Raster Data Research Articles

Examining Land-Use Change Trends in Yucheng District, Ya’an City, China, Using ANN-CA Modeling

Land systems are those that most closely interact with human beings in terms of natural resources. The relationship between the simulation of their dynamic evolution and their microscopic and macroscopic elements is complex and nonlinear. In this study, we aimed to design a scientific model that could unify the macroscopic statistical distribution with microscopic individual movements, in order to simplify the model structure and definition of transformation rules. Taking Yucheng District, City, China, as a study site, we constructed an artificial neural network cellular automata (ANN-CA) model that uses neural network algorithms to predict the land area required to restrict the number of cellular automat (CA) space simulations. First, we took the raster data of the current land-use map of Yucheng District for the years 2001 and 2009, and the effect coefficients of slope, the river system, and other elements were used as initial data for the ANN-CA model. We then analyzed the superimposed land-use evolution law from 2001 to 2009. Next, within the error limits of the results for the 2017 neural network area prediction and the space center of gravity for each land type, we predicted the land-use changes for 2017 and determined the final land type for all grid units based on the set evolution rules and land conversion threshold. Finally, the land-use prediction simulation map for 2017 and actual land-use conditions were verified for quantitative and spatial accuracy. The results reveal that the model simulated the land-use changes for Yucheng District relatively accurately. It was also found that selecting the appropriate thresholds and random variable parameters improved the model’s simulation accuracy and that the land use in the study area exhibits a clear urbanization trend.

Map algebra on raster datasets represented by compact data structures

AbstractThe increase in the size of data repositories has forced the design of new computing paradigms to be able to process large volumes of data in a reasonable amount of time. One of them is in‐memory computing, which advocates storing all the data in main memory to avoid the disk I/O bottleneck. Compression is one of the key technologies for this approach. For raster data, a compact data structure, called ‐raster, have been recently been proposed. It compresses raster maps while still supporting fast retrieval of a given datum or a portion of the data directly from the compressed data. ‐raster's original work introduced several queries in which it was superior to competitors. However, to be used as the basis of an in‐memory system for raster data, it is mandatory to demonstrate its efficiency when performing more complex operations such as the map algebra operators. In this work, we present the algorithms to run a set of these operators directly on ‐raster without a decompression procedure.

Il sistema informativo territoriale della “Carta del rischio del patrimonio culturale italiano” e i siti UNESCO italiani: un processo virtuoso per il monitoraggio della vulnerabilità dei beni patrimonio mondiale

The territorial information system was born following the Law of 19 April 1990, n. 84 (in Gazz. Uff., 26 April, n. 96), as an organic plan for the inventory, cataloging and elaboration of the Risk Charter for cultural heritage: first interventions. The system implements the pilot plan for the planned conservation of the cultural heritage of Umbria, a study promoted by Giovanni Urbani, then director of the Central Institute of Restoration. The Risk Map is a GIS composed of an alphanumeric database and the cartographic component, capable of exploring, superimposing and processing information relating to potential territorial and environmental risk factors linked to cultural heritage. The Risk Card system has two levels of risk. The first relates the concentration of assets and the danger of the territory, in order to evaluate, following a calamitous event, the probability of loss of the cultural heritage (Territorial risk). The second, on the other hand, evaluates the risk of loss of the individual cultural property and is implemented through the filing on the field of the vulnerability of cultural assets (evidenced by the state of conservation) and the relationship with the territorial danger around the property (Local risk). The CdR system produces and exposes the data present with WMS services (raster data), architectural and archaeological real estate (punctual, linear and polygonal), underwater archaeological files, historical centers and aggregated urban units of historical centers. It also has WFS services (vector data) of architectural and archaeological real estate (punctual, linear and polygonal), containers of movable goods (punctual), historical centers and aggregated urban units of historical centers. Since 2012, the Risk Charter has been in the interoperability of the Ministry of Culture within the territorial computerized system Constraints in the network, with which it increases the registry and georeferencing of cultural assets present for various reasons in the SigecWeb (ICCD) and Protected Assets systems (DG ABAP). Thanks to the agreement signed between the Service II - UNESCO Office of the General Secretariat and the Directorate General for Security of the Ministry of Culture, the system will be used for the assessment of the territorial risk of Italian UNESCO sites, but also, for the first time, to support the request for registration of a site on the World Heritage List. This is the candidacy of the “Via Appia. Regina Viarum "for which, with the support of ISPRA, the levels of territorial danger will be analyzed in detail, with reference to both natural disasters (earthquake, landslides, etc.) and to problems deriving from simple atmospheric agents and from pollution. In addition, the data on the state of conservation of the ancient route and the assets related to it will be collected with the appropriate cards of the Risk Card, which will allow the processing of vulnerability indicators and local risk indicators, which will be proposed to the UNESCO for monitoring the site over time.

Impact of stand- and landscape-level variables on pine wilt disease-caused tree mortality in pine forests.

Pine wilt disease (PWD) outbreaks have affected extensive areas of South China's forests, but the factors explaining landscape patterns of pine mortality are poorly understood. The objective of this study was to determine the relative importance of stand structure, topography, landscape context, and beetle pressure in explaining PWD severity. During 2020-2021, we identified 66 plots based on mapped PWD infestation severity. We built PWD infestation maps for 2019-2021 through field surveys. Stand structure and topography were obtained from Forest Resources Management 'One Map' and elevation raster data. We then used 'One Map' and PWD infestation maps to determine landscape context and beetle pressure variables at different spatial scales. The relative importance of 12 explanatory variables was analyzed using multi-model inference. In this study, we show that: (i) 1 km was the best spatial scale related to pine mortality, and (ii) models including landscape context and beetle pressure were much better at predicting pine mortality than models using only stand-level variables. Landscape-level variables, particularly beetle pressure, were the most consistent predictors of subsequent pine mortality within susceptible stands. These results may help forest managers identify locations vulnerable to PWD and improve existing strategies for outbreak control. © 2023 Society of Chemical Industry.

A COMPARISON THROUGH TREE EXTRACTION IN IMAGE-SPACE AND OBJECT-SPACE

Abstract. In various studies trees have been extracted and their conditions have been examined through different detection algorithms from two main data sources including (a) point cloud and (b) raster data. The output of tree extraction is the input of the next processing steps, and the importance of these outputs is proved more than before. Tree Extraction (TE) has many applications in biomass estimation, CHM extraction, etc. All of which require high accuracy and the correct position of the trees. therefore, in this study, a comparison between tree extraction algorithms in two common sources of data has been conducted. As for the raster data, all bands are first co-registered. Afterward, the trees are separated from the background by using image processing techniques such as changing the image color space and weighted averaging on different bands. Finally, TE algorithms such as watershed segmentation, valley following, local maxima, and image binarization were applied. As for the point cloud data, TE can be conducted in the object space to compensate for the methods used in the raster space with object detection algorithms e.g., the coherence between the two trees, etc. which have been discussed in detail in this paper. In the object space, three algorithms, region-based, surface normal, and Euclidean segmentation, were implemented and discussed on the same raster data set in the photogrammetric point cloud. The results show the higher accuracy of the region-based algorithm in object-space by more than 26% in comparison with the valley following algorithm in image space.

Global-scale parameters for ecological models

This paper presents a collection of environmental, geophysical, and other marine-related data for marine ecological models and ecological-niche models. It consists of 2132 raster data for 58 distinct parameters at regional and global scales in the ESRI-GRID ASCII format. Most data originally belonged to open data owned by the authors of this article but residing on heterogeneous repositories with different formats and resolutions. Other data were specifically created for the present publication. The collection includes 565 data with global scale range; 154 at 0.5° resolution and 411 at 0.1° resolution; 196 data with annual temporal aggregation over ~10 key years between 1950 and 2100; 369 data with monthly aggregation at 0.1° resolution from January 2017 to ~May 2021 continuously. Data were also cut out on 8 European marine regions. The collection also includes forecasts for different future scenarios such as the Representative Concentration Pathways 2.6 (63 data), 4.5 (162 data), and 8.5 (162 data), and the A2 scenario of the Intergovernmental Panel on Climate Change (180 data).

АРХІТЕКТУРА РОЗПОДІЛЕНОГО ПРОГРАМНОГО ЗАСТОСУНКУ ДЛЯ ГЕНЕРУВАННЯ ЛАНДШАФТІВ ПЛАНЕТОЇДІВ

The procedural generation of planetoids finds its place in the field of visualization of virtual worlds in video games, movies, and simulation tools. Due to the growing popularity of the application, the requirements for the quality, uniqueness, and scalability of visualization results are increasing, which, in turn, leads to higher requirements for hardware computing resources. This paper proposes a solution for the architecture of a software system for generating planetoid landscapes, based on a combination of a distributed computing system and the use of parallelism based on the Morsel-Driven Query Execution algorithm to overcome hardware limitations. The computing model includes the following components: a main server that supports gRPC connections; worker servers that perform the task of generating planetoid landscapes in parallel; a geospatial database containing vector data of generated planetoids (rivers and reservoirs; geographic regions – biomes, forests, and other road segments) binary storage of three-dimensional models that are superimposed on the generated planetoid landscape; tileset-storage for storing raster data required for a generation; users who use the software system for generating planetoid landscapes to realize their own goals. The use of software agents in the built system allows unifying a set of algorithms as a single entity used for a particular stage of landscape generation and solving the problem of software system extensibility. A distributed messaging system, a broker, is used to send and process requests using a topic per software agent sequence position. The broker utilizes load balancing to deliver landscape generation requests to background workers. To analyze the system’s performance, experiments were conducted with different numbers of background workers (1, 2: 4, 8; 16) and segment sizes of 512, and 2048 pixels. The lowest average time for generating one segment was obtained when the segment size was 512 pixels and the number of segments was 64. The average segment generation time for the above experiments ranged from 0.33 to 9.32 seconds. The integrated architectural solution allowed to reduce the CPU time by 2 to 5 times compared to a system that uses the approach of storing queries in the database. The solution’s efficiency is especially noticeable with large amounts of data, which is determined by the number of segments and their size in pixels.

Quantitative assessment of the relative impacts of different factors on flood susceptibility modelling: case study of Fez-Meknes region in Morocco

The mapping and assessment of flood susceptibility is an integral component of flood mitigation and prevention programs, by determining the most vulnerable regions and the associated characteristics that influence the flood susceptibility. Hence, the aim of the present study is to identify flood-prone areas in the Fez-Meknes region (Morocco) for the first time using a multicriteria approach, in particular the Analytical Hierarchy Process (AHP) technique and Geographic Information Systems (GIS). A total of fifteen conditioning factors for flooding were selected: distance to rivers, river network density, precipitation, flow accumulation, elevation, slope, plane curvature, TWI, aspect, NDVI, LULC, TRI, geology, soil type, and SPI. All factors were defined as raster data sets with a resolution of 30 x 30 m. The results showed that, the efficiency tests of the flood susceptibility map show a good accuracy using an area under the curve (AUC) by remarkably good number (0.90).in addition, LULC was recognized as the most significant factor, which is followed by the stream power index that affect the flood map.

Деградація земних покривів Харківської області внаслідок воєнних дій

Background. The analysis of trends in land cover changes will allow to detect trends in land cover changes, which will contribute to the understanding of the reasons for changes in natural ecosystems and the development of strategies for their preservation and restoration. The analysis of land cover changes can be useful for assessing the impact of anthropogenic and natural factors on land using, which is important for making decisions about the rational using of natural resources, especially in the post-war period. The purpose of the research is to allow to detect the trends of land cover changes in Kharkiv region as a result of hostilities, using the satellite materials and statistical data from open sources. Methods. For the research, materials from remote sensing of the Earth were utilized, including methods of their processing and visualization. M a t h e m a t i c a l and statistical methods were applied to process statistical information and obtain index images resulting from the processing of satellite images and the generation of thematic raster data. The article conducts an analysis of the main trends in land cover changes in the Kharkiv region, highlighting trends for the most significantly altered land cover types, specifically forests and agricultural lands, for the period 2015-2022. Results. A general trend since 2015 has been the reduction of forested areas and agricultural lands, with a significant decrease in 2022, as a result of military actions and their consequences. Land cover degradation due to military activities is currently a prominent trend, representing the primary tendency in land cover changes. As a result of military actions in the Kharkiv region, all natural resources have suffered: lands from pollution and contamination with various types of waste, atmospheric air from emissions due to explosions and fires, water bodies, forest resources, and fauna from enemy equipment, pollution, and deliberate destruction. Conclusions. For a more detailed study of land cover changes related to military activities, especially regarding the destruction of the humus layer, soil pollution with metal fragments and explosive residues, field research and the use of high-resolution aerial or satellite imagery are necessary.

Calculating Potential Green Area Impact on Land Uses to Estimate Future Green Areas in Land Use Planning

Purpose: Since green space becomes more significant in the greenhouse gas reduction, this study seeks to propose the potential green area impact that mean expected amounts and impacts of green space on land uses in the future in the planning phase. Method: To calculate the potential green areas impact, this study analyzes current characteristics of green spaces using land use spatial data and normalized difference vegetation index (NDVI) raster data. Five cities planned and developed in the 1990s and 2000s in Korea were selected, and the values of potential green area impact were calculated for each land use. The spatial unit used in the calculation was a 10 by 10 m unit cell considering the spatial resolution of NDVI raster data and combining spatial and raster data in the cell unit. Result: This study proposed

Delineation of Ground Water Recharge Potential Zones in Lahore District, Punjab, Using Remote Sensing and GIS Techniques

Lahore is the provincial capital of Punjab and the second most populous city in Pakistan. Aquifer rechargerates have been continuously decreasing over a long period of time, which has caused a significant decline in the watertable level. Rapid population growth, urban development, and industrialization have all raised the demand for watersupplies. Due to significant infrastructural development, a considerable portion of the land is now impervious andrainfall now drains as surface runoff rather than recharging the aquifer level. Average annual rainfall, one of theprimary sources used to replenish the Lahore aquifer, is insufficient to prevent the depletion of the water table, andRiver Ravi stays almost dry except in rainy seasons. Geographic information system (GIS) and remote sensing (RS)techniques are used to find suitable areas for replenishment in the Lahore aquifer system in order to enhancesustainability and prevent decreasing groundwater levels. Distance from the water channel, land use/land cover(LULC), slope, geology, drainage density, rainfall, lineament density, and soil type are the eight layers that have beenintegrated with the GIS overlay analysis. Thematic maps are generated using both conventional and remote sensingdata. These maps are eventually converted to raster data. Very good, good, moderate, poor, and very poor are the fivezones that have been delineated. A good to very good

Identification of Suitable Sites for Rainwater Harvesting Using GIS in Kandahar Province, Afghanistan

Objectives: This study aims to identify the potential areas for rainwater harvesting in Kandahar, Afghanistan. Methods: This study used GIS-based RS techniques. Satellite images were used to plan multiple thematic layers, such as lithology, slope, land use/cover, stream order, and soil texture. These layers were then converted into raster data using the raster converter feature of the ArcGIS 10.4 program. In order to come up with a site suitability map for water harvesting structure, multicriteria analysis was then carried out on the different layers, each layer’s characteristics were assigned weights as provided by experts in past research reviews. Owing to their power and their respective class weights, different layers were then overlaid. Findings: The findings showed that the catchment was grouped into categories as ’high’, ‘moderate’ and ‘low’ zones with regard to the allocated weighting of different thematic layers using weighted overlay. Twenty zones of rainwater harvesting structures were identified from the WH suitability map for the various structures for water harvesting. The suitability map helped to select water harvesting structures such as percolation tanks, storage tanks, control dams, and stop dams to be installed at the different identified sites. The potential sites of rain water harvesting identified were Shorabak, Boldak, Marrurf, Arghistan-1, Arghistan- 2, Arghistan-3, Khakres, Ghorak, Maiwand-1 and Maiwand-2 in the Kandahar Province Afghanistan. Novelty: These findings indicate that high potential zones would play a key role in the future expansion of the production of drinking water and irrigation in the study region. The suitability map is useful to hydrologists, decision-makers, and planners for quickly identifying areas with the highest potential for harvesting rainwater. Keywords: Rainwater harvesting; ground water; AHP approach; GIS; Kandahar; Afghanistan

Cropland-grassland use conversions in the agro-pastoral areas of the Tibetan Plateau: Spatiotemporal pattern and driving mechanisms

Cropland-grassland use conversion analysis constitutes crucial research on regional land and global change science, having important implications for regional ecology, food security and the well-being of farmers and herders. However, relevant research is rare, and limited attention has been given to the Tibetan Plateau. Therefore, based on land use raster data, this study utilized the land use conversion matrix method to investigate the spatial and temporal patterns of cropland-grassland conversions during 1990–2020 in the agro-pastoral areas of the Tibetan Plateau.Then this research further identified the key natural geographical and socioeconomic driving forces of cropland-grassland conversions, and examined the mechanisms and spatial heterogeneity of their driving effects by using the geographically weighted regression model. The results showed that cropland-grassland conversions temporally presented the “slow-slow-rapid” changes in 1990–2020 and were spatially clustered in the eastern semipastoral areas. Through mechanisms of ecological equilibrium, resource carrying, population mobility, interest-driven and macroregulation, natural geographical and socioeconomic factors within the cropland-grassland conversion system interacted differently in direction and extent with obvious spatial heterogeneity. Regional increasing rainfall, temperature and topographic relief expanded the extent of cropland use, coupled with the effects of distance to transportation and water sources and increased rural employment, causing widespread and profound land reclamation of grassland-to-cropland, further encouraged by cropland balance policy, while climate change and distance from inhabited settlement and transportation promoted cropland-to-grassland conversion, exacerbated by policies on nature reserve designation and cropland balance, and the fact that rapidly increasing urbanisation in the east accelerated cropland abandonment by diversifying nonfarm employment livelihood strategies. Value added in agriculture and livestock and agricultural machinery inputs tended to restrict cropland-grassland conversions by promoting their internal tapping into intensive and potential use. “Key place-based” and differentiated management policies were proposed to promote sustainable cropland-grassland use conversions and regional development.

Detection of Coastline Changing by Using Remote Sensing Imagery (Case Study in Talawi District, Tanjung Tiram District, Lima Puluh Pesisir District Batu Bara Regency)

The high pressure in utilizing coastal areas and oceans causes in coastal damage. The coastline is the confluence of land areas and sea areas, where the existence of the line and the position of the coastline can change and undergo changes over time, and the position of the existence of the line is not fixed. The purpose of this study is to find out the changes in the coastline of the Coal District, Talawi District, Tanjung Tiram District and Lima Puluh Pesisir District in 2017-2022. The data analysis technique was used in this study is quantitative descriptive analysis. The data processing includes: Data Acquisition using Satellite Imagery (Raster) data. The data is used by satellite imagery was selected based on 2017 until 2022 coverage representatives, the lowest percentage of cloud cover in the study area. Supporting Spatial Data (vectors). The Digital Shoreline Analysis System (DSAS) is a plug in for esri ArcGIS 10.4 - 10.6 desktops to calculate historical coastline change rate statistics. Based on the results of coastline delianiation, it can be seen that the length of coastal gasis in 2017 in The Lima Puluh Pesisir District is 10.48 Km, in 2022 there was a decrease to 10.42 Km while based on RBI data on a scale of 1:50,000 in the 2014 update year was 17.37 Km. The length of the coastline of Tanjung Tiram District in 2017 was 24.31 Km, there was a decrease in 2022 to 23.44 Km and based on RBI data of 16.90 Km.The Talawi District has the length of the coastline was 3.16 Km in 2017, there is an increase in 2022 to 3.22 Km while the coastline on the RBI map was 3.23 Kilometers in length.

Deep learning on multi-view sequential data: a survey.

With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.

A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion

Engineering geological conditions include the nature of rock and soil, geological structure, landform, hydrogeological conditions, and adverse geological processes. Among them, faults, fissures, folds, karst, and lithology changes seriously affect the safety and construction cost of mountain tunnels, hydraulic tunnels, and other projects. For this reason, a new method based on feature fusion is proposed to detect the geological anomalies in London and Sheffield. It established a 3D raster data model oriented to attribute information modeling and visualization of urban underground space to obtain geological data. Based on this acquired data, authors adopted the feature-level fusion extraction method based on the multi-attribute geological abnormal body to extract, fuse, fill and surface the multi-attribute data of underground space geological data. Smooth processing can realize the detection of abnormal geological bodies in underground space. It has been proved that this method can be used in geological data display, feature extraction, feature fusion, and abnormal physical examination.

Non-linear effects of industrial structure on urban haze pollution: A test and extension of the environmental Kuznets curve

Whether the Environmental Kuznets curve is established in haze pollution has an important policy. Based on the PM2.5 concentration raster data of 283 cities in MODIS and MISR, this paper empirically examines the non-linear effects of industrial structure upgrading on haze pollution using a spatial econometric model, and further tests and extends the environmental Kuznets curve using a threshold model. The study finds that there is a significant spatial spillover effect on haze pollution between cities, an inverted ''U'' shaped relationship between industrial upgrading and haze pollution, confirming the environmental Kuznets curve, and that the effect of industrial upgrading on haze pollution is mainly a direct effect, while the indirect effect is not significant. Further discussion in this paper reveals that there is a threshold effect of industrial upgrading on haze pollution; the traditional environmental Kuznets curve can be refined and extended: on both sides of the inflection point, there may be both positive and negative effects of economic development on environmental pollution, and the environmental Kuznets curve is the result of the combined effect of the two different effects.

Urban intensive land use and enterprise emission reduction: New micro-evidence from China towards COP26 targets

Energy saving and emission reduction are key elements in promoting global sustainable development. From the perspective of urban land resource utilization, this study examines how the intensive use of land resources affects enterprise emission reduction and the mechanism through which it achieves this impact. Based on land use raster data from 2000 to 2017, an urban intensive land use indicator for China is constructed from the “land concentration and continuity” perspective, which is then combined with the 2001–2014 Chinese industrial enterprise database and enterprise pollution emission database for regression analysis. The results show that the intensive use of urban land resources significantly reduces the intensity of enterprise pollution emissions. Moreover, this result remains robust after a series of tests, including substitute variables, elimination of endogeneity, and limited samples. The mechanism test proves that the emission reduction effect of urban intensive land use can be realized through two transmission channels—scale and technology effects. Further heterogeneity analysis results show that central cities and large enterprises benefit more from the intensive use of urban land to promote enterprise emission reduction. This study uses high-resolution land raster data innovatively to construct an accurate urban intensive land use index and theoretically evaluates the impact, mechanism, and heterogeneity of intensive land use on enterprise emission reduction in China. This study thus not only fills an existing theoretical research gap, but also helps promote the mechanism between the intensive use of urban land resources and the green development of enterprises.

Remote sensing analysis on primary productivity and forest cover dynamics: A Western Ghats India case study

Tropical forest ecosystems are among the most essential habitats on Earth for conserving biological diversity and short-term climate regulation. For this reason, they are key areas of conservation policies in the world. In this paper, we aim to investigate the dynamics of forest cover and their changes in primary productivity by empowering information on historical forest management and fieldwork research with remote sensing vegetation monitoring methods. The study area falls within the central portion of the Indian Western Ghats, a global biodiversity hotspot. In particular, part of the analysis was performed on the Kadamakal Reserve Forest and Pushpagiri Wildlife Sanctuary, which harbours an endemic low elevation dipterocarp evergreen forest. This area was managed by selective logging and became fully protected in 1984. We performed multiple time series macroscale analyses between 1999 and 2020 on the Indian Central Western Ghats region, using satellite products at 1 km spatial resolution from the VITO Copernicus Global Land Service on Dry Matter Productivity, Fraction of Absorbed Photosynthetically Active Radiation, and Normalised Difference Vegetation Index. We also performed a very-high spatial resolution Normalised Difference Vegetation Index differential analysis between 2021 and 2016 with Sentinel 2-L2A products to investigate forest dynamics within the reserve. At the 1 km spatial resolution has been found an increase in all three vegetation indices, by employing the LOESS statistical method for the smoothed transition autoregressive model of raster data medians of our datasets. The boxplot raster distribution analysis also highlighted a significant imbalance in dry matter productivity in the last decade (2010−2020) comparing the previous one (1999–2009). The second part of the analysis, at 10 m spatial resolution within the reserve forest, revealed a growth in the vegetation cover on the top of the Pushpagiri Mountain ridge and in a previously landslide area. The study found new erosion channels down to the upper plateau on the South-West side of the reserve due to an increment of the run-off processes during the monsoon period. This satellite analysis highlighted generalised positive vegetation trends in the Central Western Ghats, India, over the last twenty-two years, enhancing an improvement in the ecosystem functioning and carbon storage ecosystem service. Notably, through this work, we also developed a standardised and open-access framework to monitor the vegetation remotely (SVIT) during periods of forest inaccessibility for fieldwork sampling.

Pavement Crack Detection and Clustering via Region-Growing Algorithm from 3D MLS Point Clouds

Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.