustic recordings to determine activity levels of seven bat sonotypes in rice fields surrounded by a variety of land-cover types in the Nagaon district of Assam, India. Using this, we determined the most important set of features in the surrounding landscape, and the scales at which had the strongest impact, for each sonotype.ResultsOur results suggest that tree cover variables are the most important predictors of bat activity in rice fields. Distance to nearest forest, area of forest within 1 km, distance to nearest forest edge, and landscape heterogeneity influenced all five of the analysed bat sonotypes. Also important were the amount of urban land within 1 km, which exerted a negative effect on the activity of one sonotype, and moonlight activity, which negatively influenced the activity levels of one sonotype.ConclusionOur results demonstrate that when flying over rice fields, bat activity is most influenced by presence and proximity of trees. Therefore, increasing tree cover in agricultural landscapes will increase bat activity and likely the level of pest control.

Potential and diversity of bird species in various land covers in PT BSM, South Sumatera

Birds have an important role in the ecosystem, including as environmental indicators, natural pest controls, and tourist attractions. The existence of birds in a habitat type certainly plays an important role both ecologically and economically. Research that shows the ecological and economic potential of bird species in oil palm plantations is still not widely done. Therefore, research is needed to be able to show the potential and variation of bird species diversity in oil palm plantations. This research was conducted at PT Bina Sawit Makmur (PT BSM) on Desember 2019. The method used was a line transect that was carried out on five different land covers simultaneously. The results showed that a total of 28 species were found. High Conservation Value (HCV) land cover has the highest diversity, richness, and species similarity index values. The potential possessed by bird species in various types of land cover includes natural pest control, economic value animals, and environmental indicators.

STUDI DAMPAK TUTUPAN LAHAN TERHADAP SIMPANAN KARBON DI KAWASAN HUTAN CEMPAKA, KECAMATAN PRIGEN, PASURUAN, JAWA TIMUR

Human activities and global warming cause forest fires that impact increasing carbon emissions, destroy forest biodiversity and destroy soil properties. This research is needed to examine carbon storage and soil physical properties on various types of land cover in the Cempaka Forest area, which will later be used as a basis for carrying out restoration and conservation actions. This study used four observation points: Timber Forest Products, Non-Timber Forest Products, Pine, and Shrubs land cover. Carbon storage was measured in the Cempaka forest using the standard RaCSA procedure. The results showed that land cover differences significantly affect the value of carbon storage, with the highest value for storing carbon being the land cover of HHK. However, the difference in land cover did not cause different results on the physical properties of the soil. Correlation and simple regression between the value of carbon storage and other parameters show that carbon storage is strongly influenced by tree biomass and litter, with respective values of 94% and 21%.

Monitoring Land Surface Temperature (LST) and Land Cover of Basra Province using Remote Sensing Technique and GIS

This study investigates the changes occurring in the province of Basra using geospatial methods and analyzes the variations in land surface temperature among the various types of land cover. For the months of July and December in the years 2013 and 2021, Landsat images were used in Landsat 8 OLI/TIRS, and satellite images were processed using ArcGIS 10.8 software. The study's categories for land use and land cover were generated through the application of supervised classification techniques, and the land surface temperature was calculated using data from a satellite sensor's brightness temperature. According to the study's findings, there has been an increase in urban areas (including barren land). From 2013 to 2021, a greater correlation between urban land and LST was found, indicating an increasing surface urban heat island effect as evidenced by its statistically significant correlation coefficients.It has a significant impact on the variations in land surface temperature.This study also highlighted the key variations in how land use and cover affect LST. Across all time periods of investigation. Therefore, techniques for remote sensing and geographic information systems are useful for tracking and analyzing urban expansion patterns and assessing their effects on land surface temperature.

Identification of climate change impact and thermal comfort zones in semi-arid regions of AP, India using LST and NDBI techniques

Andhra Pradesh, in southern India, frequently experiences warm summers and winters. However, the year-round greater relative humidity and the positioning of the summer hot spots are linked to actual weather heat exhaustion during the warmest months. Climate change is a significant—and frequently disregarded—factor in the relationship between climate and mortality. With the use of the Land Surface Temperature (LST) and Normalized Difference Built-up Index (NDBI), we sought to identify the climatic change in this research. Additionally, we attempted to identify the Thermal Comfort Zones (TCZs) in Andhra Pradesh, South India's semi-arid regions. Arc GIS and ERDAS Imagine were used to process the satellite data of Landsat 4–5 (TM & MSS), Landsat 7 ETM+, and Landsat 8 OLI for the development of LST and NDBI. The research was conducted for the past thirty years from 1990 to 2020. The resultant thermal comfort zones map was classified into four zones, Zone - I (Safe zone), Zone – II (Moderately safe zone), Zone – III (Risk Zone), and Zone -IV (Highly Risk Zone). The relation between the LST and NDBI is also revealed using correlation regression analysis in the study region. It has been noted that urban centre and high-density regions had greater LST than rural locations. The study finds that although this differs from one ecological zone to another and the distribution of LST intensity in the urban area depends on its changing LULC, various types of land cover within an urban region might impact the spatial pattern of urban LST. The main conclusions of this study might help policymakers understand the need of enabling more sustainable urban development in cities.

Local and landscape environmental heterogeneity drive ant community structure in temperate seminatural upland grasslands.

Environmental heterogeneity is an important driver of ecological communities. Here, we assessed the effects of local and landscape spatial environmental heterogeneity on ant community structure in temperate seminatural upland grasslands of Central Germany. We surveyed 33 grassland sites representing a gradient in elevation and landscape composition. Local environmental heterogeneity was measured in terms of variability of temperature and moisture within and between grasslands sites. Grassland management type (pasture vs. meadows) was additionally included as a local environmental heterogeneity measure. The complexity of habitat types in the surroundings of grassland sites was used as a measure of landscape environmental heterogeneity. As descriptors of ant community structure, we considered species composition in terms of nest density, community evenness, and functional response traits. We found that extensively grazed pastures and within-site heterogeneity in soil moisture at local scale, and a high diversity of land cover types at the landscape scale affected ant species composition by promoting higher nest densities of some species. Ant community evenness was high in wetter grasslands with low within-site variability in soil moisture and surrounded by a less diverse landscape. Fourth-corner models revealed that ant community structure response to environmental heterogeneity was mediated mainly by worker size, colony size, and life history traits related with colony reproduction and foundation. We discuss how within-site local variability in soil moisture and low-intensity grazing promote ant species densities and highlight the role of habitat temperature and humidity affecting community evenness. We hypothesize that a higher diversity of land cover types in a forest-dominated landscape buffers less favorable environmental conditions for ant species establishment and dispersal between grasslands. We conclude that spatial environmental heterogeneity at local and landscape scale plays an important role as deterministic force in filtering ant species and, along with neutral processes (e.g., stochastic colonization), in shaping ant community structure in temperate seminatural upland grasslands.

Energy-Efficient 3D Path Planning for Complex Field Scenes Using the Digital Model with Landcover and Terrain

Path planning is widely used in many domains, and it is crucial for the advancement of map navigation, autonomous driving, and robot path planning. However, existing path planning methods have certain limitations for complex field scenes with undulating terrain and diverse landcover types. This paper presents an energy-efficient 3D path planning algorithm based on an improved A* algorithm and the particle swarm algorithm in complex field scenes. The evaluation function of the A* algorithm was improved to be suitable for complex field scenes. The slope parameter and friction coefficient were respectively used in the evaluation function to represent different terrain features and landcover types. The selection of expanding nodes in the algorithm depends not only on the minimum distance but also on the minimum consumption cost. Furthermore, the turning radius factor and slope threshold factor of vehicles were added to the definition of impassable points in the improved A* algorithm, so that the accessibility of path planning could be guaranteed by excluding some bends and steep slopes. To meet the requirements for multi-target path planning, the improved A* algorithm was used as the fitness function of the particle swarm algorithm to solve the traveling salesman problem. The experimental results showed that the proposed algorithm is capable of multi-target path planning in complex field scenes. Furthermore, the path planned by this algorithm is more passable and more energy efficient. In this experimental environment model, the average energy-saving efficiency of the path planned by the improved algorithm is 14.7% compared to the traditional A* algorithm. This would be beneficial to the development of ecotourism and geological exploration.

Socioeconomic disparities in cooling and warming efficiencies of urban vegetation and impervious surfaces

Understanding the temperature regulation effects of urban vegetation and impervious surfaces, as well as how the effects interact with socioeconomic determinants of vulnerability to drive heat risk, is of critical importance for designing effective and equitable heat mitigation strategies. However, existing studies rarely compare temperature regulation efficiencies of diverse urban land cover types, nor do they examine whether distributions of urban vegetation and impervious surfaces could help reduce heat-related inequity by offering more cooling and less warming effects to marginalized communities. By employing satellite and socioeconomic data, this study quantified the cooling efficiencies of urban vegetation and warming efficiencies of impervious surfaces in 44 US cities, and then examined how these efficiencies change across neighborhoods of varying deprivation. We find that urban vegetation, especially trees, has strong cooling effects across different urban contexts, and the cooling efficiencies of urban trees and shrubs/grass are higher in neighborhoods of greater area deprivation. Conversely, buildings and roads produce the largest warming effects, and their warming efficiencies are either greater or at similar levels of magnitude in advantaged neighborhoods, when comparing with deprived neighborhoods. These findings suggest that strategically deploying green and built infrastructure could mitigate heat-related inequity in addition to improving health overall.

Analysis of the Evolution of Land-Use Types in the Qilian Mountains from 1980 to 2020

The Qilian Mountains (QMs), located in the northeast part of the Qinghai–Tibetan Plateau in China, have a fragile ecological environment, complex and sensitive climate, and diverse land-cover types. It plays an important role in the “Qinghai–Tibetan Plateau Ecological Barrier” and “Northern Sand Control Belt” in China’s “two screens and three belts” ecological security strategy. Based on land use data of 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we utilized GIS technology, land use dynamic degree, and land use transition matrixes to analyze the spatial and temporal evolution of land use in the QMs from 1980 to 2020. The results showed the following: (1) From 1980 to 2020, grassland, forest land, and unused land were the main land-use types in the QMs, and the proportion of construction land accounted for only 0.31% of all land-use types. (2) The single land use dynamic degree showed that the dynamic degree of construction land was the highest and the fastest change rate from 2010 to 2015. The comprehensive land use dynamic degree showed that the intensity of land-use change was relatively drastic in the three time periods of 1990–1995, 1995–2000, and 2015–2020. (3) The land-use types in the study area switched infrequently during 2000–2005, 2005–2010, and 2010–2015. (4) The main transition directions of land-use types were grassland and unused land to other land-use types. These changes altered the spatial distributions of different land-use types. The study is critical for understanding the spatial and temporal change patterns of land-use change in the QMs and providing guidance for the optimization of land use in the study area and the improvement of regional eco-environmental protection.