The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region's vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami's impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia's recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions.

There is 78 % permafrost and seasonal frozen soil in the Yangtze River's Source Region (SRYR), which is situated in the middle of the Qinghai-Xizang Plateau. Three distinct scenarios were developed in the Soil and Water Assessment Tool (SWAT) to model the effects of land cover change (LCC) on various water balance components. Discharge and percolation of groundwater have decreased by mid-December. This demonstrates the seasonal contributions of subsurface water, which diminish when soil freezes. During winter, when surface water inputs are low, groundwater storage becomes even more critical to ensure water supply due to this periodic trend. An impermeable layer underneath the active layer thickness decreases GWQ and PERC in LCC + permafrost scenario. The water transport and storage phase reached a critical point in August when precipitation, permafrost thawing, and snowmelt caused LATQ to surge. To prevent waterlogging and save water for dry periods, it is necessary to control this peak flow phase. Hydrological processes, permafrost dynamics, and land cover changes in the SRYR are difficult, according to the data. These interactions enhance water circulation throughout the year, recharge of groundwater supplies, surface runoff, and lateral flow. For the region's water resource management to be effective in sustaining ecohydrology, ensuring appropriate water storage, and alleviating freshwater scarcity, these dynamics must be considered.

In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20-42% of the total losses and damages. The greatest economic losses occurred in Guaruj & aacute; (42%), followed by S & atilde;o Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guaruj & aacute; (107%), S & atilde;o Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guaruj & aacute; has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.

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.

Snow cover is an important control element in the Tibetan Plateau (TP) ecosystem; however, the impact of snow cover changes on gross primary productivity (GPP) is largely unknown, particularly under complex geographical conditions. In this study, we investigated the impacts of snow cover changes on different seasonal GPP and their mechanisms in different geographical zones using multisource remote sensing data from 1982 to 2018. Snow cover significantly affected GPP by nearly 15% of the TP region, and snow cover days (SCDs) were the dominant snow cover indicators affecting GPP variations compared with snow water equivalent (SWE) and snow cover end date (SCED). In general, an increase in snow cover leads to a significant increase in GPP in regions with low precipitation and temperature, but limits the accumulation of GPP in humid and warm regions. Furthermore, from the humid to the arid zone, the moisture effect of snow cover (by altering soil moisture) plays an increasingly important role in regulating GPP variations with increasing drought levels. This study elucidates the importance of snow cover in regulating different seasonal GPP variations and significantly improves our insight into the response of vegetation carbon uptake to snow cover changes in the TP.

Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988-2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 x 104 km2) and from alpine grassland to alpine meadow (17.43 x 104 km2) during 1988-2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influ-encing factors affecting vegetation greening on the QTP are precipitation (q -statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human develop-ment strategies.

Boreal forest and wetland have important influences on the development and protection of the ecosystem-dominated Xing'an permafrost. However, the responses of different ecosystems to climate change and the impacts on the underlying permafrost are still unclear. Here, based on the multi-period land use/land cover (LULC) data and long-time series of air temperature, combined with the ordinary least squares (OLS) and ordinary kriging (OK) methods, the effects of land use and cover change (LUCC) on the distribution of mean annual air temperature (MAAT) and permafrost in Northeast China were analyzed. From 1980s to 2010s, MAAT showed an upward trend (0.025 degrees C per yr) and extents of permafrost showed a decreasing trend (-3668 km(2)yr(-1)) in Northeast China. Permafrost degradation mainly occurred in forested land and grassland, with areal reductions of 4.0106 x 10(4) and 3.8754 x 10(4) km(2), respectively. The transformation of LULC aggravates the degradation of permafrost. The conversions of forested land and grassland to cultivated land and forested land to grassland resulted in the shrinkage of permafrost extent by 6233 km(2) from 1980s to 2010s . Our results confirm the significant impacts of LUCC on the Xing'an permafrost resulting in its degradation. Additionally, they can provide a scientific basis for ecological environment protection and restoration and sustainable development of boreal forest and wetland ecosystems in permafrost regions of Northeast China.

The Amazon Basin is at the center of an intensifying discourse about deforestation, land-use, and global change. To date, climate research in the Basin has overwhelmingly focused on the cycling and storage of carbon (C) and its implications for global climate. Missing, however, is a more comprehensive consideration of other significant biophysical climate feedbacks [i.e., CH4, N2O, black carbon, biogenic volatile organic compounds (BV0Cs), aerosols, evapotranspiration, and albedo] and their dynamic responses to both localized (fire, land-use change, infrastructure development, and storms) and global (warming, drying, and some related to El Nino or to warming in the tropical Atlantic) changes. Here, we synthesize the current understanding of (1) sources and fluxes of all major forcing agents, (2) the demonstrated or expected impact of global and local changes on each agent, and (3) the nature, extent, and drivers of anthropogenic change in the Basin. We highlight the large uncertainty in flux magnitude and responses, and their corresponding direct and indirect effects on the regional and global climate system. Despite uncertainty in their responses to change, we conclude that current warming from non-CO2 agents (especially CH4 and N2O) in the Amazon Basin largely offsets- and most likely exceeds-the climate service provided by atmospheric CO2 uptake. We also find that the majority of anthropogenic impacts act to increase the radiative forcing potential of the Basin. Given the large contribution of less-recognized agents (e.g., Amazonian trees alone emit similar to 3.5% of all global CH4), a continuing focus on a single metric (i.e., C uptake and storage) is incompatible with genuine efforts to understand and manage the biogeochemistry of climate in a rapidly changing Amazon Basin.

Climate change will alter ecosystem metabolism and may lead to a redistribution of vegetation and changes in fire regimes in Northern Eurasia over the 21st century. Land management decisions will interact with these climate-driven changes to reshape the region's landscape. Here we present an assessment of the potential consequences of climate change on land use and associated land carbon sink activity for Northern Eurasia in the context of climate-induced vegetation shifts. Under a `business-as-usual' scenario, climate-induced vegetation shifts allow expansion of areas devoted to food crop production (15%) and pastures (39%) over the 21st century. Under a climate stabilization scenario, climate-induced vegetation shifts permit expansion of areas devoted to cellulosic biofuel production (25%) and pastures (21%), but reduce the expansion of areas devoted to food crop production by 10%. In both climate scenarios, vegetation shifts further reduce the areas devoted to timber production by 6-8% over this same time period. Fire associated with climate-induced vegetation shifts causes the region to become more of a carbon source than if no vegetation shifts occur. Consideration of the interactions between climate-induced vegetation shifts and human activities through a modeling framework has provided clues to how humans may be able to adapt to a changing world and identified the trade-offs, including unintended consequences, associated with proposed climate/energy policies.

Analysis of time series imagery from satellite and aircraft platforms is useful for detecting land cover change at plot to regional scales. In this study, we created multi-temporal high spatial resolution land cover maps for seven locations in the Beringian Arctic and assessed the change in land cover over time. Land cover classifications were site specific and mostly aligned with a soil moisture gradient. Time series varied between 60 and 21 years. Four of the five landscapes studied in Alaska underwent an expansion of drier land cover classes while the two landscapes studies in Chukotka, Russia showed an expansion of wetter land cover types. While a range of land cover types was present across the landscapes studied, the extent of shrubs (in Chukotka) and open water (in Alaska) increased in all landscapes where these land cover types were present. The results support trends documented for regional change in NDVI (a measure of vegetation greenness and productivity) as well as a host of other long term, experimental and modeling studies. Using historic change trends for each land cover type at each landscape, we use a simple probabilistic vegetation model to establish hypotheses of future change trajectories for different land cover types at each of the landscapes investigated. This study is a contribution to the International Polar Year Back to the Future project (IPY-BTF).