Severity of wildfires is a critical component of the fire regime and plays an important role in determining forest ecosystem response to fire disturbance. Predicting spatial distribution of potential fire severity can be valuable in guiding fire and fuel management planning. Spatial controls on fire severity patterns have attracted growing interest, but few studies have attempted to predict potential fire severity in fire-prone Eurasian boreal forests. Furthermore, the influences of fire weather variation on spatial heterogeneity of fire severity remain poorly understood at fine scales. We assessed the relative importance and influence of pre-fire vegetation, topography, and surface moisture availability (SMA) on fire severity in 21 lightning-ignited fires occurring in two different fire years (3 fires in 2000, 18 fires in 2010) of the Great Xing’an Mountains with an ensemble modeling approach of boosted regression tree (BRT). SMA was derived from 8-day moderate resolution imaging spectroradiometer (MODIS) evapotranspiration products. We predicted the potential distribution of fire severity in two fire years and evaluated the prediction accuracies. BRT modeling revealed that vegetation, topography, and SMA explained more than 70% of variations in fire severity (mean 83.0% for 2000, mean 73.8% for 2010). Our analysis showed that evergreen coniferous forests were more likely to experience higher severity fires than the dominant deciduous larch forests of this region, and deciduous broadleaf forests and shrublands usually burned at a significantly lower fire severity. High-severity fires tended to occur in gentle and well-drained slopes at high altitudes, especially those with north-facing aspects. SMA exhibited notable and consistent negative association with severity. Predicted fire severity from our model exhibited strong agreement with the observed fire severity (mean r² = 0.795 for 2000, 0.618 for 2010). Our results verified that spatial variation of fire severity within a burned patch is predictable at the landscape scale, and the prediction of potential fire severity could be improved by incorporating remotely sensed biophysical variables related to weather conditions.

Northeast China is a major grain production area, an ecological important forest area, and the largest old industrial base which is now suffering from economic growth slowdown and brain drain. Accurate and long-term dynamic land cover maps are highly demanded for many regional applications. In this study, we developed a set of continuous annual land cover mapping product at 30 m resolution using multi-temporal Landsat images. The maps in year 2000 and 2015 were tested using another independent validation dataset and the overall accuracies were 80.69% and 88.38%, respectively. The accuracies of the maps were improved by the integration of multi-temporal Landsat images and post-classification strategies. We found a general trend that the total area of land that experienced a change in land cover each year increased over time. The area change of each land cover type is also detected. The area of forests was $3.92\times {10}^5$ km ${}^2$ in 1986, fluctuated under fire disturbance, but declined in a quite high rate over the period of 1989 to 2006, and finally stayed relatively stable in area around $3.58\times {10}^5$ km ${}^2$ . The expansion of croplands was the leading land cover change from 1986 to 2000, and then the total area of croplands slightly declined under the Grain to Green Project of China, while shrublands, grasslands and wetlands began to increase. The area of impervious surfaces increased by more than 502% during the last three decades, and about 73% of the new built-up area was converted from croplands. We also demonstrated the our maps could capture the important land cover conversion processes, such as urbanization, forest logging activities, and agricultural expansion.

Surface mining is a major driver of land use land cover (LULC) change in many mountainous areas such as the Appalachian region. Typical reclamation practices often result in land cover dominated by grass and shrubs. Assessing ecosystem services that can be obtained from a forest landscape may help policy-makers and other stakeholders fully understand the benefits of forestry-based reclamation (FRA). The objectives of this study are to (1) identify how surface mining and reclamation changed the LULC of a watershed encompassing the north fork of the Kentucky River, (2) assess the biophysical value of four major ecosystem services under the contemporary LULC condition, and (3) assess the benefits of the FRA scenario in the provision of ecosystem services. Geographic Information System (GIS) was used to study the LULC change and InVEST software models for ecosystem services assessment. The results indicate that watershed’s forest area has decreased by 7751 hectares from 2001 to 2011 and mining/reclamation activities may have contributed 65% of the overall changes in LULC. Barren and grassland land covers provide less carbon storage, yield more water, and export more sediments and nutrients than forests. At the watershed level, the FRA scenario increased carbon storage (13%) and reduced water yield (5%), sediment export (40%), and nutrient export (7%). The provision of these ecosystem services varies at the subwatershed level, and such spatial heterogeneity is primarily driven by land cover composition, precipitation, and topography. This study provides critical information regarding the ecological benefits of restoring mined land to assist policy and decision making at landscape scales.

Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m²·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.

Anticipating how boreal forest landscapes will change in response to changing fire regime requires disentangling the effects of various spatial controls on the recovery process of tree saplings. Spatially explicit monitoring of post-fire vegetation recovery through moderate resolution Landsat imagery is a popular technique but is filled with ambiguous information due to mixed pixel effects. On the other hand, very-high resolution (VHR) satellite imagery accurately measures crown size of tree saplings but has gained little attention and its utility for estimating leaf area index (LAI, m²/m²) and tree sapling abundance (TSA, seedlings/ha) in post-fire landscape remains untested. We compared the explanatory power of 30 m Landsat satellite imagery with 0.5-m WorldView-2 VHR imagery for LAI and TSA based on field sampling data, and subsequently mapped the distribution of LAI and TSA based on the most predictive relationships. A random forest (RF) model was applied to assess the relative importance and causal mechanisms of spatial controls on tree sapling recovery. The results showed that pixel percentage of canopy trees (PPCT) derived from VHR imagery outperform all Landsat-derived spectral indices for explaining variance of LAI (R²_VHR = 0.676 vs. R²_Landsat = 0.427) and TSA (R²_VHR = 0.508 vs. R²_Landsat = 0.499). The RF model explained an average of 55.5% (SD = 3.0%, MSE = 0.382, N = 50) of the variation of estimated LAI. Understory vegetation coverage (competition) and post-fire surviving mature trees (seed sources) were the most important spatial controls for LAI recovery, followed by burn severity (legacy effect), topographic factors (environmental filter) and nearest distance to unburned area (edge effect). These analyses allow us to conclude that in our study area, mitigating wildfire severity and size may increase forest resilience to wildfire damage. Given the easily-damaged seed banks and relatively short seed dispersal distance of coniferous trees, reasonable human help to natural recovery of coniferous forests is necessary for severe burns with a large patch size, particularly in certain areas. Our research shows the VHR WorldView-2 imagery better resolves key characteristics of forest landscapes like LAI and TSA than Landsat imagery, providing a valuable tool for land managers and researchers alike.