Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R²) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management.

Introduction: This study delves into the spatiotemporal dynamics of land use and land cover (LULC) in a Metropolitan area over three decades (1991–2021) and extends its scope to forecast future scenarios from 2031 to 2051. The intent is to aid sustainable land management and urban planning by enabling precise predictions of urban growth, leveraging the integration of remote sensing, GIS data, and observations from Landsat satellites 5, 7, and 8.Methods: The research employed a machine learning-based approach, specifically utilizing the random forest (RF) algorithm, for LULC classification. Advanced modeling techniques, including CA–Markov chains and the Land Change Modeler (LCM), were harnessed to project future LULC alterations, which facilitated the development of transition probability matrices among different LULC classes.Results: The investigation uncovered significant shifts in LULC, influenced largely by socio-economic factors. Notably, vegetation cover decreased substantially from 49.21% to 25.81%, while forest cover saw an increase from 31.89% to 40.05%. Urban areas expanded significantly, from 7.55% to 25.59% of the total area, translating into an increase from 76.31 km² in 1991 to 258.61 km² in 2021. Forest area also expanded from 322.25 km² to 409.21 km². Projections indicate a further decline in vegetation cover and an increase in built-up areas to 371.44 km² by 2051, with a decrease in forest cover compared to its 2021 levels. The predictive accuracy of the model was confirmed with an overall accuracy exceeding 90% and a kappa coefficient around 0.88.Discussion: The findings underscore the model’s reliability and provide a significant theoretical framework that integrates socio-economic development with environmental conservation. The results emphasize the need for a balanced approach towards urban growth in the Islamabad metropolitan area, underlining the essential equilibrium between development and conservation for future urban planning and management. This study underscores the importance of using advanced predictive models in guiding sustainable urban development strategies.

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination ( R a d j 2 ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( R a d j 2 > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

In the context of uneven-aged mixed-species forest management, an individual-tree basal area increment model considering forest structural diversity was developed for oaks (Quercus spp.) using data collected from 11,860 observations in 845 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, south-central China. Since the data was longitudinal and had a nested structure, we used a linear mixed-effects approach to construct the model. We also used the variance function and an autocorrelation structure to describe within-plot heteroscedasticity and autocorrelation. Finally, the optimal mixed-effects model was determined based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik) and the likelihood ratio test (LRT). The results indicate that the reciprocal transformation of initial diameter at breast height (1/DBH), relative density index (RD), number of trees per hectare (NT), elevation (EL) and Gini coefficient (GC) had a significant impact on the individual-tree basal area increment. In comparison to the basic model developed using least absolute shrinkage and selection operator (LASSO) regression, the mixed-effects model performance was greatly improved. In addition, we observed that the heteroscedasticity was successfully removed by the exponent function and autocorrelation was significantly corrected by AR(1). Our final model also indicated that forest structural diversity significantly affected tree growth and hence should not be neglected. We hope that our final model will contribute to the scientific management of oak-dominated forests.

We transformed a Pinus massoniana plantation, the most important conifer plantation in southern China, with four different transformation treatments, in which Pinus massoniana was thinned to a density of 70%, and then differing richness and compositions of enrichment plantings were added. In order to examine the effects of the transformation, we compared species composition, stand structure and growth pattern in transformed stands with those in control stands. The results suggested that in the transformed stands species composition was diverse with trees both from the enrichment plantings and from natural recruitment. The size structure was changed such that the diameter at breast height (DBH) distribution tended to shift from a nearly normal distribution to an irregular multi-modal distribution. Substantial new ingrowth was found in the small DBH classes. The residual trees in the transformed stands were significantly larger than in the control treatment. However, for all trees, the control stands had the largest mean size, even though the residual tree growth was significantly smaller in the control stands. Finally, transformation treatment A4, which had the smallest overall mortality rate and simultaneously the mortality rate of each tree species was smaller than the corresponding value in other transformation treatments, was identified as the optimal transformation.

Subtropical natural forests are unique due to their ecological and economic functions. However, most of these forests are highly degraded, which impairs the ability to provide ecological and economic benefits. Enrichment planting is an important approach to restore natural degraded forests. Species arrangement is of great importance to inform enrichment planting. Species association refers to the interrelationship of different species occupying a habitat and is a static description of the organic connection formed by the interaction of species. Species association, therefore, provides a scientific basis for species arrangement in enrichment planting. Additionally, because an old-growth forest is a climax community that has attained great age without significant disturbance, the species association in an old-growth forest can provide valuable information on the reference conditions for forest management. In this study, the species association between dominant tree species (including saplings and adult trees) was investigated in an old-growth forest in the Gutianshan National Nature Reserve in Zhejiang province in subtropical China. The objective of the study was to inform species arrangement for enrichment planting. The result showed that the overall species association exhibited a significant net positive association, indicating a dynamic balance of stable structure and species composition in the old-growth forest. Additionally, the pairwise species association was examined using the χ² test, the Dice index, and Spearman’s rank correlation coefficient; significant positive and negative pairwise species associations were detected. Based on the species association and the light requirements of the tree species, an optimal species arrangement was determined to support enrichment planting for restoring natural degraded forests. It is expected that the results of this study will contribute to the restoration of natural degraded forests in subtropical China.