When developing Arctic territories, studying and forecasting the state of cryogenic landscapes in the context of climate change plays an important role. General conclusions about permafrost degradation do not fully capture changes at regional and local levels, as the direction and pace of landscape transformation depend on many factors, including the specific characteristics of the terrain. Permafrost degradation and changes in the depth of the active layer thickness (ALT) can be accompanied by alterations in ground vegetation cover (GVC) and surface moisture, which can be recorded through remote sensing (RS) data. However, there is a knowledge gap regarding the use of RS data to identify long-term trends in the phytocenotic properties of GVC and soil moisture at different geomorphological levels, as well as to determine the relationship between these trends and changes in ALT. In this study, based on Landsat data from 1985 to 2024, changes in GVC and soil moisture across various geomorphological levels were identified in a local area of the Yamal Peninsula. The analysis used the NDVI vegetation index, the NDWI moisture index, and the WI (Wetness Index) temperature-vegetation index, which reflects the moisture content of GVC and soil. The general trend observed is an increase in the growth rates of these indices as the geomorphological levels rise from the floodplain to Terrace IV. A comparison of these observed trends in the NDVI, NDWI, and WI indices with in situ geocryological observations shows the potential of using these indices as indicators of ALT change.

利用1990—2024年间的Landsat遥感影像与气象数据，文章通过多时相影像计算归一化水体指数NDWI，结合K-means聚类方法计算羊卓雍措面积，并用一元线性拟合分析其变化趋势。羊湖在1996—2004年间显著扩张，受降水和融水补给增加，输入量超过输出量；2004—2014年间则经历了明显的缩减，归因于气温升高加剧蒸发，且融水和降水输入未显著变化，导致输入量小于输出量。利用傅里叶变换分析湖泊面积时序特征，发现其变化具有低频特性。在不同时间尺度上，羊湖面积的变化受降水、气温和积雪影响的具体过程各不相同。在超过15年周期（0.03 Hz,0.06 Hz）的低频变化中，羊湖面积与降水呈弱相关性，主要受到气温升高和积雪融化的影响，涉及蒸发量的增减以及积雪融化的促进或抑制。在10～15年周期（0.09 Hz,0.12 Hz）范围内，湖泊面积变化由降水和气温共同调控，影响湖泊水量的收支平衡。气候变暖是驱动羊湖面积年代尺度上变化的主要因素。

Key messageIntegrating airborne laser scanning and satellite time series data across the forest rotation enhances decision-making in precision forestry. This review supports forest managers by illustrating practical applications of these remote sensing technologies at different stages of intensive forest plantation management-such as site assessment, monitoring, and silviculture-helping improve productivity, sustainability, and operational efficiency.ContextIntensively managed forest plantations depend on high-resolution, timely data to guide silviculture and promote sustainability.AimsThis review explores how airborne laser scanning (ALS) and satellite time series data support precision forestry across key stages, including site assessment, establishment, monitoring, inventory updates, growth tracking, silvicultural interventions, and harvest planning.ResultsThe review highlights several key applications. ALS-derived digital elevation models and canopy metrics improve site productivity estimation by capturing micro-topographic variables and soil formation factors. Combining ALS with multispectral data enhances monitoring of seedling survival and health, although distinguishing seedlings from non-living components remains a challenge. ALS-based Enhanced Forest Inventories provide spatially detailed forest metrics, while satellite time series and vegetation indices support continuous monitoring of growth and early detection of drought, fire, and pest stress. ALS individual tree detection models offer insights into competition, stand structure, and spatial variability, informing thinning and fertilization decisions by identifying trees under stress or with high growth potential. These models also help mitigate drought and wind damage by guiding density and canopy structure management. ALS terrain data further support harvest planning by optimizing machinery routes and reducing environmental impacts.ConclusionDespite progresses, challenges remain in refining predictive models, expanding remote sensing applications, and developing tools that translate complex data into field operations. A major barrier is the technical expertise needed to interpret spatial data and integrate remote sensing into workflows. Continued research is needed to improve accessibility and operational relevance. High-resolution data still offer strong potential for adaptive management and sustainability.

河冰遥感判别对冰情监测提供了重要的支持。河冰指数判别方法是河冰遥感判别的核心工具。然而,目前仍缺乏对常用指数判别模型在不同河道类型的综合性对比研究。针对此问题,本研究采用了5种遥感指数模型(RDRI、NDSI、MNDSI、NDWI、反射率阈值法),选择黄河上游河道不同特征的6个研究区3种河道类型,对不同研究区中河冰指数模型的阈值稳定性、精度和适用性进行了分析讨论。结果表明:5种遥感指数模型的构建方式共同反映出河冰在可见光、近红外和短波红外波段的光谱特性是河冰判别最为重要的基础。RDRI指数在多个方面表现最佳,平均Kappa系数为0.914 4,推荐其作为河冰指数判别方法的最优选择。NDSI和MNDSI指数可以通过调整阈值有效排除浅雪的干扰。NDSI、MNDSI和NDWI指数在河源段研究区的精度表现良好,而反射率阈值法在性能上稍逊于RDRI指数,但其算法简单仍然具有一定的应用价值。对于不同河道类型的研究区,5种遥感指数模型的在顺直河道的精度最高,弯曲河道次之,分叉河道最低。

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

冰川是最重要的淡水储存库之一,精确识别冰川和监测冰川的变化对于了解气候变化和水资源管理具有重要意义。基于Landsat 8影像,以喀喇昆仑区域为研究对象,利用单波段阈值法、雪盖指数法、非监督分类、监督分类和U-Net卷积神经网络提取冰川边界,并以交并比和混淆矩阵对冰川边界提取结果进行精度评定。结果表明,非监督分类和单波段阈值法对于表碛覆盖型冰川以及阴影中冰川存在严重的漏分现象,易将薄雪覆盖的山地错分为冰川,K-means的提取效果最差,交并比为57.69%,Kappa系数为0.57。监督分类方法对于表碛覆盖型冰川的提取效果有明显改善,但对于阴影中的冰川的提取效果不佳,提取结果的Kappa系数均为0.70以上。雪盖指数法可以有效提取阴影中的冰川,但易将大面积冰川中的非冰川区域错分为冰川,交并比为74.49%,Kappa系数为0.76。U-Net卷积神经网络能够较完整地提取冰川边界,精度要明显高于其他分类方法,重叠面积最接近地面真值面积,其交并比为88.57%,Kappa系数为0.90。U-Net卷积神经网络虽然表现较好,但是对于极小面积冰川仍存在漏分,后续研究可通过改进网络结构来提高精度...

Supra-permafrost groundwater (SPG) is a key factor that causes damage to highways and railways in the Qinghai-Tibet Engineering Corridor (QTEC). It is difficult to monitor SPG in the field due to their complex formation mechanisms and movement characteristics. Traditional single-site field monitoring studies limit the spatial and temporal precision of SPG spatial distribution. To determine the moisture content of shallow soils and the SPG distribution along the QTEC, this work employed the temperature vegetation dryness index and remote sensing models for groundwater table distribution models. The accuracies of the models were validated using measurements obtained from different sites in the corridor. In the permafrost zones of the QTEC, 72%, 22% and 6% of the SPG were located at depths of 0.5-1, 1 m, respectively. Meanwhile, 79.4% of the area along the Qinghai-Tibet Highway (QTH) (Xidatan-Tanggula) contained SPG. In these sections with SPG, 37.9% have an SPG table at depths of 0.5-0.8 m. This study preliminarily explored the SPG distribution in the QTEC with a 30 m resolution. The findings can help improve the spatial scale of SPG research, provide a basis for the analysis of the hydrothermal mechanisms, and serve as a guide in the assessment of operational risks and road structure designs.

Quantitatively evaluating the ecological environment impacts of vegetation destruction due to open-pit mining activities is vital for enhancing the green mining standard and cost management capabilities of mining enter-prises. Based on the Landsat time series, this study proposes an ecological environment impact assessment and quantitative characterization method for vegetation destruction in mining areas resulting from open-pit mining activities. First, the modified normalized difference water index and the normalized difference vegetation index time series data were calculated. The water body thresholds and the fraction of vegetation coverage were ascertained using the K-means clustering algorithm and the dimidiate pixel model, respectively, to determine the area of direct vegetation destruction in mining areas. Second, utilizing the Theil-Sen Median trend analysis and the Mann-Kendall test, the indirect impact area of vegetation in the mining region was identified. Lastly, by integrating vegetation's net primary productivity with the Chinese Emission Allowance price index, the total carbon emission cost of vegetation destruction due to mining activities over 20 years was calculated to be about 2.122 million yuan. The findings indicated that the ecological environmental impact of open-pit mining activities on vegetation destruction cannot be ignored. From 2000 to 2020, open-pit mining at the Wulishan limestone mine in Anhui Province, China, increased the area of direct vegetation destruction by 9.072 x 105 m2, and the indirect impact area on vegetation was 7.371 x 105 m2. The carbon emission cost of vegetation destruction in the direct destruction area was about 104,000 yuan per year, and the carbon emission cost of vegetation damage in the indirect impact area was approximately 2,082.53 yuan per year. This research provides a scientific foun-dation for ecological environmental protection, regulations, green mining, and cost management for mining enterprises, promoting the harmonious progress of both the economy and environmental protection.

Global warming, increasing population, and parched soils are escalating the frequency and intensity of forest fires. Global warming raises temperatures and extends droughts, making forests more susceptible to fires. A growing population pressures forest areas for settlement and agriculture, increasing fire risk. Dry soils and vegetation ignite easily, accelerating fire spread. After fires, damage assessment and reforestation are crucial. This study examines the impact of the July 18, 2023, forest fire on Rhodes Island's vegetation. Using spectral analyses of Landsat 8 images, the fire's damage to vegetation was assessed. The NBR (Normalized Burn Ratio) index determined pre- and post-fire vegetation changes. The burned area was calculated using dNDVI and dNBR. While dNDVI measures vegetation health, dNBR detects burned areas before and after a fire. The burned area was 16.037 ha using dNDVI and 17.678 ha using dNBR, showing consistent results. The burned area signals significant ecological consequences like habitat loss, negative impacts on biodiversity, and increased soil erosion. These analyses are essential for planning ecosystem recovery and developing appropriate restoration strategies after a fire.

Over the past several decades, various trends in vegetation productivity, from increases to decreases, have been observed throughout Arctic-Boreal ecosystems. While some of this variation can be explained by recent climate warming and increased disturbance, very little is known about the impacts of permafrost thaw on productivity across diverse vegetation communities. Active layer thickness data from 135 permafrost monitoring sites along a 10 degrees latitudinal transect of the Northwest Territories, Canada, paired with a Landsat time series of normalized difference vegetation index from 1984 to 2019, were used to quantify the impacts of changing permafrost conditions on vegetation productivity. We found that active layer thickness contributed to the observed variation in vegetation productivity in recent decades in the northwestern Arctic-Boreal, with the highest rates of greening occurring at sites where the near--surface permafrost recently had thawed. However, the greening associated with permafrost thaw was not sustained after prolonged periods of thaw and appeared to diminish after the thaw front extended outside the plants' rooting zone. Highest rates of greening were found at the mid-transect sites, between 62.4 degrees N and 65.2 degrees N, suggesting that more southernly sites may have already surpassed the period of beneficial permafrost thaw, while more northern sites may have yet to reach a level of thaw that supports enhanced vegetation productivity. These results indicate that the response of vegetation productivity to permafrost thaw is highly dependent on the extent of active