Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.

A Supplementary Module to Improve Accuracy of the Quality Assessment Band in Landsat Cloud Images

Cloud contamination is a serious obstacle for the application of Landsat data. To popularize the applications of Landsat data, each Landsat image includes the corresponding Quality Assessment (QA) band, in which cloud and cloud shadow pixels have been flagged. However, previous studies suggested that Landsat QA band still needs to be modified to fulfill the requirement of Landsat data applications. In this study, we developed a Supplementary Module to improve the original QA band (called QA_SM). On one hand, QA_SM extracts spectral and geometrical features in the target Landsat cloud image from the original QA band. On the other, QA_SM incorporates the temporal change characteristics of clouds and cloud shadows between the target and reference images. We tested the new method at four local sites with different land covers and the Landsat-8 cloud cover validation dataset (“L8_Biome”). The experimental results show that QA_SM performs better than the original QA band and the multi-temporal method ATSA (Automatic Time-Series Analyses). QA_SM decreases omission errors of clouds and shadows in the original QA band effectively but meanwhile does not increase commission errors. Besides, the better performance of QA_SM is less affected by the selections of reference images because QA_SM considers the temporal change of land surface reflectance that is not caused by cloud contamination. By further designing a quantitative assessment experiment, we found that the QA band generated by QA_SM improves cloud-removal performance on Landsat cloud images, suggesting the benefits of the new method to advance the applications of Landsat data.

Monitoring Spatial and Temporal Scales of Shoreline Changes in Lahou-Kpanda (Southern Ivory Coast) Using Landsat Data Series (TM, ETM+ and OLI)

Shoreline changes are crucial for assessing human-ecosystem interactions in coastal environments. They are a valuable tool for determining the environmental costs of socioeconomic growth along coasts. In this research, we present an assessment of shoreline changes along the eastern coast of Lahou-Kpanda of the Ivory Coast during the period from 1980 to 2020 by applying Digital Shoreline Analysis System method using Landsat Data Series. The measurement of the shoreline dynamics of the Lahou-Kpanda coastline is mainly described in three parts: the west straight cordon, the dynamics at the mouth and the east straight cordon. The findings show a drastic reduction in natural shorelines. The greatest transition occurred along the mouth segment of the coast, where the average erosive velocity approaches 90 meters each year and the average distance has decreased by around 2 kilometers. The Ivory Coast lost more than 40% of its biological shorelines between 1980 and 2020, according to this report, a worrying development because these are regions that were once biologically abundant and highly rich. In general, human operations on the Ivory Coast’s shorelines have never had such an impact. The effects of these changes on habitats, as well as the vulnerability of new shoreline investments to increased human activity and sea-level rise, must be measured.

An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.

Mapping Environmental Impacts of Rapid Urbanisation and Deriving Relationship between NDVI, NDBI and Surface Temperature: A Case Study

Urbanisation is a complex global phenomenon driven by unorganised expansion, increased immigration, and population explosion. Changes in land cover are one of the most critical components for managing natural resources and monitoring environmental impacts in this context. In the present study, a hybrid classification approach was applied to Landsat data to get insight into the urbanisation of the Chandigarh capital region from 2000 to 2020. The results demonstrate an increasing urbanisation tendency on the city’s outskirts, particularly in the north-western and southern directions. The most considerable alterations were seen in the class vegetation as it swiftly transformed to built-up regions. Two indices, namely NDVI and NDBI and surface temperature images, were also derived from studying their inter-relationships. The paper suggests a positive linear relationship between surface temperature and NDBI while a negative correlation between NDVI and NDBI. Such studies may help city planners to take timely and appropriate efforts to reduce the environmental consequences of urbanisation.

Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

Research on the change of Spatio-temporal characteristics of Urban built-up area based on remote Sensing-- A case study of Yulin City, Shaanxi Province

Since the reform and opening up, the process of urbanization in China has been at a high level. In order to accurately obtain the boundary of urban built-up area and the spatiotemporal change characteristics of urban built-up area in recent years, this paper takes Yulin City as an example. With the help of ArcGIS, ERDAS and other platforms, we combine spatial comparison method and index method to extract urban built-up area from DMSP-OLS night light data and Landsat image data. At the same time, the confusion matrix is used to verify the extraction boundary. The research shows that: (1) Using DMSP-OLS night light data and Landsat data to extract the built-up area, the overall accuracy can reach 99%; (2) The urban built-up area of Yulin City expanded at a high speed from 2005 to 2018; (3) The urbanization process in the northeast of Yulin City is obviously higher than that in the south in recent years. This study has a certain reference value for the extraction of urban built-up areas and the development of urbanization in Yulin City by using DMSP-OLS night light data and Landsat data.