Monitoring Land Cover Change and Disturbance of the Mount Wutai World Cultural Landscape Heritage Protected Area, Based on Remote Sensing Time-Series Images from 1987 to 2018

Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains' Northern Slopes, Xinjiang, China

Accurately capturing the changing patterns of ecological quality in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) and researching its significant impacts responds to the requirements of high-quality sustainable urban development. In this study, the spatial and temporal distribution patterns of remote sensing ecological index (RSEI) were obtained by normalization and PCA transformation of four basic indicators based on Landsat images. It then employed geographic detectors to analyze the factors that influence ecological change. The result demonstrates that: (1) In the distribution of land use conversions and degrees of human disturbance, built-up land, principally urban land, and agricultural land, represented by dry land, are rising, while the shrinkage of grassland is the most substantial. The degree of human disturbance is increasing overall for glaciers. (2) The overall ecological environment of the northern slopes of Tianshan is relatively poor. Temporally, the ecological quality changes and fluctuates, with an overall rising trend. Spatially, ecological quality is low in the north and south and high in the center, with high values concentrated in the mountains and agriculture and low values in the Gobi and desert. However, on a large scale, the ecological quality of the Urumqi-Changji-Shihezi metropolitan area has worsened dramatically compared to other regions. (3) Driving factor detection showed that LST and NDVI were the most critical influencing factors, with an upward trend in the influence of WET. Typically, LST has the biggest influence on RSEI when interacting with NDVI. In terms of the broader region, the influence of social factors is smaller, but the role of human interference in the built-up area of the oasis city can be found to be more significant at large scales. The study shows that it is necessary to strengthen ecological conservation efforts in the UANSTM region, focusing on the impact of urban and agricultural land expansion on surface temperature and vegetation.

Elaborate Monitoring of Land-Cover Changes in Cultural Landscapes at Heritage Sites Using Very High-Resolution Remote-Sensing Images

Insufficient data and imperfect methods are the main obstacles to realize Target 11.4 of the Sustainable Development Goals (SDGs). Very high-resolution (VHR) remote sensing provides a useful tool to elaborate monitor land-cover changes in cultural landscapes so as to evaluate the authenticity and integrity of the cultural heritage sites (CHS). In this study, we developed a semi-automatic two-level workflow to efficiently extract delicate land-cover changes from bi-temporal VHR images (with spatial resolution ≤ 1 m), where most current studies can only manually interpret changes at this scale. Based on the monitoring result, we proposed an indicator named interference degree that can quantify the changes in cultural landscapes of the CHS as a complementary indicator to achieve Target 11.4 for SDGs. Three representative types of CHS with different landscapes were studied in 2015 and 2020 based on the VHR Google Earth images, including cave temples, ancient architectural buildings, and ancient sites. The proposed workflow was demonstrated to be effective in extracting delicate changes efficiently with the accuracy around 85%. The interference degree well reflects the preservation status of these CHS and can be periodically observed in a long term as an evaluation indicator. This study shows the potential to produce the first-hand global-monitoring data of CHS to support Target 11.4, thus serving for the sustainable development of the world’s cultural heritage.

Remote Sensing for Biocultural Heritage Preservation in an African Semi-Arid Region: A Case Study of Indigenous Wells in Northern Kenya and Southern Ethiopia

The region of Southern Ethiopia (Borana) and Northern Kenya (Marsabit) is characterised by erratic rainfall, limited surface water, aridity, and frequent droughts. An important adaptive response to these conditions, of uncertain antiquity, has been the hand-excavation of a sequence of deep wells at key locations often along seasonal riverbeds and valley bottoms where subterranean aquifers can be tapped. Sophisticated indigenous water management systems have developed to ensure equitable access to these critical water resources, and these are part of well-defined customary institutional leadership structures that govern the community giving rise to a distinctive form of biocultural heritage. These systems, and the wells themselves, are increasingly under threat, however, from climate change, demographic growth, and socio-economic development. To contribute to an assessment of the scale, distribution and intensity of these threats, this study aimed to evaluate the land-use land-cover (LULC) and precipitation changes in this semi-arid to arid landscape and their association with, and impact on, the preservation of traditional wells. Multitemporal Landsat 5, 7 and 8 satellite imagery covering the period 1990 to 2020, analysed at a temporal resolution of 10 years, was classified using supervised classification via the Random Forest machine learning method to extract the following classes: bare land, grassland, shrub land, open forest, closed forest, croplands, settlement and waterbodies. Change detection was then applied to identify and quantify changes through time and landscape degradation indices were generated using the Shannon Diversity Index fragmentation index within a 15 km buffer of each well cluster. The results indicated that land cover change was mostly driven by increasing anthropogenic changes with resultant reduction in natural land cover classes. Furthermore, increased fragmentation has occurred within most of the selected buffer distances of the well clusters. The main drivers of change that have directly or indirectly impacted land degradation and the preservation of indigenous water management systems were identified through an analysis of land cover changes in the last 30 years, supporting insights from previous focused group discussions with communities in Kenya and Ethiopia. Our approach showed that remote sensing methods can be used for the spatially explicit mapping of landscape structure around the wells, and ultimately towards assessment of the preservation status of the indigenous wells.

Design of Protected Area by Tracking and Excluding the Effects of Climate and Landscape Change: A Case Study Using Neurergus derjugini

This study aimed to use the applications of Ensemble Species Distribution Modelling (eSDM), Geographical Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA) for the design of a protected area (PA) for the critically endangered yellow-spotted mountain newt, Neurergus derjugini, by tracking and excluding the effects of climate and landscape changes in western Iran and northeastern Iraq. Potential recent and future distributions (2050 and 2070) were reconstructed by eSDM using eight algorithms with MRI-CGCM3 and CCSM4 models. The GIS-based MCDA siting procedure was followed inside habitats with high eSDM suitability by eliminating the main roads, cities, high village density, dams, poor vegetation, low stream density, agricultural lands and high ridge density. Then, within the remaining relevant areas, 10 polygons were created as “nominations” for PAs (NPAs). Finally, for 10 different NPAs, the suitability score was ranked based on ratings and weights (analytical hierarchy process) of the number of newt localities, NPA connectivity, NPA shape, NPA habitat suitability in 2070, NPA size, genetic diversity, village density and distance to nearest PAs, cities, and main roads. This research could serve as a modern realistic approach for environmental management to plan conservation areas using a cost-effective and affordable technique.

Spatiotemporal ecological quality assessment of metropolitan cities: a case study of central Iran

The present study used the recently developed Remote Sensing-Based Ecological Index (RSEI) to assess the temporal-spatial variation of ecological quality in the metropolitan city of Isfahan (Iran) as a member of the UNESCO Creative Cities Network. This study was conducted from the Landsat TM/OLI satellite images of 2004, 2009, 2014 and 2019. The RSEI was synthesized by principal component analysis for four indices of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Land Surface Moisture (LSM) and Normalized Differential Build-up, and Bare Soil Index (NDBSI) based on the framework of the Pressure-State-Response (PSR) in the aforementioned years. The ecological quality of the city was assessed by RSEI over a 15-year period. The index has a value range of 0 (completely poor ecological quality) to 1 (completely desirable). In addition, the spatial heterogeneity of RSEIs at different intervals was assessed by the Moran index. The results showed that the RSEI value was always less than 0.4, which indicated the unfavourable ecological quality of the city. This index was 0.34, 0.37, 0.26 and 0.30 in 2004, 2009, 2014 and 2019, respectively. Therefore, the ecological quality of the city did not have a constant trend during the studied period and had several fluctuations, which could be attributed to the natural and anthropogenic changes in the studied period. Additionally, the results of the Moran index showed a steady decline, which indicated a declining homogeneity during this period. Matching the calculated RSEIs with the realities of the region at each time interval suggested that the index could be a useful tool for assessing urban ecological quality.

Remote Sensing for Cultural Heritage Assessment and Monitoring: The Case Study of Alba Iulia

In recent times, satellite-based remote sensing has a growing role in archaeology and inherently in the cultural heritage management process. This paper demonstrates the potential and usefulness of satellite imagery for the documentation, mapping, monitoring, and in-depth analysis of cultural heritage and the archaeological sites located in urban landscapes. The study focuses on the assessment and monitoring of Alba Iulia, which is one of the Romanian cities with the richest historical past. Multitemporal analysis was performed to identify the land use/land cover changes that might contribute to an increased cultural heritage vulnerability to natural disasters. A special emphasis was dedicated to the assessment of the built-up area growth and consequently of the urbanization trend over a large time interval (30 years). Next, the urbanization and urban area expansion impact was further analyzed by concentrating on the urban heat island within Alba Iulia city and Alba Iulia Fortress (located in the center of the city). As temperature change represents a key element of climate change, the temperature trend within the same temporal framework and its impact on cultural heritage were determined. In the end, with regard to the cultural heritage condition assessment, the research was complemented with an assessment of the urban ground and individual building stability, using persistent scatterer interferometry. The results contribute to the detailed depiction of the cultural heritage site in such a manner that the site is monitored over an extensive timeframe, its current state of conservation is accurately determined, and the future trends can be identified. In conclusion, the present study offers reliable results regarding the main factors that might endanger the cultural heritage site as a basis for future preservation measures.

Evaluation of the Temporal and Spatial Changes of Ecological Quality in the Hami Oasis Based on RSEI

Given the restrictions on special geographic locations in development processes, the measurement and analysis of the ecological quality of the Hami Oasis are of great significance for the protection of this fragile oasis. In this study, the ecological quality of the Hami Oasis was monitored by constructing a remote sensing ecological index (RSEI) for arid areas. Using the standard deviation ellipse and moving window method, the ecological status and space–time changes were explored for both their external and internal factors in the Hami Oasis. Finally, a geo-detector was employed to determine the driving factors of the ecological quality of the Hami Oasis. The results revealed that: (1) In the remote sensing ecological index constructed in the Hami Oasis, the main influencing factors were dryness and wetness. The average value of the ecological quality of the oasis was less than 0.5, and the ecological quality level was relatively poor. Among the five grades of ecological quality in the Hami Oasis, the poor grade and the good grade showed the largest changes, decreasing by 200 and increasing by 300, respectively, which were mainly concentrated in the periphery of the oasis. (2) The improved ecological quality of the Hami Oasis was mainly manifested in the expansion of the artificial oasis, while the deteriorated area was manifested as an increase in the built-up area. Moreover, the ecological quality of the Hami Oasis presented a ringlike nesting distribution pattern from the internal built-up area to the artificial oasis periphery. (3) The external expansion direction of the ecological quality of the Hami Oasis featured southeast–northwest expansion, which was consistent with the direction of the rivers and traffic roads. The transformation between different ecological qualities in the oasis and the expansion of the built-up area were the reasons for the fragmentation of the Hami Oasis’ landscape. (4) Compared to a single factor, the dual-factor for the ecological quality of the Hami Oasis had stronger explanatory power. Moreover, changes in land use types caused changes in the ecological quality of the Hami Oasis. During the study period, we found that human activities had a more significant impact than natural factors on the development of the Hami Oasis. (5) The Moran’s I Index increased from 0.835268 in 2000 to 0.923976 in 2018, and the p values in the study area all reached a 0.05 significant level. At the same time, the areas with p values above the 0.01 and 0.001 significant levels have also increased significantly in the past 18 years.

Satellite Remote Sensing for the Analysis of the Micia and Germisara Archaeological Sites

The capabilities of satellite remote sensing technologies and their derived data for the analysis of archaeological sites have been demonstrated in a large variety of studies over the last decades. Likewise, the Earth Observation (EO) data contribute to the disaster management process through the provision of updated information for areas under investigation. In addition, long term studies may be performed for the in–depth analysis of the disaster–prone areas using archive satellite imagery and other cartographic materials. Hence, satellite remote sensing represents an essential tool for the study of hazards in cultural heritage sites and landscapes. Depending on the size of the archaeological sites and considering the fact that some parts of the site might be covered, the main concern regards the suitability of satellite data in terms of spatial and spectral resolution. Using a multi–temporal Sentinel–2 dataset between 2016 and 2019, the present study focuses on the hazard risk identification for the Micia and Germisara archaeological sites in Romania as they are endangered by industrialisation and major infrastructure works and soil erosion, respectively. Furthermore, the study includes a performance assessment of remote sensing vegetation indices for the detection of buried structures. The results clearly indicate that Sentinel–2 imagery proved to be fundamental in meeting the objectives of the study, particularly due to the extensive archaeological knowledge that was available for the cultural heritage sites. The main conclusion to be drawn is that satellite–derived products may be enhanced by integrating valuable archaeological context, especially when the resolution of satellite data is not ideally fitting the peculiarities (e.g., in terms of size, underground structures, type of coverage) of the investigated cultural heritage sites.

Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade

Terrestrial protected areas (PAs) play an essential role in maintaining biodiversity and ecological processes worldwide, and the monitoring of PAs is a useful tool in assessing the effectiveness of PA management. Advanced remote sensing technologies have been increasingly used for mapping and monitoring the dynamics of PAs. We review the advances in remote sensing-based approaches for monitoring terrestrial PAs in the last decade and identify four types of studies in this field: land use & land cover and vegetation community classification, vegetation structure quantification, natural disturbance monitoring, and land use & land cover and vegetation dynamic analysis. We systematically discuss the satellite data and methods used for monitoring PAs for the four research objectives. Moreover, we summarize the approaches used in the different types of studies. The following suggestions are provided for future studies: (1) development of remote sensing frameworks for local PA monitoring worldwide; (2) comprehensive utilization of multisource remote sensing data; (3) improving methods to investigate the details of PA dynamics; (4) discovering the driving forces and providing measures for PA management. Overall, the integration of remote sensing data and advanced processing methods can support PA management and decision-making procedures.

Dynamic Monitoring and Analysis of Ecological Quality of Pingtan Comprehensive Experimental Zone, a New Type of Sea Island City, Based on RSEI

Islands face increasingly prominent environmental problems with rapid urbanization. Hence, timely and objective monitoring and evaluation of island ecology is of great significance. This study took the Pingtan Comprehensive Experimental Zone (PZ) in the east sea of Fujian Province of China as the research object. Based on remote sensing technology, four Landsat images from 2007 to 2017 and the remote sensing ecological index (RSEI) were used to explore the ecological status and space–time change. The results showed that from 2007 to 2011, the average RSEI decreased from 0.519 to 0.506, indicating that the ecological quality generally showed a slight downward trend, mainly due to large-scale development brought by the construction; by 2014, although the ecology of the original area improved, the overall ecology was still declining with 0.502 mean RSEI mainly because of large-scale reclamation projects; by 2017, the average RSEI rebounded to 0.523, which was attributed to the fact that ecological construction and protection were emphasized in the construction of PZ, especially in reclamation areas. In conclusion, the increase of large area bare soil will lead to the decline of regional ecology, but the implementation of scientific ecological planning is conducive to ecological restoration and construction.

Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study

Low-altitude remote sensing platform has been increasingly applied to observing local thermal environments due to its obvious advantage in spatial resolution and apparent flexibility in data acquisition. However, there is a general lack of systematic analysis for land cover (LC) classification, surface urban heat island (SUHI), and their spatial and temporal change patterns. In this study, a workflow is presented to assess the LC’s impact on SUHI, based on the visible and thermal infrared images with high spatial resolution captured by an unmanned airship in the central area of the Sino-Singapore Guangzhou Knowledge City in 2012 and 2015. Then, the accuracy assessment of LC classification and land surface temperature (LST) retrieval are performed. Finally, the commonly-used indexes in the field of satellites are applied to analyzing the spatial and temporal changes in the SUHI pattern on a local scale. The results show that the supervised maximum likelihood algorithm can deliver satisfactory overall accuracy and Kappa coefficient for LC classification; the root mean square error of the retrieved LST can reach 1.87 °C. Moreover, the LST demonstrates greater consistency with land cover type (LCT) and more fluctuation within an LCT on a local scale than on an urban scale. The normalized LST classified by the mean and standard deviation (STD) is suitable for the high-spatial situation; however, the thermal field level and the corresponded STD multiple need to be judiciously selected. This study exhibits an effective pathway to assess SUHI pattern and its changes using high-spatial-resolution images on a local scale. It is also indicated that proper landscape composition, spatial configuration and materials on a local scale exert greater impacts on SUHI.