Multi-temporal Analysis Research Articles

Multi-temporal analysis of land use change using GIS and satellite imagery: Implications for sustainable urban planning

Urbanization is a significant driver of land use change, particularly in rapidly growing metropolitan areas. This research investigates Greenfield City land use change in the 20-year period (2000-2020) using GIS and satellite data. The mapping shows where the greatest land-use changes occurred, ranging from increased residential and commercial developments to the loss of agricultural fields and the omission of green space. The work applies multi-temporal analyses of Landsat satellite images taken in 2000, 2010 and 2020 to estimate land cover change and its effects on urban planning and sustainability. They indicate that theres a clear rise in housing and business developments, but also a steep decline in farming and greenspace. These transformations affect the environment, with habitat loss, biodiversity destruction and encroachment on natural resources. The paper wraps up by focusing on the issues of sustainability in urban planning and how better land use planning is required to reduce the negative environmental effects of urban sprawl.

Visualising intercity mobility patterns during China’s Spring Festival travel rush under the COVID-19 pandemic

ABSTRACT This paper introduces a new tool, the lunar phase visualisation, and conducts a multitemporal analysis of the spatial patterns and their changes in China’s intercity mobility during the Spring Festival travel rush between 2019 and 2023. By associating the volume of intercity movement in China across various years with lunar phases, it illustrates that during both the return home (first 20 days) and back-to-city (last 20 days) periods, intercity mobility demonstrates a trend of initial decline followed by an increase. This trend aligns with the onset and subsequent mitigation of the COVID-19 pandemic, with more pronounced changes observed at the city level versus the provincial level. It also underscores the resilience of mobility during traditional holidays amid a public health crisis.

Multitemporal analysis and mapping of land use changes caused by sugar cane expansion in the State of São Paulo, Southeast of Brazil

Multitemporal analysis and mapping of land use changes caused by sugar cane expansion in the State of São Paulo, Southeast of Brazil

Multi-temporal analysis of forest canopy cover in Ngel Nyaki Forest Reserve using the Sentinel-1 and Sentinel-2 data

ABSTRACT This research used the Sentinel-1 and Sentinel-2 data to map forest canopy cover. The main aim of this study is to assess the capability and value of SAR and Optical image data for mapping, estimating, and monitoring forest canopy cover and its parameters in the Ngel Nyaki Forest Reserve in the Mambilla Plateau, Taraba State, Nigeria. The spatial distribution pattern of forest vegetation cover between 2015 and 2021 was analyzed using a random forest model with Sentinel-1 and maximum likelihood for Sentinel-2 data and the relationship between the biophysical parameter Leaf-Area-Index (LAI) and the spectral indices. The results show changes in canopy cover derived from Sentinel-1 and Sentinel-2 with varying canopy cover frequencies, indicating a decrease from 34% to 27% and 32% to 25% from 2015 to 2021, respectively. The basal area per acre (BA/ac) ranges from 30 to 170 square feet/acre, with a mean of 62.82 square feet/acre and a standard deviation of 37.77 square feet/acre. Moreover, the analysis of biophysical parameters shows that the LAI for the entire forest area was 3.88 in 2015, indicating a relatively dense canopy with substantial leaf cover, while the LAI value of 3.82 in 2021 suggests a slight decrease in leaf area density.

Designing a Multitemporal Analysis of Land Use Changes and Vegetation Indices to Assess the Impacts of Severe Forest Fires Before Applying Control Measures

Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the multitemporal conditions of a sixth-generation forest fire through the use and implementation of tools such as remote sensing, photointerpretation with geographic information systems (GISs), thematic information on land use, and the use of spatial indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Burned Ratio (NBR), and its difference (dNBR) with satellite images from Sentinel-2. To improve our understanding of the dynamics and changes that occurred due to the devastating forest fire in Los Guájares, Granada, Spain, in September 2022, which affected 5194 hectares and had a perimeter of 150 km, we found that the main land use in the study area was forest, followed by agricultural areas which decreased from 1956 to 2003. We also observed the severity of burning, shown with the dNBR, reflecting moderate–low and moderate–high levels of severity. Health and part of the post-fire recovery process, as indicated by the NDVI, were also observed. This study provides valuable information on the spatial and temporal dimensions of forest fires, which will favor informed decision making and the development of effective prevention strategies.

Modeling the interaction between wildfires and windthrows: A pilot case study for Italian Alps

Wildland fires and windthrows represent relevant disturbances for forest ecosystems worldwide. In this context, especially for Italian catchments, the interaction between windthrows and changes in wildfire behaviour starting from ALS data processing is scarcely investigated. Therefore, this research aims to compute a multi-temporal analysis of the interaction between windthrows and wildfire behaviour in a forested area (Veneto region, northern Italy), recently affected by the renamed Vaia windstorm. The semi-empirical FlamMap model was applied, starting from ALS data processing implemented in R for mapping the spatial distribution of forest attributes and fuels within the catchment. The role of windthrows in altering wildfire behaviour was investigated considering ALS point clouds acquired before and after the occurrence of the storm Vaia. Digital Terrain Models (DTMs), Canopy Height Models (CHMs), topographic data and metrics describing forest structure were extracted from ALS data for both scenarios at 5 m resolution, to compare changes in wildfire behaviour over time. Differences in Rate of Spread (RoS), flame length (FL), midflame windspeed (WS) and arrival time (AT) were assessed, and their correlation with windstorm damages was investigated at the catchment detail. , An increase of RoS, FL, and WS greater than 30 m/min, 3 m and 1.1 m/s were respectively estimated in windthrown areas, as well as a decrease of AT greater than 30 min, attesting the key role of windthrows in altering wildfire behaviour over time. The correlation between windthrows and changes in wildfire attributes was finally modeled by computing regression analysis, with R2 of 0.86, 0.93, 0.62, and 0.91 resulted for RoS, FL, WS and AT. This research represents a pilot case study for better detecting changes in wildfires behaviour due to windthrows occurrence, therefore proposing and carrying out effective planning and management strategies for disturbed forest stands over time.

SAR image integration for multi-temporal analysis of Lake Manchar Wetland dynamics using machine learning

The Manchar Lake wetland complex, Pakistan’s largest freshwater-lake, faces unprecedented ecological challenges amidst climate change and human pressures, necessitating urgent, data-driven conservation strategies. This study employs cutting-edge multi-sensor remote sensing techniques to quantify and analyze the dynamic changes in this critical ecosystem from 2015 to 2023, aiming to provide a comprehensive understanding of wetland dynamics for informed management decisions. Integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral imagery, we assessed changes in wetland extent, vegetation health, and land-use patterns using spectral indices and topographic data. Our methodology achieved classification accuracies exceeding 92% across all study years, revealing significant ecosystem fluctuations. Water body extent exhibited a non-linear trend, expanding from 318.5 km² (5%) in 2015 to 397.0 km² (7%) in 2019, before contracting to 369.9 km² (6%) in 2023. This pattern was corroborated by MNDWI values. Concurrently, vegetation covers dramatically increased from 405.5 km² (7%) in 2019 to 1081.6 km² (18%) in 2023. The Enhanced Vegetation Index (EVI) reflected this trend, decreasing from 0.61 in 2015 to 0.41 in 2019, before recovering to 0.53 in 2023. Land use changes were substantial, with agricultural areas increasing from 118.4 km² (2%) in 2015 to 498.0 km² (8%) in 2023. SAR data consistently supported these observations. Topographic analysis, including the Topographic Wetness Index (TWI), provided crucial insights into wetland distribution and resilience. This comprehensive analysis highlights the complex interplay between natural processes and human influences shaping the Manchar-Lake ecosystem, underscoring the urgent need for adaptive management strategies in the face of rapid environmental change.

Exploiting Legal Reserve Compensation as a Mechanism for Unlawful Deforestation in the Brazilian Cerrado Biome, 2012–2022

This research investigates the illegal Vegetation Suppression Authorization (VSA) for Legal Reserve Compensation (LRC) in Tocantins, Brazil, between 2012 and 2014. The VSA is a license issued by state environmental agencies that authorizes the removal or clearing of native vegetation. This infraction resulted from the state environmental agency’s mistaken interpretation of Resolution 07/2005 of the State Environmental Council and the Native Vegetation Protection Law, which operates nationally and takes precedence over state resolutions and laws. The study area focuses on eight municipalities contained in the Formoso, Pium, and Javaés river basins. The methodology examines irregular VSA-LRC cases through geospatial data analysis, multitemporal analysis using remote sensing data, and economic assessments in both the origin and destination areas of the compensatory reserve. The results showed that among the 217 VSA-LRC applications during the period, 93 had corresponding geospatial data, indicating that 57.1% of the geospatial data were missing. This discrepancy between the authorized area and the available geospatial information restricts the oversight capability of the regulatory agency. A decade-long deforestation analysis of VSA-LRCs using remote sensing data considered three phases: 2012–2014 (application and issuance of VSA-LRCs), 2015–2018 (operational issuance), and 2019–2022 (post-expiration of VSA-LRCs). The results revealed a total VSA-LRC area of 343.291 km2 (34,329.10 ha), with 229.1169 km2 (22,911.69 ha) of deforestation. Notably, 54.877 km2 (5487.70 ha) of this deforestation occurred in 2019–2022, after the VSA-LRC had expired. The analysis of the LRC origin and destination areas demonstrates a unidirectional strategy, from economically more valuable areas to less valuable ones, according to land market dynamics. Therefore, this investigation concludes the need for greater administrative transparency, the development of a reliable geospatial data system for monitoring, and policy changes and actions to enhance environmental protection.

Sedimentation analysis at the mouth of the Dique Channel to the Cartagena Bay through hydrographic surveys

This article demonstrates the problem currently facing Cartagena Bay due to excessive sedimentation at the mouth of the Dique Channel, based on the analysis carried out by the Hydrographers of the National Hydrographic Service taking advantage of the management of the different seabed research technologies available to the Caribbean Oceanographic and Hydrographic Research Center (CIOH). Between December 2022 and January 2023, a hydrographic survey was carried out at the mouth of the Dique Channel, carrying out a multitemporal analysis with the hydrographic data from 2012 and 2020 stored in the hydrographic database, allowing a clear identification of the volume of the sedimentation rate in this important area of Cartagena Bay. The article shows the need for high monitoring of the study area by the National Maritime Authority, also because the port of Cartagena is one of the most important in Colombia.

UAV REMOTE SENSING APPLICATIONS IN BEACH-CLIFF SYSTEM MONITORING OF MORPHODYNAMIC PROCESSES

This study aims to evaluate the morphodynamic changes in a beach-cliff system in northeastern Brazil using high resolution data collected by UAV-SfM techniques to understand the geomorphic changes of the coastal landscape. The study used the Geomorphic Change Detection (GCD) method applied to short-term monitoring. This method uses Digital Elevation Models (DEMs) to determine the morphological changes in terms of both erosion and deposition through DEMs of Difference (DoDs). The difference gridded models are acquired by drone-based remote sensing, which can accurately and efficiently provide ultra-high resolution imagery and digital surface model data to measure volumetric changes. The geomorphic changes are observed on high resolution maps. The multi-temporal analysis revealed significant volumes of erosion and deposition throughout the beach-cliff system over the study period. In the first interval (May/2021 - November/2021), the cliff recorded -8,715 m³ of erosion and 2,816 m³ of deposition, with the beach sector showing erosion in 86% of its area. In the second period (November/2021 - March/2022), more severe erosion rates were observed on the cliff, reaching -10,853 m³ with 51% of its area eroded, while the beach showed the opposite behavior to that previously analyzed, with 73% of the beach experiencing an accumulation of sand and a positive balance of 5,960 m³. The results of this study can be fruitful in identifying the different of geomorphology hazards, coastal impacts of cliff urbanization and in the use of remotely piloted aircraft in coastal monitoring. Furthermore, the results indicate that the management of the beach-cliff system must be integrated and never dissociated, as is generally done by coastal managers. Keywords: Geomorphometry; DEMs; Drone-Based Remote Sensing; Mass Movement; Elevation and Volumetric.

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam.

Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation's population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms, have elevated landslide susceptibility in many parts of the country. This research examines the spatio-temporal variations in landslide susceptibility across central Vietnam over several years, using multi-temporal landslide inventories from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020). Additionally, the research explores the impact of individual landslide causative factors on the probabilistic occurrences of landslides. The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. These changes may be early indicators of increasing landslide susceptibility in response to changing hydro-climatic conditions. The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. The landslide susceptibility models achieved high Area Under the Receiver Operating Characteristic Curve (AUC) (>95%), accuracy (>89%), and sensitivity (>90%) scores, signifying the robustness of the models. Additionally, the uncertainty of the models was quantified and spatially mapped. This multi-temporal analysis of landslide susceptibility is crucial for understanding the regional susceptibility trends and identifying areas with increasing, decreasing, and consistently high susceptibility to landslides. These insights are invaluable for prioritizing mitigation and risk reduction strategies in landslide-prone regions and guiding appropriate land use planning.

Assessment of mangrove health based on pressure–state–response framework in Guangxi Beibu Gulf, China

Mangroves, primarily found along tropical and subtropical coastlines, are crucial ecosystems that offer significant ecological services, including coastal protection, carbon sequestration, and biodiversity support. However, these ecosystems are under severe threat from human activities such as aquaculture, agriculture, and urban development, leading to deforestation and degradation. This study aims to develop a comprehensive framework for assessing the health of mangrove ecosystems using the Pressure-State-Response (PSR) model with annual temporal resolution from 2019 to 2023. It consists of three dimensions: Pressure (human activities and natural factors affecting the ecosystem), State (current condition of the ecosystem), and Response (efforts to mitigate adverse effects and restore health). This study incorporates innovative physical and structural parameters alongside traditional indicators to improve the accuracy of the assessment. The method offers advantages such as ease of measurement, low cost, early warning capabilities for ecosystem damage, and high sensitivity to ecological changes. It provides valuable insights for decision-makers and enhances the ability to detect trends in mangrove health over time through a large-scale, multi-temporal analysis. Using remote sensing techniques and the Analytic Hierarchy Process (AHP), we identified 12 key indicators. The AHP method was used to determine the weight of each indicator, achieving a consistency ratio (CR) of less than 0.1, which ensures the reliability of the assessment. The evaluation included calculating and analysing the Comprehensive Health Index (CHI), Pressure (PHI), State (SHI), and Response (RHI) health indices. Results indicated that the mangrove ecosystems in the Beibu Gulf region were “healthy” state by 2019 through 2022. However, in 2023, the health status declined to “relatively healthy,” indicating a downward trend. In conclusion, this study presents a robust framework for assessing mangrove ecosystem health using the PSR model. The findings underscore the urgent need for conservation efforts to reverse the declining health trend and ensure the long-term sustainability of mangrove ecosystems in the Beibu Gulf.

LANDSCAPE CHANGES BASED ON HYDROLOGICAL CHARACTERISTICS IN THE WAE RIUAPA WATERSHED WEST SERAM REGENCY, MALUKU, INDONESIA

Changes in the Wae Riuapa River Basin landscape in West Seram Regency, Maluku, reflect complex ecological and social dynamics. The Wae Riuapa watershed is an integral part of the hydrological cycle in the region, influencing water availability, water quality, and geomorphological processes. This research aims to model changes in the landscape of the Wae Riuappa watershed regarding hydrological characteristics. The research method uses ArcGIS 10.3 for spatial analysis of 2010 and 2020 Landsat images and hydrooffice 12.0 software, BFLOW 3.0 module, a hydrological model for graphical separation of daily discharge. The results of modeling landscape changes in the Wae Riuappa watershed on hydrological characteristics obtained a regression model that depicts landscape changes as having a significant simultaneous and partial influence on discharge conditions in the Wae Riuappa watershed during the research period. The spatial pattern of area distribution for the nine land cover types in the study period was relatively similar. Widespread forest land cover still dominates the landscape of the Wae Riuapa watershed. However, the pattern of widespread distribution of changes is starting to decrease in primary and secondary forest land cover. Meanwhile, the distribution pattern of changes in agricultural land and mixed gardens and settlements has increased slowly and significantly. The results of a multi-temporal analysis of the hydrological characteristics of the Wae Riuapa watershed show that the daily discharge trend experiences multi-temporal fluctuations.

Multitemporal analysis of cliffs evolution along an Atlantic African coast (Safi Region, Morocco)

Unstable coastal cliffs represent a major threat to coastal infrastructure, housing, and economic activities, which depend on coastal stability. Understanding recession rates and the factors influencing them is therefore essential for enhancing risk prediction and management. Here, we investigated the evolution of coastal cliffs in a crucial sector of the Atlantic African coast in Morocco, stretching from Cap Beddouza in the north to Jorf Lihoudi in the south, covering an area of around 48 km and including the large city of Safi. The instability of these cliffs constitutes the main coastal geological hazard of the area, which has its geomorphological expression in different types of landslides punctuating the coastal cliff. We employed multidecadal aerial imagery along with GIS technique to calculate the rate of change of the cliff over 66 years. Our results indicate that most of the Safi Region coastal zone has undergone recession with rates of change generally variable from 0.04 to 0.08 m/yr ± 0.01 m. The central part of the study area, conversely, experienced the highest retreat rate, exceeding 0.10 m/yr. The spatial variability of recession rates is explained through geological and morphological factors such as the nature of low-strength clay rock formations, favoring large-scale gravity movements. These data are crucial to better define the current level of coastal hazard in this densely populated portion of the Moroccan coast, given that cliff recession have considerable effects on the economic, social and environmental risk of the area. Moreover, this study represents one of the first applications of the Digital Shoreline Analysis System (DSAS) method to an African coastline segment, specifically from Cape Beddouza to the Lihoudi cliffs. More generally, it is among the few studies focused on cliff recession rates in Africa. While a similar application has been conducted for the cliff base in the northern part of the same study area (Raja et al., 2023), that study primarily addresses coastal cliff failure hazards along the Safi coastline in Morocco. In contrast, the present study is focused on determining long-term cliff recession rates and understanding the distribution of these rates and modes of retreat in relation to the physical processes affecting the study area.

Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico

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.

Analysis of spatio-temporal development of mining landforms using aerial photographs: Case study from the Ostrava–Karviná mining district

Abstract The anthropogenic relief transformations of the mining landscape are characterised by high dynamics of changes over time that can be effectively mapped on a large scale using aerial images. The Karviná part of the Ostrava-Karviná mining district, which stands for significant hard coal mining area in the Czech Republic, has been selected to analyse the spatio-temporal development of anthropogenic landforms. Anthropogenic landforms were visually identified from the aerial images from 1947, 1966, 1971, 1985, 1994, 2009, and 2018. Specific anthropogenic landforms were analysed along with their spatio-temporal changes based on obtained vector data. The total extent and number of anthropogenic landforms increased the most during the periods 1947–1966 and 1971–1985. The same anthropogenic landforms occurred on circa 24 hectares in the entire period 1947–2018. The most anthropogenic landforms remained preserved in the last observed period 2009–2018. The multi-temporal analysis of aerial photographs and overlay operations in GIS enabled to map the age of specific landforms and to identify changed or unchanged anthropogenic landforms. This method is suitable for the study of landform dynamics at mining sites and is particularly relevant for the planning challenges in post-mining reclamation.

Ice degradation and Boulder size frequency distribution analysis of the fresh Martian crater S1094b

S1094b is the largest (155 m-size) and southernmost known ice-exposing fresh crater discovered so far on Mars, revealing a relatively pure and unstable subsurface ice deposit located at the northern Martian mid-latitudes. In this work, we analyze HiRISE images taken on 27 February 2022 and on 5 December 2022 to perform a multi-temporal analysis of its ice-rich ejecta, combining this analysis with geologic mapping, the boulder size frequency distribution (SFD) and thermal modeling. The objective is to provide a multidisciplinary characterization of both the impact and subsequent exposed ice sublimation processes. The boulder SFD of both February and December cases show a power-law best fit with indices −4.68 ± 0.15 and − 3.47 ± 0.10, respectively. In the same timeframe, the density of boulders per km2 ≥ 1.5 m changes from 3908, to 596. This flattening is mainly due to the sublimation and consequent loss of the smaller-size icy boulders. This is confirmed by the ice volume computation performed on the area, which changed from ∼20,274 ± 3997 m3 to ∼7951 ± 1117 m3, i.e. a decrease of ∼60% in 274 Sols. The thermal models showed that the ice in this region is always unstable, leading to a total of 6504.71 sublimation hours from which we estimated a sublimation rate of ∼0.15 ± 0.04 mm/h (i.e. ∼3.60 ± 0.96 mm/Sol). The presence of this amount of accessible ice at such low latitudes could be a valuable resource for potential future human missions.

Effects of climate change and land use on the hydrologic regime using the Hydro-BID tool: A case study of the Andean mountain basin in Colombia

ABSTRACT Changes on the land surface generate changes in land cover, which directly affect the availability of water in watersheds. This article evaluates the case study regarding the effects on the hydrological regime of the Andean mountain basin on the Coello river basin in Colombia due to changes in land use/land cover during the 2000–2019 period by the use of the Hydro-BID tool. The physical analysis of the land surface included the processing of Landsat 7 ETM and Landsat 8 OLI satellite images for the years 2001, 2003, 2015, and 2019. Seven types of coverage were determined based on these data using the Mixed Gaussian Method. The changes between each year were evaluated, after which the land use/land cover change for the year 2050 was predicted using a Markov chain. The multi-temporal analysis showed a decrease in forested areas during the studied period, while low vegetation significantly increased within the watershed. This trend was shown to continue in the future scenario for the year 2050, with an increase in flow on the watershed of 59.6%. Additionally, the climate change scenarios were modeled with the changes in land use. The combined effects established a progressive decrease in the modal flow.

Assessing interpreter’s disagreements in land cover reference data collection from historical Landsat time series in Amazon

ABSTRACT Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field data can be very rare for multi-temporal analysis, many studies use samples visually interpreted from Remote Sensing images. This subjective process is prone to errors in interpretation, leading to uncertainties not fully quantified in the reference sample collection of historical time series. We evaluated the agreement rate among independent interpreters in the identification of land cover classes assigned to pixels for yearly Landsat observations from 1984 to 2020, in the Lower Tapajós Basin region (within the Brazilian Amazon). Considering 10 classes, we found that sets of three interpreters completely disagreed on the label of 14.8% of the pixels for each year, and completely agreed in less than half (39.6%). These values change to 2.7% and 68.8% when we consider only five classes in the analysis. We observed time-dependent variations in agreement for specific land cover classes: whereas forest areas tend to present an increase in the disagreement rate over time, land cover types that happen only in previously deforested areas tend to present a higher proportion of pixels with better agreement rates. Disagreements tend to be higher in pixels identified as borders or near borders between targets. However, there is a non-negligible amount of disagreements in pixels identified as not borders caused by limitations derived from the unit of analysis, image composition techniques, and intrinsic similarities among classes. We highlight problems with the identification of classes that happen in complex spatiotemporal patterns (Secondary Vegetation, Small-scale Agriculture, and Degraded Forest). These results alert to possible inconsistencies in reference samples collected for the accuracy assessment of land cover time series and the correspondent values of the calculated accuracy indexes.

Analysis of Land Use Changes in the Sado Estuary (Portugal) from the 19th to the 21st Century, Based on Historical Maps, Fieldwork, and Remote Sensing

This study analyses land use changes in the Sado Estuary (West-Central Portugal) based on a multi-temporal analysis of 19th century cartographic data and 21st century remote sensing land use maps, updated by fieldwork. A GIS plot of land use evolution is summarized in a quantitative table. The comparison shows the changes in land use, with increasing occupation by human economic activities, including extensive agriculture and forestry, as well as localized urbanization and industrialization. The main elements of the landscape impacted by anthropogenic uses were (i) hydrography—river dams affected the flow dynamics and sedimentary processes in the estuary; (ii) vegetation—increasing agriculture and forestry reduced the area of native vegetation, which is now mostly occupied by vineyards, pine forests and cork oaks; (iii) wetlands—tidal and alluvial plains are being occupied by rice cultivation, aquaculture, industries, and ports; (iv) coastal dunes—new developments are occupying large areas of Holocene coastal dunes; and (v) natural environment—mining and dredging have affected some habitats and biodiversity. This analysis is intended to help the territorial organization of present and future economic activities, as well as to reduce environmental and social problems, thus promoting the long-term sustainability of this rapidly evolving region.