From Archived Historical Aerial Imagery to Informative Orthophotos: A Framework for Retrieving the Past in Long-Term Socioecological Research

Long-Term Assessment of Spatio-Temporal Landuse/Landcover Changes (LUCCs) of Ošljak Island (Croatia) Using Multi-Temporal Data—Invasion of Aleppo Pine

The karst landscapes of the Mediterranean are regarded as some of the most vulnerable, fragile, and complex systems in the world. They hold a particularly interesting group of small islands with a distinctive, recognizable landscape. The Republic of Croatia (HR), which has one of the most indented coasts in the world, is particularly known for them. In this paper, we analyzed the spatio-temporal changes (STCs) in the landscape of Ošljak Island, the smallest inhabited island in HR. Landuse/landcover change (LUCC) analysis has been conducted from 1944 to 2021. The methodology included the acquisition of multi-temporal data, data harmonization, production of landuse/landcover (LU/LC) maps, selection of optimal environmental indicators (EIs), and simulation modeling. In total, eleven comparable LU/LC models have been produced, with moderate accuracy. STCs have been quantified using the nine EIs. The dominant processes that influenced the changes in the Ošljak landscape have been identified. The results have shown that, in recent decades, Ošljak has undergone a landscape transformation which was manifested through (a) pronounced expansion of Aleppo pine; (b) deagrarianization, which led to secondary succession; and (c) urban sprawl, which led to the transformation of the functional landscape. The most significant of the detected changes is the afforestation of the Aleppo pine. Namely, in a 77-year span, the Aleppo pine has expanded intensively to an area of 11.736 ha, created a simulation model for 2025, and pointed to the possibility of the continued expansion of Aleppo pine. Specific guidelines for the management of this new transformed landscape have been proposed. This research provides a user-friendly methodological framework that can efficiently monitor LUCCs of a smaller area in the case when geospatial data are scarce and satellite imagery of coarser resolution cannot be used. Moreover, it gives an insight into the availability and quality of multi-temporal data for the HR.

Evaluation of Land-Use Changes as a Result of Underground Coal Mining—A Case Study on the Upper Nitra Basin, West Slovakia

Mining activity has one of the most fundamental influences on the landscape (in terms of both aesthetics and use). Its activity and manifestations, even when mining takes place underground, have visual manifestations on the surface. The impact of subsurface mining has a synergistic effect on the elements of the landscape structure. This manifestation is continuous in the context of mining intensity. Using the Earth remote sensing method, we identified several fundamental changes. The most significant of these was the creation of wetlands and the modification of watercourse lines. In the area in which there was no permanent water sources, several water areas with a total area of more than 30 ha were created. We also found that the length of watercourses has halved, the area of grassland has doubled, and urban area has decreased. It was the creation of water areas that supported not only better ecological stability of the landscape, but also the growth of biodiversity. Wetlands can be a dynamic element of future development. Understanding the development of land-cover changes is necessary for the purpose of planning nature and landscape conservation, as well as to identify areas of conflict with economic use.

Multi-Level Sensing Technologies in Landslide Research-Hrvatska Kostajnica Case Study, Croatia

In March 2018, a landslide in Hrvatska Kostajnica completely destroyed multiple households. The damage was extensive, and lives were endangered. The question remains: Can it happen again? To enhance the knowledge and understanding of the soil and rock behaviour before, during, and after this geo-hazard event, multi-level sensing technologies in landslide research were applied. Day after the event field mapping and unmanned aerial vehicle (UAV) data were collected with the inspection of available orthophoto and "geo" data. For the landslide, a new geological column was developed with mineralogical and geochemical analyses. The application of differential interferometric synthetic aperture radar (DInSAR) for detecting ground surface displacement was undertaken in order to determine pre-failure behaviour and to give indications about post-failure deformations. In 2020, electrical resistivity tomography (ERT) in the landslide body was undertaken to determine the depth of the landslide surface, and in 2021 ERT measurements in the vicinity of the landslide area were performed to obtain undisturbed material properties. Moreover, in 2021, detailed light detection and ranging (LIDAR) data were acquired for the area. All these different level data sets are being analyzed in order to develop a reliable landslide model as a first step towards answering the aforementioned question. Based on applied multi-level sensing technologies and acquired data, the landslide model is taking shape. However, further detailed research is still recommended.

An Integrated Remote-Sensing and GIS Approach for Mapping Past Tin Mining Landscapes in Northwest Iberia

Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes.

Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity.

Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

Geographic object-based image analysis (GEOBIA) is a primary remote sensing tool utilized in land-cover mapping and change detection. Land-cover patches are the primary data source for landscape metrics and ecological indicator calculations; however, their application to visual landscape character (VLC) indicators was little investigated to date. To bridge the knowledge gap between GEOBIA and VLC, this paper puts forward the theoretical concept of using viewpoint as a landscape imageability indicator into the practice of a multi-temporal land-cover case study and explains how to interpret the indicator. The study extends the application of GEOBIA to visual landscape indicator calculations. In doing so, eight different remote sensing imageries are the object of GEOBIA, starting from a historical aerial photograph (1957) and CORONA declassified scene (1965) to contemporary (2018) UAV-delivered imagery. The multi-temporal GEOBIA-delivered land-cover patches are utilized to find the minimal isovist set of viewpoints and to calculate three imageability indicators: the number, density, and spacing of viewpoints. The calculated indicator values, viewpoint rank, and spatial arrangements allow us to describe the scale, direction, rate, and reasons for VLC changes over the analyzed 60 years of landscape evolution. We found that the case study nature reserve (“Kózki”, Poland) landscape imageability transformed from visually impressive openness to imageability due to the impression of several landscape rooms enclosed by forest walls. Our results provide proof that the number, rank, and spatial arrangement of viewpoints constitute landscape imageability measured with the proposed indicators. Discussing the method’s technical limitations, we believe that our findings contribute to a better understanding of land-cover change impact on visual landscape structure dynamics and further VLC indicator development.

Analysis of Changes in Forest Structure using Point Clouds from Historical Aerial Photographs

Dynamic changes in land use, many of which are related to land abandonment, are taking place in many regions of the world. As a result, forest vegetation appears, which in part is a consequence of planned afforestation programs and in part has the characteristics of secondary forest succession. Monitoring of forest structure allows the range and dynamics of such changes to be identified. The aim of the study was to assess the usefulness of historical aerial photographs in the determination of forest structure. On the basis of such data, a point cloud was created which represented the forest structure in 1966. Subsequently, using airborne laser scanning data for the same area, corresponding datasets describing the situation in 2012 were created. Comparison of the two tall vegetation models made it possible to perform four analyses related to forest structure changes over a period of 46 years. The analyses were carried out in four areas in southern Poland. The analysis of the results confirmed that historical aerial photographs may be a valuable source in long-term analyses of changes in the range and height structure of areas containing tall vegetation.