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This study evaluates and contrasts changes in vegetation cover over three decades in two forest areas in the State of Chihuahua in northern Mexico with different management statuses: one with sustainable forest management and the other protected as a Flora and Fauna Protection Area. The hypothesis proposed that both areas would have maintained or increased their vegetation cover since 1995. Satellite images from the periods 1995–2008, 2008–2014, 2014–2022, and 1995–2022 were analyzed. The results showed that Ejido El Largo y Anexos significantly increased forest areas and reduced grasslands due to sustainable management practices, with a notable expansion of pine–oak and pine forests. In contrast, the Tutuaca Flora and Fauna Protection Area experienced a notable loss of oak and oak–pine forests, suggesting ineffectiveness in its conservation policies. However, there was less loss in Douglas Fir forests, indicating some effective protection efforts. The comparison reveals opposing dynamics: while Ejido El Largo y Anexos demonstrates success in sustainable management, the Tutuaca Flora and Fauna Protection Area faces conservation challenges. In conclusion, this study highlights the need for active management approaches to maintain ecosystem cover and functionality. [ABSTRACT FROM AUTHOR]

Remote sensing is an essential tool for monitoring and mapping vegetation. Multitemporal analysis of satellite imagery allows for the observation of landscape evolution, fostering the development of management plans aimed at preserving natural resources and maintaining biodiversity. The objective of this article was to present a multitemporal analysis of land use and land cover in the municipality of São José dos Campos, SP, with a focus on vegetation cover, using TM-Landsat 5 and OLI-Landsat 8 images, over a period of 10 years (2010 to 2020). These images were classified into the following classes: Cerrado (savanna), Atlantic Forest, anthropogenic areas, reforestation, agricultural areas, water bodies, and urban areas, using supervised classification techniques in ArcGIS software. The results showed an increase of 4.52% in the urban area, an increase of 314.17% in the silviculture areas and consequently the reduction of the original vegetation cover of the municipality, where the areas of Cerrado were reduced by 88.9% and those of Atlantic Forest by 8.57%, evidencing the need for the implementation of projects and public policies that favor the preservation of the natural plant remnants of São José dos Campos and the protection of endemic species belonging to the present biomes in the region. [ABSTRACT FROM AUTHOR]

In response to the escalating demand for mineral resources and the imperative for sustainable management of natural assets, the development of effective methods for monitoring mining excavations is essential. This study presents an innovative decision-making model that employs a suite of spectral indices for the sustainable monitoring of mining activities. The integration of the Combinational Build-up Index (CBI) with additional spectral indices such as BRBA and BAEI, alongside multitemporal analysis, enhances the detection and differentiation of mining areas, ensuring greater stability and reliability of results, particularly when applied to single datasets from the Sentinel-2 satellite. The research indicates that the average accuracy of excavation detection (overall accuracy, OA) for all test fields and data is approximately 72–74%, varying with the method employed. Utilizing a single CBI index often results in a significant overestimation of producer's accuracy (PA) over user's accuracy (UA), by about 10–14%. Conversely, the introduction of a set of three complementary indices achieves a balance between PA and UA, with discrepancies of approximately 1–3%, and narrows the range of result variations across different datasets. Furthermore, the study underscores the limitations of employing average threshold values for excavation monitoring and suggests the adoption of dedicated monthly thresholds to diminish accuracy variability. These findings could have considerable implications for the advancement of autonomous and largely automated systems for the surveillance of illegal mining excavations, providing a predictable and reliable methodology for remote sensing applications in environmental monitoring. [ABSTRACT FROM AUTHOR]

Human behavior in relation to land use often has negative effects on this resource, resulting in the loss of fertility and its degradation. This, in turn, leads to a decrease in vegetation cover. Four Landsat 7 images were used corresponding to the years 2010, 2015, 2020 and 2021. The results obtained reveal that the values of the Normalized Difference Vegetation Index (NDVI) present similar trends in the coverage of the years 2015 to 2021. In this analysis, the presence of scarce and dispersed vegetation stands out. In 2015, this category experienced an increase of 0.98% compared to 2010, totaling approximately 28,866.9 hectares and covering a total area of 72,964.76 hectares. However, it is still only 4,437 hectares away from covering the entire territorial area studied. The most significant loss rate is observed in 2015, with -9.6% of dense vegetation. It is evident that the pressure of human activities on soil cover has generated a considerable increase in scarce and scattered vegetation. The spatial analysis allowed us to identify the areas with reduction or increase during the study years. [ABSTRACT FROM AUTHOR]

The change in land use resulting from human activities occurs at both global and local scales. The present research focuses on the multi-temporal analysis of land use change on Santa Cruz Island, Galápagos. Therefore, the analysis of land uses and their transition with vegetation cover was proposed to examine their evolution. Geographic Information Systems and the Supervised Classification technique were employed to identify changes in the terrain over time and the factors responsible for these alterations. In order to obtain accurate information about existing land uses in the study area, free satellite images were used, allowing the distinction of four distinct classes. Through this analysis, it was possible to determine the evolution of land use over a period of 32 years, spanning from 1991 to 2023. The results revealed that the classified areas underwent modifications due to both natural and human-induced factors, including livestock and agricultural activities, as well as demographic growth. To validate the obtained data, a confusion matrix was conducted, and the Kappa index was applied. These procedures demonstrated the robustness and agreement of the model used, ensuring high accuracy compared to reality. In conclusion, this study provides a comprehensive view of land use change on Santa Cruz Island, Galápagos, and offers relevant information about the factors that have contributed to such changes over time. The results have significant implications for sustainable land management and ecosystem conservation in this important region. [ABSTRACT FROM AUTHOR]

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Landslides are among the most common type of hazard that affect mountainous regions. While the impact of the single landslide is often localized and limited to the influence area, in some cases, the occurrence of major events can result in significant and long-lasting social, economic, and environmental impacts that extend beyond the area directly affected by the event. These major landslides involve the sudden detachment of large volumes of rock mass and cause significant disturbance of in-situ stress field due to slope debuttressing and reduction in lateral support, which often result in conditions of limit equilibrium affecting the remaining part of the slope. Effects of such limit equilibrium conditions can range from a long term, gradual morphological evolution of the slope due to progressive detachment of material, to the development of multistage landslides, involving the failure of volumes of rock mass similar in magnitude and impacts to the original event. In this work, we investigate the post-failure morphological evolution of the daylighting rupture surface and deposit of one of the most important historical landslides, the 1963 Vajont Slide. A preliminary investigation of a pair of airborne laser scanner (ALS) datasets, collected in 2017 and 2023, is undertaken to assess and compute the change in elevation across selected areas within the rupture surface. Based on a three-dimensional change detection analysis, the observed volumetric changes of selected, inferred rockfall events are estimated. Terrestrial and airborne photographs are also used to identify the unstable volumes that have progressively detached, as well as the damage features that outlined these unstable blocks. Finally, we discuss the long-term evolution of the slope with focus on progressive damage accumulation and its spatial relationship with inherited, tectonic structures. Ultimately, this contribution is intended to highlight the important role of post-slope failure damage accumulation on the long-term stability of rock slopes, emphasizing the critical role that post-failure monitoring and analysis can play in outlining the residual landslide hazard and, in some instances, the potential development of multi-stage landslides. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAV) for forest monitoring has grown significantly in recent years, providing information with high spatial resolution and temporal versatility. UAV with multispectral sensors allow the use of indexes such as the normalized difference vegetation index (NDVI), which determines the vigor, physiological stress and photosynthetic activity of vegetation. This study aimed to analyze the spectral responses and variations of NDVI in tree crowns, as well as their correlation with climatic factors over the course of one year. The study area encompassed a 1.6-ha site in Durango, Mexico, where Pinus cembroides, Pinus engelmannii, and Quercus grisea coexist. Multispectral images were acquired with UAV and information on meteorological variables was obtained from NASA/POWER database. An ANOVA explored possible differences in NDVI among the three species. Pearson correlation was performed to identify the linear relationship between NDVI and meteorological variables. Significant differences in NDVI values were found at the genus level (Pinus and Quercus), possibly related to the physiological features of the species and their phenology. Quercus grisea had the lowest NDVI values throughout the year which may be attributed to its sensitivity to relative humidity and temperatures. Although the use of UAV with a multispectral sensor for NDVI monitoring allowed genera differentiation, in more complex forest analyses hyperspectral and LiDAR sensors should be integrated, as well other vegetation indexes be considered. [ABSTRACT FROM AUTHOR]

Flooding is a common natural disaster in Purworejo District, which can be caused by the overflowing of the Bogowonto River. The use of multitemporal analysis with Synthetic Aperture Radar (SAR) images, such as Sentinel-1, has the potential to aid in flood inundation detection for disaster mitigation in the area. However, there has not been any research examining the compatibility of flood inundation detection using multitemporal Sentinel-1 images with the flood susceptibility characteristics of the Bogowonto River. This study aims to evaluate this using a SWOT analysis. The results show that multitemporal analysis using Sentinel-1 images is not suitable for detecting flood inundation in the Bogowonto River due to difficulties in finding the right acquisition time at the time of the flood event. The duration of floods in the Bogowonto River is approximately 1-2 days, while the earliest reacquisition time for Sentinel-1 images for this study is 12 days. Additionally, Sentinel-1 images using band C have limitations in detecting floods under vegetation.

Landscape restoration activities must be conducted through a transdisciplinary process, integrating social, economic, environmental, and governance aspects. Combining visions from the natural and social sciences is a challenge in highly complex territories, where unique ecosystem characteristics, economic processes, stakeholders of diverse nature, and different normativity converge. The harmonization of multiple techniques, such as multicriteria spatial analysis, expert knowledge elicitation, and social mapping, allows for an approach to defining landscape restoration areas in complex regions. This paper employs multiple techniques to define ecosystem restoration areas in a complex Colombian Andes landscape, integrating ecological and social components for sustainable development. We observed that areas of high and very high feasibility for ecological restoration, encompassing 179.5 hectares (4.84% of the study area), are predominantly located near primary forests. Although some areas have a low feasibility for conservation processes, they should not be disregarded as they still require protection. Landowners prioritize watershed and soil restoration as the most important landscape restoration activity due to their interest in improving water-related ecosystem services. This proposal enables the identification of areas with a higher restoration potential at the property level, facilitating prioritization and investment allocation for future implementation. [ABSTRACT FROM AUTHOR]

Hyperspectral (HS) image change detection (CD) is an integral component of multitemporal remote-sensing (RS) Earth observation research. However, the existing HS image CD technology still has some problems, such as insufficient effective information extraction and weak correlation between shallow information and deep information, and so on. This letter proposes a new approach called attention-guided feature fusion network for multitemporal HS image CD (AgF2Net). This method is capable of efficiently retrieving and combining spatial–spectral (SS) features extracted from both shallow and deep layers of HS images, thereby enhancing the network’s capability to capture features from multitemporal HS images. The attention-guided enhanced joint feature extraction strategy is used to obtain a better change discriminative feature representation, and the multilevel features extracted from the backbone network are combined between spatial information and spectral information. The integrated channel feature fusion module (ICFFM) not only solves the problem of insufficient feature fusion at different levels, but also strengthens effective semantic information and forms features with more discriminative ability, while also realizing the advantages of multiple features and enhancing the network’s robustness and the accuracy of CD results. According to experimental results obtained from three publicly available datasets for detecting changes in HS images, the findings indicate that the proposed AgF2Net outperforms most advanced state-of-the-art (SOTA) methods. The source code of the AgF2Net will be public on https://github.com/NWH/AgF2Net. [ABSTRACT FROM AUTHOR]

In response to the escalating demand for mineral resources and the imperative for sustainable management of natural assets, the development of effective methods for monitoring mining excavations is essential. This study presents an innovative decision-making model that employs a suite of spectral indices for the sustainable monitoring of mining activities. The integration of the Combinational Build-up Index (CBI) with additional spectral indices such as BRBA and BAEI, alongside multitemporal analysis, enhances the detection and differentiation of mining areas, ensuring greater stability and reliability of results, particularly when applied to single datasets from the Sentinel-2 satellite. The research indicates that the average accuracy of excavation detection (overall accuracy, OA) for all test fields and data is approximately 72–74%, varying with the method employed. Utilizing a single CBI index often results in a significant overestimation of producer’s accuracy (PA) over user’s accuracy (UA), by about 10–14%. Conversely, the introduction of a set of three complementary indices achieves a balance between PA and UA, with discrepancies of approximately 1–3%, and narrows the range of result variations across different datasets. Furthermore, the study underscores the limitations of employing average threshold values for excavation monitoring and suggests the adoption of dedicated monthly thresholds to diminish accuracy variability. These findings could have considerable implications for the advancement of autonomous and largely automated systems for the surveillance of illegal mining excavations, providing a predictable and reliable methodology for remote sensing applications in environmental monitoring.

With the increasing volume of collected Earth observation (EO) data, artificial intelligence (AI) methods have become state-of-the-art in processing and analyzing them. However, there is still a lack of high-quality, large-scale EO datasets for training robust networks. This paper presents AgriSen-COG, a large-scale benchmark dataset for crop type mapping based on Sentinel-2 data. AgriSen-COG deals with the challenges of remote sensing (RS) datasets. First, it includes data from five different European countries (Austria, Belgium, Spain, Denmark, and the Netherlands), targeting the problem of domain adaptation. Second, it is multitemporal and multiyear (2019–2020), therefore enabling analysis based on the growth of crops in time and yearly variability. Third, AgriSen-COG includes an anomaly detection preprocessing step, which reduces the amount of mislabeled information. AgriSen-COG comprises 6,972,485 parcels, making it the most extensive available dataset for crop type mapping. It includes two types of data: pixel-level data and parcel aggregated information. By carrying this out, we target two computer vision (CV) problems: semantic segmentation and classification. To establish the validity of the proposed dataset, we conducted several experiments using state-of-the-art deep-learning models for temporal semantic segmentation with pixel-level data (U-Net and ConvStar networks) and time-series classification with parcel aggregated information (LSTM, Transformer, TempCNN networks). The most popular models (U-Net and LSTM) achieve the best performance in the Belgium region, with a weighted F1 score of 0.956 (U-Net) and 0.918 (LSTM).The proposed data are distributed as a cloud-optimized GeoTIFF (COG), together with a SpatioTemporal Asset Catalog (STAC), which makes AgriSen-COG a findable, accessible, interoperable, and reusable (FAIR) dataset. [ABSTRACT FROM AUTHOR]

Constant monitoring of airports and aviation bases has become one of the priorities in today's strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change. [ABSTRACT FROM AUTHOR]

Trees outside forests (TOFs) are important landscape features that provide numerous functions (ecosystem services) that are not valued due to a lack of knowledge about these resources. The aim of this study was to evaluate the changes induced by the anthropogenic land use change in relation to their effects on TOFs. The dynamics of TOFs were examined through detailed photointerpretation mapping and characterization by land use/land cover and other environmental variables. The landscape function of TOFs and relative dynamics were analyzed, revealing landscape simplification due to the loss of TOFs, both in number and area, and a relative loss of connectivity. In 2000, TOFs accounted for 2.6% more forest area than mapped in the regional forest map; in fifteen years, about 30% of the total area has been lost, one-third of which has been converted to forest and the remainder permanently lost. The causes of the loss of TOFs are partly due to the abandonment of agricultural land, but also to the actions of farmers who remove these elements for various reasons. In protected areas (Natura 2000 network), the loss is less due to the different characteristics of land use/cover and land management. [ABSTRACT FROM AUTHOR]

Context: Given the increasing population concentration, both the proximity of the town of Madrid to the city of Bogotá and the growing metropolitan area around the latter have a significant influence on the increased urbanization and the change in land use in the town of Madrid, Cundinamarca. Objective: The objective of this study was to carry out a multitemporal analysis of the change in coverage and land use caused by urban expansion in the municipality of Madrid between 1977 and 2020. Methodology: Satellite images were observed through supervised classification, following the Corine Land Cover methodology, which was adapted for the Colombian context, and utilizing geographic information systems. Area variation analyses were performed for each classification in each of the evaluated years. Results: The data obtained show that the forest cover area and seminatural zones have been reduced by 86% in a period of 43 years, and that artificial territories have progressively become predominant in each of the years, even reaching a coverage greater than 50% of the municipality area. The agricultural territories have maintained 66,95% of the surface of the municipality, and they correspond the land use with the largest occupied area for the evaluated period, which shows the importance of the soil for the economic development of the population. Conclusions: This situation shows that population growth and urban expansion cause variations in coverage and land uses. Their valuation is an aspect that must be considered when elaborating development plans for municipal entities.

Acute and early symptoms of forest dieback linked to climate warming and drought episodes have been reported for relict Abies pinsapo Boiss. fir forests from Southern Spain, particularly at their lower ecotone. Satellite, orthoimages, and field data were used to assess forest decline, tree mortality, and gap formation and recolonization in the lower half of the altitudinal range of A. pinsapo forests (850-1550 m) for the last 36 years (1985-2020). Field surveys were carried out in 2003 and in 2020 to characterize changes in stand canopy structure and mortality rates across the altitudinal range. Time series of the Normalized Difference Vegetation Index (NDVI) at the end of the dry season (derived from Landsat 5 and 7 imagery) were used for a Dynamic Factor Analysis to detect common trends across altitudinal bands and topographic solar incidence gradients (SI). Historical canopy cover changes were analyzed through aerial orthoimages classification. Here we show that extensive decline and mortality contrast to the almost steady alive basal area for 17 years, as well as the rising photosynthetic activity derived from NDVI since the mid-2000s and an increase in the forest canopy cover in the late years at mid and high altitudes. We hypothesized that these results suggest an unexpected resilience in A. pinsapo forests to climate change-induced dieback, that might be promoted by compensation mechanisms such as (i) recruitment of new A. pinsapo individuals; (ii) facilitative effects on such recruitment mediated by revegetation with other species; and (iii) a 'release effect' in which surviving trees can thrive with fewer resource competition. Future research is needed to understand these compensation mechanisms and their scope in future climate change scenarios. [ABSTRACT FROM AUTHOR]

High urbanization and a consequent change in land cover can lead to a deterioration in air quality and generate impacts on public health. The objective of this paper is to provide a multitemporal analysis of the influence of particulate matter ≤ 10 μm (PM10) on human mortality from the land cover variation in a Latin American megacity. Six monitoring stations (monitoring daily PM10 concentration, increases in daily mortality (IDM), and land cover) were established throughout the megacity. The results suggest that for every 10% increase in vegetation cover, the daily PM10 concentration and IDM decreases by 7.5 μg/m3 and 0.34%, respectively. Moreover, it is evident that the monitoring station with the lowest vegetation cover (8.96 times) shows an increase of 1.56 times and 4.8 times in the daily PM10 concentration and IDM, respectively, compared with the monitoring station with the highest vegetation cover (46.7%). It is also suggested that for each increase of 100 inhabitants/hectare in population density, the daily PM10 concentration and IDM increases by 9.99 µg/m3 and 0.45%, respectively. Finally, the population densification of the megacity possibly implies a loss of vegetation cover and contributes to the increase in PM10 and IDM. [ABSTRACT FROM AUTHOR]

Coastal wetlands are biodiversity hotspots, highly threatened, and for which restoration actions have been widely implemented. Systematic monitoring of biodiversity after restoration actions on Mediterranean salt marshes vegetation needs further attention. We analyzed temporal changes in plant species composition and ecology in a restored brackish wetland on the Adriatic coast (Central Italy) by a re-visitation study of 33 historical plots (year 2010), newly collected after 10 years (2021), across a brackish mosaic composed by salt meadows, halophilous scrubs and salt steppes referable to three habitats of conservation concern in Europe (EU codes: 1410, 1420 and 1510*). Changes in species richness and cover, in the ecological characteristics of the mosaic and each habitat type were tested by comparing some ecological groups (e.g. diagnostic, alien and ruderal species) and Ellenberg bio-indicator values by a Mann-Whitney test. Similarity percentage procedure for identifying which species indicate temporal changes was also performed. After restoration, we observed a general improvement of the environmental quality of the brackish mosaic with the establishment of typical pauci-specific plant communities, a significant recovery of diagnostic species cover and a reduction of ruderal and alien ones. We also registered an increase in Ellenberg salinity and temperature values likely related also to coastal erosion and climatic change. The results of our study suggest that vegetation dynamics could be used to monitor coastal restoration trajectory in the Mid- and Long-Term local interventions. [ABSTRACT FROM AUTHOR]

Intensified human activities can augment soil organic carbon (SOC) losses from the world's croplands, making SOC a highly dynamic parameter both in space and time. Sentinel-2 spectral imagery is well placed to capture the spatiotemporal variability of SOC, but its capability has only been demonstrated for agricultural regions mostly located in Europe. Furthermore, most studies so far only used single-date images that resulted in spatially non-continuous SOC maps, hampering their ability to aid multiscale SOC assessments. Here, we aim to achieve spatially continuous mapping of SOC in croplands, by creating multitemporal bare soil composites that maximize cropland coverage in two regions of varying agroecosystems and landscape structure in the Northeast China Chernozem region and the Belgian Loam Belt. Bare soil pixels were extracted via spectral index thresholding that excluded contaminated pixels from external perturbance. Multitemporal soil composites were then obtained by averaging over multiple single-date bare soil images that were selected within pre-determined optimal time-windows, corresponding to the region-specific crop sowing periods when best possible surface conditions were expected. Results show that the optimal time-window filter ensured selective inclusion of single-date images that themselves yielded stable and robust SOC predictions across multiple years. Spectral-based models developed from multitemporal composites consistently produced better or similar prediction accuracies than single-date images for both study regions (R2: 0.52–0.62; RMSE: 0.17–0.21 g 100 g−1), while also achieved maximum cropland coverage (>82 %). Bootstrap modelling demonstrated that SOC mapping via multitemporal Sentinel-2 data was associated with small uncertainties. Investigations into the significant spectral bands that contributed to the prediction of SOC suggested that, regardless of the study regions, the physical relationship between spectral bands and SOC that predominantly exists for laboratory spectra is largely translated into Sentinel-2 platforms. This study highlights the widespread applicability of multitemporal Sentinel-2 remote sensing for effective and high-resolution SOC mapping, in order to detect localized soil degradation as well as to inform regional cropland management in diverse agroecosystems. [ABSTRACT FROM AUTHOR]

Landscape restoration activities must be conducted through a transdisciplinary process, integrating social, economic, environmental, and governance aspects. Combining visions from the natural and social sciences is a challenge in highly complex territories, where unique ecosystem characteristics, economic processes, stakeholders of diverse nature, and different normativity converge. The harmonization of multiple techniques, such as multicriteria spatial analysis, expert knowledge elicitation, and social mapping, allows for an approach to defining landscape restoration areas in complex regions. This paper employs multiple techniques to define ecosystem restoration areas in a complex Colombian Andes landscape, integrating ecological and social components for sustainable development. We observed that areas of high and very high feasibility for ecological restoration, encompassing 179.5 hectares (4.84% of the study area), are predominantly located near primary forests. Although some areas have a low feasibility for conservation processes, they should not be disregarded as they still require protection. Landowners prioritize watershed and soil restoration as the most important landscape restoration activity due to their interest in improving water-related ecosystem services. This proposal enables the identification of areas with a higher restoration potential at the property level, facilitating prioritization and investment allocation for future implementation.

Acute and early symptoms of forest dieback linked to climate warming and drought episodes have been reported for relict Abies pinsapo Boiss. fir forests from Southern Spain, particularly at their lower ecotone. Satellite, orthoimages, and field data were used to assess forest decline, tree mortality, and gap formation and recolonization in the lower half of the altitudinal range of A. pinsapo forests (850-1550 m) for the last 36 years (1985-2020). Field surveys were carried out in 2003 and in 2020 to characterize changes in stand canopy structure and mortality rates across the altitudinal range. Time series of the Normalized Difference Vegetation Index (NDVI) at the end of the dry season (derived from Landsat 5 and 7 imagery) were used for a Dynamic Factor Analysis to detect common trends across altitudinal bands and topographic solar incidence gradients (SI). Historical canopy cover changes were analyzed through aerial orthoimages classification. Here we show that extensive decline and mortality contrast to the almost steady alive basal area for 17 years, as well as the rising photosynthetic activity derived from NDVI since the mid-2000s and an increase in the forest canopy cover in the late years at mid and high altitudes. We hypothesized that these results suggest an unexpected resilience in A. pinsapo forests to climate change-induced dieback, that might be promoted by compensation mechanisms such as (i) recruitment of new A. pinsapo individuals; (ii) facilitative effects on such recruitment mediated by revegetation with other species; and (iii) a ‘release effect’ in which surviving trees can thrive with fewer resource competition. Future research is needed to understand these compensation mechanisms and their scope in future climate change scenarios.

Forests play a central role in the management of the Earth's climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios. [ABSTRACT FROM AUTHOR]

Small woods, linear tree formations, or scattered trees in agricultural areas are receiving increasing attention for their multifunctional role, especially if associated to cultural landscapes. Osaki Kodo's Traditional Water Management System for Sustainable Paddy Agriculture represents one of the most important cultural landscapes (satoyama) of Japan, also included by the FAO in the GIAHS (Globally Important Agricultural Heritage Systems) Programme. Here, local farmers surrounded their farmhouses with small woods, called igune, as a protection from the cold winter wind, creating a peculiar landscape characterized by an intensively cultivated plain dotted with small wood patches. The research aims at deepening the knowledge of igune, evaluating the landscape role and monitoring their changes in the last 20 years, through multitemporal and spatial analyses. In addition, a literature review has been performed to assess other Ecosystem Services (ESs) provided by igune within the study area. Despite the limited overall surface, 1737 igune and small woods currently characterize the area, with 72% of them having a surface smaller than 0.5 hectares. The multitemporal and spatial analyses show that their number, distribution, and spatial pattern remained almost completely unchanged in the last 20 years, testifying their key role in characterizing the local cultural landscape. Least-Cost Path analysis highlighted a crucial role in connecting the two forest nodes of the region, as 90% of the path passes inside more than 70 different igune and small woods. Literature review demonstrated that igune and other small woods still provide various ESs, including ecological network, habitat for various flora and fauna species, firewood, and byproducts, as well as cultural services. This maintenance of the traditional management in cultural forests is crucial not only to retain their landscape role, but mostly for the preservation of the related ESs, as changes in the management can lead to changes in horizontal and vertical structures, and in species composition. [ABSTRACT FROM AUTHOR]

The integration of field surveys, bibliographic research and multitemporal analysis of historical maps, aerial photographs and satellite images in a GIS environment, allowed the current and past geomorphological features of the old city of Alessandria and its surrounding areas, NW Italy, to be identified and mapped. Their analysis provided an overview of the geomorphological evolution of the city that is strictly related to the historical vicissitudes occurred since the Middle Ages. Nowadays, the most representative landforms and deposits characterizing the urban landscape result from human interventions and are associated with ancient military facilities and infrastructures, a historical man-made channel network no longer recognizable, the Tanaro riverbed channelization, and the urban sprawl occurred from the second half of the nineteenth century onwards. This study represents a useful tool for urban planning and management and for raising the citizens’ awareness of the urban-landscape geomorphological features and evolution, and therefore the geo-hydrological risk.

With the increasing volume of collected Earth observation (EO) data, artificial intelligence (AI) methods have become state-of-the-art in processing and analyzing them. However, there is still a lack of high-quality, large-scale EO datasets for training robust networks. This paper presents AgriSen-COG, a large-scale benchmark dataset for crop type mapping based on Sentinel-2 data. AgriSen-COG deals with the challenges of remote sensing (RS) datasets. First, it includes data from five different European countries (Austria, Belgium, Spain, Denmark, and the Netherlands), targeting the problem of domain adaptation. Second, it is multitemporal and multiyear (2019–2020), therefore enabling analysis based on the growth of crops in time and yearly variability. Third, AgriSen-COG includes an anomaly detection preprocessing step, which reduces the amount of mislabeled information. AgriSen-COG comprises 6,972,485 parcels, making it the most extensive available dataset for crop type mapping. It includes two types of data: pixel-level data and parcel aggregated information. By carrying this out, we target two computer vision (CV) problems: semantic segmentation and classification. To establish the validity of the proposed dataset, we conducted several experiments using state-of-the-art deep-learning models for temporal semantic segmentation with pixel-level data (U-Net and ConvStar networks) and time-series classification with parcel aggregated information (LSTM, Transformer, TempCNN networks). The most popular models (U-Net and LSTM) achieve the best performance in the Belgium region, with a weighted F1 score of 0.956 (U-Net) and 0.918 (LSTM).The proposed data are distributed as a cloud-optimized GeoTIFF (COG), together with a SpatioTemporal Asset Catalog (STAC), which makes AgriSen-COG a findable, accessible, interoperable, and reusable (FAIR) dataset.

Trees outside forests (TOFs) are important landscape features that provide numerous functions (ecosystem services) that are not valued due to a lack of knowledge about these resources. The aim of this study was to evaluate the changes induced by the anthropogenic land use change in relation to their effects on TOFs. The dynamics of TOFs were examined through detailed photointerpretation mapping and characterization by land use/land cover and other environmental variables. The landscape function of TOFs and relative dynamics were analyzed, revealing landscape simplification due to the loss of TOFs, both in number and area, and a relative loss of connectivity. In 2000, TOFs accounted for 2.6% more forest area than mapped in the regional forest map; in fifteen years, about 30% of the total area has been lost, one-third of which has been converted to forest and the remainder permanently lost. The causes of the loss of TOFs are partly due to the abandonment of agricultural land, but also to the actions of farmers who remove these elements for various reasons. In protected areas (Natura 2000 network), the loss is less due to the different characteristics of land use/cover and land management.

Constant monitoring of airports and aviation bases has become one of the priorities in today’s strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change.

Understanding how and what land cover changes and transitions have occurred in a territory is crucial to planning and managing high-demand surfaces. At the landscape level, the challenge is determining the allocation and management of various land cover options. Therefore, for natural resources planning and management, a study characterizing and analysing the territory of interest should be included. This work aimed to analyse the changes and land cover patterns in the city of Pereira, Colombia, within the framework of the Colombian Coffee Cultural Landscape. The evaluated period was between 1997 and 2014, and a Geographic Information System, ENVI 4.8 programme and QGIS programme were used for multitemporal analysis. To describe the land cover transitions, two temporal moments were analysed with Landsat satellite images: one moment was for the year 1997, which was taken in August (Landsat 5), and the other moment was for the year 2014, which was taken in July (Landsat 8). At level 1 of CORINE (Coordination of information on the environment), the areas of land cover corresponding to agricultural areas, forests and semi-natural areas decreased most in the analysis period, while artificial surfaces increased. At level 3, the cover with the greatest decrease in territory was coffee crops, which showed a negative annual loss rate of -3.97%, followed by permanent crops (-2.67%). The continuous and discontinuous urban fabric showed the greatest growth with a positive annual rate of 4.14%. In conclusion, the land cover that lost the most territory was coffee crop, mainly due to political-economic factors, such as the dissolution of the International Coffee Agreement and the National Federation of Coffee Growers that discouraged coffee cultivation and permanent crops. Likewise, sociocultural factors, such as smallholder farmers have guided the changes in land cover and have stimulated productive styles to adapt and remain, increasing heterogeneous agricultural areas.

Waterleakage detection and localization in distribution networks pipelines is a challenge for utility companies. For this purpose, thermal Infrared Radiation (IR) techniques have been widely applied in the literature. However, the classical analysis of IR images has not been robust in detecting and locating leakage, due to presence of thermal anomalies such as shadows. In this study, to improve the detection and location accuracy, a digital image processing tool based on multitemporal IR is proposed. In multitemporal IR analysis, the variation of soil’s temperature due to field temperature can be obtained; and hence; estimating variations due to water leakage would be more accurate. An experimental setup was built to evaluate the proposed multitemporal IR water leak detection method. In order to consider the temporal temperature variation due to water leakage and mitigate the field temperature effects, a luminance transformation of the IRimages was introduced. To determine the temporal temperature variation of the soil’s surface due to the leakage, several metrics have been considered such as the difference, the ratio, the log-ratio and the coefficient variation (CV) images. Based on the experimental results, the log-ratio and the CVimages were the most robust metrics. Then, based on log-ratio or the CV image, a temporal variation image (TVI) that traduces the temporal IR luminance variation was introduced. The analysis of the TVI image showed that the CV image is less noisy than the log-ratio image, and can more accurately locate the leakage. Finally, based on TVI histogram, a threshold was defined to classify the TVI image into leakage/non-leakage areas. Results showed that the proposed method is capable of accurately detecting and locating water leakage, which is an improvement to the false detections of spatial thermal IR analysis.

The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results.

The use of deep learning (DL) methods for change detection (CD) is currently dominated by supervised models that require a large number of labeled samples. However, these samples are difficult to acquire in the multitemporal case. A possible alternative is leveraging methods that exploit transfer learning for CD by reusing DL models pretrained for other tasks. However, the performance of the transfer-learning-based models decreases as much as the target images differ from the ones used for training the model. To overcome this limit, we propose an unsupervised CD method that exploits multiresolution deep feature maps derived by a convolutional autoencoder (CAE). It automatically learns spatial features from the input during the training phase without requiring any labeled data. The proposed method processes the bitemporal images to obtain and compare multiresolution bitemporal feature maps. These feature maps are then analyzed by a feature-selection technique to select the most discriminant ones. Furthermore, an aggregated multiresolution difference image is computed and used for a detail-preserving multiscale CD. In the context of this CD approach, we propose two alternative strategies to retrieve multiscale reliability maps. We tested the proposed method on bitemporal multispectral images acquired by Landsat-5 and Landsat-8 representing burned areas and Sentinel-2 images representing deforested areas. Results confirm the effectiveness of the proposed CD technique. [ABSTRACT FROM AUTHOR]

Copyright of Revista Avances: Investigación en Ingeniería is the property of Universidad Libre, Facultad de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Differential interferometric synthetic aperture radar (DInSAR) has been widely used as one of the most important technologies for determining coseismic deformation. However, DInSAR processing is often perturbed by errors including atmospheric effects and those in topographic models, SAR satellite orbits, and phase unwrapping operation. These nuisance components can degrade the accuracy of the measurements and, therefore, distort the inversion of fault slips especially for moderate earthquakes. We propose in this letter a multitemporal InSAR (MTInSAR) method aiming to accurately determine coseismic deformation. By jointly analyzing a set of preseismic SAR images and one postseismic image, the method allows effective separation of coseismic deformation from topographic and satellite orbital errors deformation based on the distinct spatio-temporal characteristics of the signals. Since the solution is achieved directly from wrapped differential phases, the retrieved deformation is also immune to phase unwrapping errors. The October 6, 2008 Mw 6.3 Dangxiong, China earthquake is studied with the proposed method as an example. The slip inverted, respectively, from the MTInSAR and DInSAR coseismic deformation measurements shows up to 34-cm differences, indicating that the topographic error and inaccurate removal of orbital errors can bias the fault slip inversion. [ABSTRACT FROM AUTHOR]

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Mapping the changes of land use and cover through the classification of satellite images is one of the essential sources to investigate and monitor the Earth´s surface. When performed in a multitemporal perspective, this approach requires specific procedures to match the images used. Considering that parkland/grassland savanna patches could increase over time due to different uses, this research aims to present a suitable method for processing Landsat-like images to investigate land cover dynamics. The study area covers seven municipalities in the Brazilian state of Piauí, Cerrado biome, which has been substantially affected by deforestation due to extensive agricultural projects in the past decades. The semi-automatic satellite imaging georegistration and the object-oriented classification of Landsat 5 and 8 satellites over the last 30 years in decadal periods are among the methodological procedures used. Findings demonstrate the semi-automatic image registration process as an effective method for the geometric correction of Landsat scenes, and the object-based classification procedures are appropriate for multitemporal studies allowing comparative metrics of landcover class changes by period. Regarding the remaining natural landcover within the study area, the results showed a substantial decrease of woodland savanna patches from 73% in 1986 to 43% in 2016, while agricultural fields increased from 4% to 25% in 30 years.

The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results. [ABSTRACT FROM AUTHOR]

Anthropic activities have progressively left their mark in the environment where man benefits from the ecosystem goods and services, which has transcended socio-environmental conflicts throughout history. Proof of this is the drastic changes suffered by the Bogotá River due to the uncontrolled discharges from the continuous growth of populations and activities surrounding the body of water. In the present study, the variability of the environmental parameters in the upper basin of the Bogotá River between the years 2007 to 2019 is exposed after the issuance of the Agreement 43 of 2006 of the CAR, for which a multi-temporal analysis was carried out and statistical data. The results show that throughout the twelve (12) years of monitoring there is no contamination due to nitrates, nitrites, or sulfates in the upper basin of the Bogotá River, similarly, the monitored pH remained within the objectives of quality except for 3 years in which the maximum value allowed in the upper part of the basin was exceeded. However, the increasing deterioration of the water quality in the Bogotá River is mainly attributed to the total suspended solids that shows a permanent non-compliance in the upper and lower upper basin, added to the non-compliance in the lower upper basin according to the quality objectives for the dissolved oxygen, BOD and COD parameters. Consequently, the results show the scarce impact derived from the actions developed by the local and environmental authorities to ensure the quality objectives in the upper basin of the Bogotá River, and the effect of weak water governance in the basin. [ABSTRACT FROM AUTHOR]

The integration of field surveys, bibliographic research and multitemporal analysis of historical maps, aerial photographs and satellite images in a GIS environment, allowed the current and past geomorphological features of the old city of Alessandria and its surrounding areas, NW Italy, to be identified and mapped. Their analysis provided an overview of the geomorphological evolution of the city that is strictly related to the historical vicissitudes occurred since the Middle Ages. Nowadays, the most representative landforms and deposits characterizing the urban landscape result from human interventions and are associated with ancient military facilities and infrastructures, a historical man-made channel network no longer recognizable, the Tanaro riverbed channelization, and the urban sprawl occurred from the second half of the nineteenth century onwards. This study represents a useful tool for urban planning and management and for raising the citizens' awareness of the urban-landscape geomorphological features and evolution, and therefore the geo-hydrological risk. [ABSTRACT FROM AUTHOR]

High urbanization and a consequent change in land cover can lead to a deterioration in air quality and generate impacts on public health. The objective of this paper is to provide a multitemporal analysis of the influence of particulate matter ≤ 10 μm (PM10) on human mortality from the land cover variation in a Latin American megacity. Six monitoring stations (monitoring daily PM10 concentration, increases in daily mortality (IDM), and land cover) were established throughout the megacity. The results suggest that for every 10% increase in vegetation cover, the daily PM10 concentration and IDM decreases by 7.5 μg/m3 and 0.34%, respectively. Moreover, it is evident that the monitoring station with the lowest vegetation cover (8.96 times) shows an increase of 1.56 times and 4.8 times in the daily PM10 concentration and IDM, respectively, compared with the monitoring station with the highest vegetation cover (46.7%). It is also suggested that for each increase of 100 inhabitants/hectare in population density, the daily PM10 concentration and IDM increases by 9.99 µg/m3 and 0.45%, respectively. Finally, the population densification of the megacity possibly implies a loss of vegetation cover and contributes to the increase in PM10 and IDM.

Over the past decades, the Southeast Atlantic Forest in Paraíba do Sul River Valley has suffered intense deforestation and human disturbances. Due to the Atlantic Forest biodiversity and the economic relevance of such a region in Brazil, spatial-temporal analyses are of crucial importance to protect the forest, as well as to support economic decision-making of public and private agents. In this context, the use of change detection techniques applied to remote sensing imagery arises as a powerful tool to track and map the Earth's surface transformations. Therefore, this work investigates the effectiveness and practical feasibility of distinct unsupervised change detection approaches when they are applied to reveal the spatial-temporal dynamics in Paraíba do Sul River Valley across the last four decades. Different change detection approaches such as Change Vector Analysis (CVA), a K-Means and Principal Component Analysis (PCA-KM) framework, and a Alternating Sequential Filtering (ASF) based process were taken and properly tuned to cope with Landsat image series. The analysis of the results revealed a permanent land cover change rate over the last decades. Moreover, these changes do not necessary occur in the same locations, as it was confirmed the existence of successive modifications in original coverage of the study area. Another observed aspect is that the simplest technique for detecting changes, CVA, turned out to be the best approach to map the changes in the examined region. [ABSTRACT FROM AUTHOR]

During the last years, in the sub-basin of the Chicú river, the agricultural and cattle exploitation has intensified and has depleted the water resources, thereby causing a deficit that has limited the continuity of such agricultural activity. Therefore, it is necessary to quantify land use and land cover changes contribute to the hydrological response to achieve sustainable management of the water resources in the sub-basin. In this sense, an integrated approach was used, which includes the SWAT (Soil and Water Assessment Tool) hydrological model and the different LUCC (Land use and cover change) maps obtained through tele-detection by using Landsat images to decide the hydrological response in the basin with the changes in land cover and uses in 1997, 2001, 2006, 2011, and 2016. As a result of the SWAT modeling, it can be noticed that the surface run-off varies according to the type of cover and extension, increasing or decreasing the water flow according to the characteristics of each cover, as in the case of bare lands (AGRL). While in 2006 it represented an area of 7.32% with a run-off of 39.25 mm, in 2001 its area decreased to 5.66% with a run-off of 44.9 mm. Moreover, in 1997 a flow of 4.45 m3/s can be observed, whereas in 2001 it decreases by 15% in the main current, which can be justified by a decrease of 8.8% in dense (FRSD) and fragmented (FRDT) forests. For 2006 and 2011 scenarios, the flow increases 13% and 50%, respectively, which corresponds to an increase of 36% and 48% concerning 2001 in clean grasses (PAST); despite the increase in clean grasses (PAST), the surface run-off was maintained almost constant above 9 mm, and it is thus considered a more stable vegetation cover.

Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios.

This article focuses on problematics of creating digital orthophotos using historical aerial photos. The project is part of a grant project that deals with change in rural architecture over the last two centuries. The orthophotos are one of the outputs created in the grant project and illustratively represent development of the selected villages over the last 80 years. In addition to orthophotos other outputs such as analytical map data (development of selected villages since the years around 1830 till present) are created. The article describes the current state of orthophoto creation and also difficulties that might be encountered at processing of historical photos. The specificity of this project is in small areas (primarily built-up areas of selected villages) that are processed. Such an area is usually captured only in one or two photos, which significantly affects the way of the photogrammetric processing. The project involves three preselected villages. Orthophotos are created for each of them in three terms (approximately 1938 + 1970 + 1985) and this chronological order is amended with purchased data for the year 1953 and the present. The time series of orthophotos, together with other inputs, bring better understanding to the development of rural settlements in the context of socio-historical events – e.g. Second World War, socialist expropriation, collectivization, agricultural large-scale production, changes in ownership after 1989 etc.

A study was performed regarding high Andean wetland degradation in a paramo area between the municipalities of Villamaría and Manizales, Colombia, by way of multi-temporal analysis, using satellite images from optical sensors, such as LANDSAT and RAPIDEYE, as well as images from RADAR sensors (ALOS PALSAR, SENTINEL 1), and analysis of anthropic and natural factors. As a result, the wetlands have begun a significant, linear decline with 67.9% water mirror loss in a nine-year period. There is also a direct relationship between wetland loss, and decreases in precipitation, and anthropization processes. It was determined, from the anthropic factor analysis, that that livestock and agricultural land use are those which cause the greatest negative effect on wetland decline in the studied area.

Copyright of Dilemas Contemporáneos: Educación, Política y Valores is the property of Dilemas Contemporaneos: Educacion, Politica y Valores and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human occupation and its productive activities give rise to changes in the Earth's natural covers that affect ecosystem services and, specifically, water regulation. According to the multi-temporal integrated analysis of the coverage and hydrological, it was possible to demonstrate that the upper basin of the Chinchiná River, which is of strategic importance for the city of Manizales, due to its natural conditions of soils and natural coverage, does not have a good capacity for water regulation that is it deteriorates with the expansion of grass covers and herbaceous vegetation. The importance of the study lies in the fact that the knowledge of the relationship between the water regulation capacity versus vegetation cover allows projecting the adequate use of the land in order not to further affect the regulation capacity of the surface runoff that serves as the basis of the biodiversity and supply of the city of Manizales. [ABSTRACT FROM AUTHOR]

The quality of characterization in digital elevation models (DEM) is fundamental in geomorphology, especially in multitemporal studies in which we want to recognize changes in relief by calculating eroded or sedimented volume. Generally, this is achieved from DEM whose accuracy depends on the data and interpolation methods used to construct the model. Different methods could introduce a significant not real change in multitemporal comparison. In this paper we will check how the interpolation method (inverse distance weighted, kriging, natural neighbor and triangulated irregular network), and other parameters, influence the construction of a DEM in different terrains. The results show that, in particular, the interpolation method can have an influence. Moreover, they reveal that the use of one method or another, in the generation of files for the calculation of volume changes in the terrain, can give rise to significant differences in zones of relatively little extension in geomorphological terms. [ABSTRACT FROM AUTHOR]