Forest fires are major disturbances that reshape ecosystem structure and function, yet post-fire trajectories of vegetation greenness and productivity often diverge. The spatial patterns and drivers of this structural–functional decoupling remain insufficiently understood. Here, we quantified differences between normalized recovery trends of normalized difference vegetation index (NDVI) and net primary productivity (NPP) in China's forests during the first five years following single-burn events (2001–2015) using a metric termed ΔnSlope (defined as the difference between normalized NDVI and NPP recovery slopes). By applying a sensitivity threshold of 0.5 standard deviations to distinguish significant decoupling from background noise, we found that 53.93% of the burned areas exhibited significant structural–functional decoupling. Specifically, functional recovery (NPP) significantly outpaced structural recovery (NDVI) in 29.60% of the total burned area. Spatially, NPP‑lead decoupling was most pronounced in northeastern forests and the North China–Inner Mongolia ecotone, while synchronous or NDVI‑lead patterns occurred in southwestern regions. Using a random forest model on the continuous dataset, we identified that fire-year precipitation was the strongest positive driver of synchrony, whereas post-fire drought stress amplified divergence. Topography and soil properties further modulated heterogeneity, with higher elevation, steeper slopes, and sandy soils increasing decoupling by constraining resource availability. These findings reveal substantial spatial variability in post-fire ecosystem recovery across China and highlight the dominant role of water availability in shaping structural–functional dynamics. The results provide a scientific basis for region-specific forest restoration and resilience management under a changing climate.

As a key biophysical parameter describing forest vegetation structure, Leaf Area Index (LAI) is an essential and widely used indicator for evaluating forest ecosystem function and health. LAI retrieval from remote sensing observations primarily relies on canopy radiative transfer models (RTMs) that quantitatively characterize the complex relationship between canopy parameters and reflectance. However, most physical models currently used for LAI retrieval are one-dimensional (1D) RTMs, which typically assume the canopy to be horizontally homogeneous and thus fail to capture the inherent heterogeneity within the canopy. Although three-dimensional (3D) RTMs can better characterize the structural complexity of forest canopies, their high computational demand and the difficulty of parameterization often limit their application to large-scale remote sensing retrievals. In this study, a novel 3D Look-Up Table (3D-LUT) approach was developed for retrieving forest LAI from Landsat by accounting for the heterogeneity within forests through the integration of LiDAR-based scene reconstructions to parameterize the RTM. Instead of using idealized homogeneous layers or simple geometric objects, our approach used airborne LiDAR data to reconstruct realistic and structurally representative 3D forest scenes for typical forest types, including Deciduous Broadleaf Forest (DBF), Deciduous Needleleaf Forest (DNF), Evergreen Broadleaf Forest (EBF), and Evergreen Needleleaf Forest (ENF). Based on these reconstructed forest scenes, type-specific LAI look-up tables (LUTs) were built by coupling the 3D RTM Large-scalE remote Sensing data and image Simulation (LESS) with an analytical model PATH_RT, an accurate and efficient RTM based on 3D path-length distribution and spectral invariant theory, enabling accurate LAI retrieval from Landsat imagery. This method was compared against field observations collected from 16 National Ecological Observatory Network (NEON) sites and 8 Integrated Carbon Observation System (ICOS) sites, which comprise a representative sample of different forest types. Additionally, intercomparison was conducted using the High-resolution Global LAnd Surface Satellite (Hi-GLASS) LAI product, Simplified Level-2 Prototype Processor (SL2P) algorithm and the MODIS LAI product. Validation against in situ data demonstrated that the proposed algorithm can achieve high-accuracy retrieval of LAI across four forest types, with RMSE ranging from 0.93 to 1.20 m2/m2 and MAE from 0.73 to 1.00 m2/m2. The intercomparison results revealed that retrieval algorithms based on the PROSAIL model, such as SL2P, tend to underestimate forest LAI. In contrast, the proposed algorithm shows strong overall agreement with the Hi-GLASS LAI product and MODIS LAI product, which are derived from a deep learning framework and a 3D RTM, respectively, supporting its reliability for regional-scale forest LAI retrieval. By generating the simulated dataset derived from realistically reconstructed 3D forest structures using LiDAR data, this study further advances the application of LiDAR in quantitative remote sensing retrieval.

Foliage area volume density (FAVD) and leaf chlorophyll content (LCC) are two key traits closely linked to the structure and physiological status of trees. However, their physically-based retrieval at the individual tree level has remained challenging due to the complex interactions of scattering and absorption within the irregularly shaped tree crowns, as well as multiple scattering among neighboring trees, particularly in the near-infrared (NIR) spectrum. In this study, we proposed a tree-specific retrieval strategy that leverages unmanned aerial vehicle (UAV) imagery and corresponding photogrammetric point clouds to establish a tree-specific spatial adjacency constraint within the three-dimensional (3D) RTM-based inversion procedure for each individual tree. Unlike previous approaches that relied exclusively on pixel-level information from the region of interest, the proposed method fully accounted for the multiple scattering from adjacent trees and explicitly incorporates the irregularity of tree crown shapes. In the RTM-based prediction of the spectral reflectance of a focal tree (i.e., the target tree), the structures of adjacent trees were integrated alongside the focal tree, thereby forming a spatial adjacency constraint. This ensures that the scattering regime of the focal tree in the simulated scenario aligns with that of the actual scenario. The proposed method was assessed using both real UAV data and synthetic datasets. The results showed that tree-level retrieval under the adjacency constraint was highly consistent with reference (RRMSE of less than 0.22), whereas retrieval without the adjacency constraint exhibited substantial mis-estimation, particularly for FAVD (RRMSE of up to 0.44). Although the multiple scattering from adjacent trees was primarily influenced by the illumination geometry and tree canopy cover (TCC), sensitivity analysis of the sun zenith angle (SZA) and TCC revealed that retrieval accuracy slightly improved with a decreasing SZA and an increasing TCC. This improvement can be attributed to the enhanced treatment of multiple scattering under these conditions. These findings underscore the effectiveness of the tree-specific retrieval strategy for accurately estimating plant functional traits across forest stands. Moreover, they suggest the potential for monitoring functional diversity and long-term ecosystem process at the forest landscape scale through the use of functional traits.

Burn severity assessment is critical for understanding the pattern of post-fire vegetation recovery and ecosystem resilience. Previous studies proposed various field criteria (e.g., Composite Burn Index (CBI)) to quantify burn severity from strata level to total site level, yet suffering from surveyors' subjective interpretation across site conditions. High-resolution passive remote sensing allows for more objective assessment based on the strong relation between fire damages and spectral features. Importantly, burn severity generally characterizes differences between forest overstory and understory layers due to their discrepancies in vegetation structures and environmental conditions. Spatially explicit mapping of strata-level burn severity is vital for post-fire forest management and ecological function evaluation. Until now, almost no available spaceborne remote sensing method can concurrently assess overstory and understory burn severity over heterogeneous forests. In this study, we proposed a Hybrid Composite Burn Index (HCBI) that comprehensively indicates the fire-induced spectral and structural changes along the vertical profile by integrating spectral and waveform attributes from both active and passive remote sensing data. Firstly, we introduced two remote sensing-based rating factors namely relative spectral change (RSC) and relative waveform change (RWC), and established HCBI through weighting and scoring the rating factors. Subsequently, we evaluated the effectiveness and generality of overstory, understory, and total site HCBI based on various pairs of simulated remote sensing data of pre-fire and post-fire forest scenes. Thirdly, we derived the spatially discontinuous maps of overstory, understory, and total site HCBI of the Xiushan fire site in Great Xing'an Mountain using WorldView-2 (WV-2) multispectral imagery and Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR data. Finally, we produced wall-to-wall HCBI maps using multiple predictive variables from pre-fire and post-fire Sentinel-2 MultiSpectral Instrument (MSI) images with a Random Forest (RF) algorithm. The predicted overstory, understory, and total site HCBI were validated by field-surveyed CBI. The assessment results based on simulation data showed HCBI was sensitive to the fire damage regardless of burn severity levels and vegetation cover levels (R2 of larger than 0.97 and RMSE of <0.15). The prediction results based on RF models achieved reliable HCBI of the total site, overstory, and understory levels (R2 of around 0.85 and RMSE of around 0.30). We found the wall-to-wall maps of HCBI captured the subtle horizontal and vertical variation even in the case of understory burn alone. We concluded that the newly proposed HCBI can advance the remote sensing-based assessment of stratified burn severity, offering opportunities for making fine-scale forest management decisions.

Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40-6.92 mu g/cm2 for simulation datasets and 8.21-9.76 mu g/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76-9.83 mu g/cm2 for simulation datasets and 12.76-13.06 mu g/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval.

High-severity fire altered the landscape pattern and destroyed forest resilience in the Great Xing'an Mountain. Predicting the spatiotemporal patterns of forest recovery in high-severity burned areas is critical for formulating effective post-fire management measures. Previous studies attempted to address this topic by fitting the recovery trajectory based on time-series remote sensing vegetation indices. However, these methods typically require long-term time series of satellite images for prediction and have sometimes been demonstrated as an unrealistic representation of the post-fire forest patterns due to the saturation issues of vegetation indices. In this study, we proposed a novel approach to predict post-fire forest recovery patterns by coupling a process-based Physiological Principles in Predicting Growth (3-PG) model with bi-temporal satellite imagery. First, based on tree core data, we calibrated the physiological parameters of the 3-PG model using a Model-Independent Parameter Estimation (PEST) model. Second, we used vegetation indices derived from bi-temporal Landsat data to estimate site-specific parameters of the 3-PG model and extrapolated the meteorological factors by a Mountain Microclimate Simulation Model (MTCLIM). Third, we used these variables as input parameters to drive the 3-PG model, resulting in the simulation of the post-fire 50-year regeneration dynamic of the leaf area index (LAI). The calibrated 3-PG model was validated by the independent sites, providing good estimates of DBH and biomass components with R2 of greater than 0.994 and RRMSE of less than 15.7 %. The 3-PG model predicted LAI maps were evaluated by referenced LAI retrieved from Landsat data, showing a high correlation with reference but the accuracy decreased with the prediction time increased. The model predicted forest recovery to pre-fire LAI within 32 years where the longest recovery interval was observed in the low seedling density and soil fertility. This study shows great potential for reasonable prediction and assessment of forest recovery after the fire occurrence. By combining more simulated scenarios, our approach also facilitates the exploration of the impact of artificial measures and climate on post-forest recovery.

>Forestry protection has constituted a fundamental element of China's forest management policy for the past four decades,and has played a critical role in increasing forest area and biomass stock [1,2].Nevertheless, an efficacious forest management policy should balance the dual roles of forests, serving as both carbon sinks and ecosystems [3],

Predicting the dynamic process of post-fire regrowth is critical for understanding the specific forest succession trajectory. 3-PGmix model (i.e., Physiological Principles in Predicting Growth for mixed stands) has been reported as a powerful tool for predicting the growth of mixed forest species. However, the prediction of post-fire forest productivity at a regional scale is infrequently documented and not well understood. In this study, we used remote sensing vegetation parameters to retrieve a series of site-specific parameters for driving the 3PGmix model to simulate the spatial-scale dynamics of post-fire vegetation net primary production (NPP) recovery and predict the response of NPP under different future climate conditions. The result indicated that the extended 3PGmix model can accurately simulate the post-fire dynamic of NPP at spatial scales and the predictions are consistent well with the LAI estimated NPP based on the 3PGS model. A higher fertility rating (FR) was predicted to accelerate the process of post-fire forest successional and shorten the duration time when the species proportion achieves balance and Climate change promoted the increase of NPP in the sequence of RCP 8.5 > RCP 4.5 > current climate.

Plant area density (PAD) of individual trees is an important structural indicator related to tree growth status, stress levels due to pests and diseases, photosynthesis potential, and evapotranspiration. Airborne laser scanning (ALS) provides unprecedented 3D information for mapping forest canopy parameters. Previous studies mainly focused on mapping stand-level and 2D leaf area index. This study proposes a method to estimate PAD from discrete and multiple return ALS data at individual tree scales. The proposed method uses path length distribution to eliminate crown-shape-induced clumping, as well as intensity information to estimate crown transmittance from relative low-density points. The path length distribution is derived from the 3D crown boundary contours created by an alpha shape algorithm, which explicitly considers the non-uniform LiDAR pulse penetration distances. Pulse intensity is calibrated with the nearest pure-ground pulse to mitigate the need for prior leaf and ground reflectance information, which can be used in areas with a heterogeneous background. The proposed method was evaluated both in virtual experiments as well as with terrestrial laser scanning (TLS) data. The virtual experiments used the large-scale remote sensing data and image simulation model (LESS) to simulate virtual ALS scanning data based on abstract and realistic canopies. Results showed that the ALS-derived PAD is highly accurate, with RMSE less than 0.02 and R2 > 0.99 for the abstract sphere and cube crowns, and RMSE = 0.19 and R2 = 0.578 for the realistic crowns. The comparison with TLS of a birch plot shows that the ALS-derived PAD is consistent with those derived from TLS, with RMSE = 0.14 and R2 = 0.46. This study demonstrated that using the full intensity and geometry information of a point cloud is capable of generating high-resolution forest parameters from ALS data.

The Yellow River Basin serves as a crucial ecological barrier in China, emphasizing the importance of accurately examining the spatial distribution of forest carbon stocks and enhancing carbon sequestration in order to attain “carbon peaking and carbon neutrality”. Forest patches have complex interactions that impact ecosystem services. To our knowledge, very few studies have explored the connection between these interactions and carbon stock. This study addressed this gap by utilizing complex network theory to establish a forest ecospatial network (ForEcoNet) in the Yellow River Basin in which forest patches are represented as nodes (sources) and their interactions as edges (corridors). Our objective was to optimize the ForEcoNet’s structure and enhance forest carbon stocks. First, we employed downscaling technology to allocate the forest carbon stocks of the 69 cities in the study area to grid cells, generating a spatial distribution map of forest carbon density in the Yellow River Basin. Next, we conducted morphological spatial pattern analysis (MSPA) and used the minimum cumulative resistance model (MCR) to extract the ForEcoNet in the basin. Finally, we proposed optimization of the ForEcoNet based on the coupling coordination between the node carbon stock and topological structure. The results showed that: (1) the forest carbon stocks of the upper, middle, and lower reaches accounted for 42.35%, 54.28%, and 3.37% of the total, respectively, (2) the ForEcoNet exhibited characteristics of both a random network and a scale-free network and demonstrated poor network stability, and (3) through the introduction of 51 sources and 46 corridors, we optimized the network and significantly improved its robustness. These findings provide scientific recommendations for the optimization of forest allocation in the Yellow River Basin and achieving the goal of increasing the forest carbon stock.

Understanding post-fire forest recovery is critical to the study of forest carbon dynamics. Many previous studies have used multispectral imagery to estimate post-fire recovery, yet post-fire forest structural development has rarely been evaluated in the Great Xing’an Mountain. In this study, we extracted the historical fire events from 1987 to 2019 based on a classification of Landsat imagery and assessed post-fire forest structure for these burned patches using Global Ecosystem Dynamics Investigation (GEDI)-derived metrics from 2019 to 2021. Two drivers were assessed for the influence on post-fire structure recovery, these being pre-fire canopy cover (i.e., dense forest and open forest) and burn severity levels (i.e., low, moderate, and high). We used these burnt patches to establish a 25-year chronosequence of forest structural succession by a space-for-time substitution method. Our result showed that the structural indices suggested delayed recovery following the fire, indicating a successional process from the decomposition of residual structures to the regeneration of new tree species in the post-fire forest. Across the past 25-years, the dense forest tends toward greater recovery than open forest, and the recovery rate was faster for low severity, followed by moderate severity and high severity. Specifically, in the recovery trajectory, the recovery indices were 21.7% and 17.4% for dense forest and open forest, and were 27.1%, 25.8%, and 25.4% for low, moderate, and high burn severity, respectively. Additionally, a different response to the fire was found in the canopy structure and height structure since total canopy cover (TCC) and plant area index (PAI) recovered faster than relative height (i.e., RH75 and RH95). Our results provide valuable information on forest structural restoration status, that can be used to support the formulation of post-fire forest management strategies in Great Xing’an Mountain.

Fire disturbance has been one of the main reasons for the alteration of forest succession trajectory and forest composition in the Daxing’anling Mountain of Inner Mongolia. Therefore, predicting the dynamic process of post-fire regrowth is critical for understanding the specific forest succession trajectory of this region. The 3-PGmix model (i.e., Physiological Principles in Predicting Growth for mixed stands) has been reported as a powerful tool for predicting the growth of mixed forest species. However, simulating post-fire regrowth of mixed forest with 3-PGmix remains challenging due to the uncertainty of species-specific as well as site-specific model parameters. Based on the field measurements from a wide range of environmental conditions, the 3-PGmix model was calibrated by conducting the sensitivity analysis and optimization of species-specific parameters for mixed forest stands of larch (Larix gmelinii) and birch (Betula platyphylla) with the PEST model (Model-Independent Parameter Estimation), and by using our newly developed methods to accurately estimating site-specific parameters (e.g., fertility rating, stand density, climate factors). The calibrated 3-PGmix model was tested against the independent sites and predicted the characteristic of post-fire forest regrowth. The sensitivity analysis shows that the parameters describing the forest canopy are more sensitive to the objective function of the PEST model. The calibrated 3-PGmix model performed well on independent sites, resulting in agreement with the field-measured diameter at breast height (DBH), the biomass of components, and stand density with a bias of less than 15% on most sites. The 3-PGmix model prediction of post-fire forest recovery characteristics found that the highest recovery rate of 5.5% per year was predicted from the moderate burn severity scenarios. A higher fertility rating was predicted to accelerate the process of post-fire forest successional and shorten the duration time when the species proportion achieves balance. The mixed-forest specific calibration of the 3-PGmix model in this study enables the explicit prediction of post-fire forest succession, and more simulations over different spatial scales with a wide range of environmental conditions by coupling with remote sensing observations can be expected.

One of the main initiatives for China to achieve the goal of being carbon neutral before 2060 is transforming monocultures into mixed plantations in subtropical China, because mixed forests possess a higher quality than monocultures in various ways. Very high spatial resolution (VHR) satellite imagery is very promising to precisely monitor the transformation process under the premise of clarifying the canopy reflectance anisotropy of mixed plantations. However, it is almost impossible to understand the canopy reflectance anisotropy of mixed plantations with real satellite data due to the extreme lack of multiangular VHR satellite images. In this study, the effects of the mixture mode on the canopy bidirectional reflectance factor (BRF) were comprehensively analyzed with simulated VHR images. The three-dimensional (3D) Discrete Anisotropic Radiative Transfer model (DART) was used to construct a pure coniferous scene, a pure broadleaved scene, and 27 coniferous–broadleaved mixed plantation scenes containing 3 mixture patterns (i.e., mixed by single trees, mixed by stripes, and mixed by patches) and 9 mixing proportions (i.e., from 10% to 90% with the interval of 10%), and to simulate red (R) and near-infrared (NIR) VHR images for these 3D scenes at both the solar principal plane (SPP) and perpendicular plane (PP) under different solar-viewing geometries. Negative correlations were generally found between the canopy BRF and the ratio of conifers in a mixed stand. The anisotropy of conifer dominated plantations is more prominent than broadleaf dominated plantations, especially for the single tree mixture. Although the level of anisotropy is much lower for PP than SPP, it should not be ignored, especially for the R band. Observations under large viewing zenith angles at PP are more preferred to study the effect of mixing proportions, followed by forward observations at SPP. The R band image has higher potential to distinguish mixture patterns for broadleaf-dominated situations, while the NIR band image has a higher potential for conifer-dominated situations. Furthermore, the canopy BRF generally increases with the solar zenith angle, and one meter can be considered as the optimal spatial resolution for the optical monitoring of the mixture mode. The findings of the current study add some valuable theoretical knowledge for the accurate monitoring of coniferous–broadleaved mixed plantations with VHR imagery.

Fire is a major disturbance in Daxing'anling Mountain in China, affecting ecosystem carbon cycle significantly. However, the return of forest productivity at regional scale in high fire disturbance has not been well documented and understood. The objective of this paper was to evaluate remote sensing driven 3-PG model for simulating the forest recovery in high-intensity burnt areas. Differences in productivity among sites were accounted for by only changing the values of fertility rating and climate condition which calculated from remote sensing data. The adapted model performed well in the experimental plots, and accurately estimated above ground biomass and diameter growth. Then, a case study was conducted on the 2001 burnt area, and NDVI images simulation used for validating the effectiveness of the model in a large scale area.

建立合成孔径雷达(synthetic aperture Radar,SAR)数据和光学植被指数的定量关系有助于融合这两种数据源,提高山区森林遥感的时序监测能力.为此,以内蒙古大兴安岭根河林区为例,首先分析了归一化植被指数(nor-malized difference vegetation index,NDVI)、增强型植被指数(enhanced vegetation index,EVI)、绿度植被指数(green-ness vegetation index,GVI)和归一化水分指数(normalized difference water index,NDWI)与C波段雷达数据的相关性,接着对比了不同森林干扰下NDVI,NDWI与X,C,L波段雷达数据的相关性差异.结果表明:①极化比(polariza-tion ratio,PR)和干涉相干系数与各植被指数呈显著负相关,PR与NDVI,EVI,GVI线性趋势好(R2=0.40～0.49),VH的干涉相干系数与各植被指数线性趋势好(R2=0.43～0.51);②地表类型会影响VH与NDVI线性回归结果,在植被密集的灌草、火烧迹地和森林内线性趋势好(R2=0.64～0.76);③不同森林干扰下相关性存在差异:在火烧迹地内NDVI与X波段HH和C波段PR呈显著负相关,NDWI与C波段VH呈显著正相关;在未受干扰林地内NDVI和NDWI与C波段PR呈显著正相关;在采伐迹地内L波段PR与NDVI呈显著负相关,L波段VV和VH的PR与NDWI呈显著负相关.

针对湿地植被存在典型的季节及年际变化特征,常用的遥感识别手段无法对湿地火烧严重程度实现准确评价的问题,提出了一种适用于湿地火烧严重程度的评价方法.基于2001年9月扎龙湿地的火灾事件,应用K-means聚类分析,从季节与年际2个方面的NBR阈值中获取不同火烧严重程度的训练样本,并利用随机森林机器学习算法建立基于光谱指数的分类模型,从而实现了湿地火烧严重程度的准确制图与评价.结果表明,交叉验证的分类总体精度为89.9％,各个火烧严重程度之间未出现严重的混分情况,且该模型具有一定的可移植性,能够成功地用于湿地火灾研究,从而为湿地火灾管理提供相应的参考依据.

Fire is a major disturbance factor in Daxing'anling region, with important impacts on carbon balance of forest ecosystems. Fire severity and the distinction of microclimates induced by different topography are the primary factors driving the restoration of post-fire net primary productivity (NPP). In this study, we examined the influence of fire severity and topographic factors on the restoration of forest NPP in the Genhe forest region. The spatial and temporal restoration process of post-fire NPP were simulated by combining with MTCLIM and 3PGS model based on multiyear Landsat TM satellite (2008-2012) and climate (1980-2010) data. The results showed that the 3PGS-MTCLIM model could precisely estimate the spatial distribution of NPP at small scales, with a good correlation between simulated and observed values (R2=0.828). The percentage of declined NPP in the year following the fire ranged 43%-80%, and the average NPP recovery period for this region was about 10 years by comparing pre- and post-fire NPP. Fire severity had significant impacts on post-fire recovery. The stronger the fire intensity, the longer the recovery period was needed. The NPP recovered relatively slower after a period of fast-speed recovery. Among the three topographic factors, elevation was the strongest one affecting forest NPP restoration, followed by slope and aspect.