North Korea being one of the most degraded forests globally has recently been emphasizing in forest restoration. Monitoring the trend of forest restoration in North Korea has important reference significance for regional environmental management and ecological security. Thus, this study constructed and analyzed a time-series land use land cover (LULC) map to identify the LULC changes (LULCCs) over extensive periods across North Korea and understand the forest change trends. The analysis of LULC used Landsat multi-temporal image and Random Forest algorithm on Google Earth Engine(GEE) from 2001 to 2018 in North Korea. Through the LULCC detection technique and consideration of the cropland change relation with elevation, the forest change in North Korea for 2001–2018 was evaluated. We extended the existing sampling methodology and obtained a higher overall accuracy (98.2% ± 1.6%), with corresponding kappa coefficients (0.959 ± 0.037), and improved the classification accuracy in cropland and forest cover. Through the change detection and spatial analysis, our research shows that the forests in the southern and central regions of North Korea are undergoing restoration. The sampling method we extended in this study can effectively and reliably monitoring the change trend of North Korea forests. It also provides an important reference for the regional environmental management and ecological security in North Korea.

Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

One method of understanding landscape pattern changes is through an understanding of land use/land cover (LULC) changes, which are closely related to landscape pattern changes. Previous studies have monitored LULC changes across North Korea but did not consider landscape changes at a local scale. Using multiple LULC products to construct sample points, the LULC was classified using a random-forest algorithm and Landsat satellite dataset. The overall accuracy of the classification was 97.66 ± 1.36%, and the Kappa coefficient was 0.95 ± 0.03. Based on the classification results, landscape indices were used to quantify and monitor landscape pattern changes. The results showed that, from 2000 to 2020, there was an increasing trend in built-up and forest areas in Pyongyang, while cropland showed a decreasing trend, and landscape fragmentation increased. However, urban expansion was not the main factor affecting fragmentation. The main factors were forest recovery and cropland reduction, leading to an increase in landscape fragmentation in Pyongyang.

The movement of wildlife can be constrained by river development projects owing to the presence of artificial structures. This study analyzes the spatial riparian crossing patterns of two mammal species, the leopard cat (Felis bengalensis euptilura) and water deer (Hydropotes inermis). We examine their ability to traverse from habitat on one side of the river to the other, through riparian vegetation, embankments, and the river,. We used 34 months of monitoring on 104 stream segments with varying levels of human modification on the Seom river, South Korea, to analyze their riparian crossing preferences across the river between suitable habitats on either side of the steam. Environmental variables were used for two scales of analysis: a watershed scale and a stream segment scale that examined both riparian habitat suitability and riparian crossings. At the watershed scale, habitat suitability was determined using species distribution modeling. The resulting maps were inputted with other predictor variables to logistic regression models to predict areas of high probability for the species' occurences. For the river segment analyses, we determined species’ presences and potential crossings from snow tracking, heat sensor cameras, and scat. We compared the predicted crossing locations from the watershed scale model to observed crossings. The watershed models indicate that leopard cats prefer upland forest, while water deer prefer the forest edge and riparian corridor. For both target species, the best riparian habitats were characterized by the presence of vegetation cover on the embankment and by at least one side of an embankment being adjacent to farmland or forest cover. The two species showed different riparian crossing preferences. Water deer crossed through large culverts or under bridges, whereas leopard cats utilized drainage pipes and smaller culvert boxes. Stream reaches located close to a river tributary had the highest connectivity values, and areas modeled as good habitats were linked near riparian and watershed areas with high density for both species as well as culvert boxes with a high openness ratio. Artifacts such as steep banks, concrete embankments, and adjacent roads were found to degrade the riparian connectivity of wildlife. These outcomes can be used to identify suitable riparian wildlife habitats of mammals, to evaluate lateral connectivity of riparian corridors for mammals, and to develop criteria for river conservation and restoration in urbanizing areas and developing countries.

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R² = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.