Driving Factors and Spatial Distribution of Aboveground Biomass in the Managed Forest in the Terai Region of Nepal

Forests play a crucial role in absorbing atmospheric carbon dioxide, acting as a reservoir that helps counterbalance human-caused greenhouse gas emissions to mitigate climate change impacts [1,2,3]. Carbon storage in forests represents the largest portion, accounting for 82.5% of the total carbon stored in terrestrial vegetation. This significant carbon reservoir plays a vital role in acting as the primary component of the vegetation carbon sink [4,5]. Tropical forests store about 55% of the total carbon in forests and contribute to 70% of the global forest carbon sink [3,6]. Deforestation and forest degradation can lead to carbon emissions entering the atmosphere, affecting global climate and environmental change [7,8,9,10]. Despite the critical role of forests in mitigating climate change through carbon sequestration, there is a significant challenge in accurately estimating the forest biomass and understanding the factors influencing its dynamics. The current concerns about global change and the functioning of ecosystems require accurate forest biomass estimates and an examination of its dynamics [11].

In terrestrial forest ecosystems, the above-ground biomass (AGB) of trees serves as the most crucial and prominent carbon reserve [12,13]. Though field measurements offer precise data on AGB estimation, the sampling process can be constrained by challenging terrain or limited resources. In recent years, remote sensing (RS) technology has emerged as the most preferred method, enabling researchers to obtain a broad-scale, real-time overview of vegetation conditions. This advancement has provided a valuable tool for studying and monitoring vegetation on a large scale [14,15]. Integrating remote sensing data with forest inventory data has evolved into a potent technique for accurately estimating AGB in forest stands [16,17]. Based on remote sensors’ information and allometric equations, the predicted AGB has been calibrated and validated with ground truth to develop biomass estimation models [18]. Remote sensing data, such as light detection and ranging (LiDAR) data, proves advantageous in assessing forest characteristics like tree height, which directly correlates with forest biomass [17,19]. Over the past few years, airborne laser scanning (ALS), alternatively referred to as light detection and ranging (LiDAR), has emerged as the prevailing technology for acquiring precise topographic information, and it has been extensively applied in vegetation mapping and forest inventory, respectively [17,20,21]. ALS data captures the horizontal and vertical distribution of the forest canopies and does not saturate the spectral response of dense canopies, in contrast to multispectral imagery or aerial photography [22]. This advancement of RS technology, integrated with intensive site-based inventory methods, has also played a crucial role in monitoring and managing forests, particularly in initiatives like REDD+ (reducing emissions from deforestation and forest degradation) [23].

In tropical and subtropical forests, carbon stocks are declining at a rate of 1–2 billion tons per year [24] and are primarily affected by different drivers, such as the forest management regime and natural disturbance [25,26,27,28], the species composition of forests and forest type [29], and stand age structure [30,31]. The accumulation of AGB and its distribution in forested ecosystems are also significantly influenced by climate [32,33], as well as soil characteristics and topography [34,35]. Climatic data plays a significant role in understanding how temperature and precipitation influence tree growth [36], resulting in variation in AGB accumulation [37]. Moreover, the variation in AGB of forest stands is triggered by changes in land use and land cover because of human-induced activities [38]. Variations in soil properties and nutrient availability to trees also offer valuable insights into AGB dynamics [39]. AGB of trees is also influenced by variations in water availability, tree cover [40], and altitude [41,42,43]. In a broader context, the ALS-generated AGB maps can be combined with various geospatial data, including climate data, soil attributes, vegetation types, and land use patterns, to investigate the relationships between these factors and the AGB distribution. In the present research, we used the random forest (RF) model to analyze and describe the spatial distribution of AGB in managed forests in Nepal. The RF model is a machine learning algorithm capable of handling complex datasets and identifying important predictors of AGB distribution [44].

The forest of Nepal is categorized based on its own protected compasses, “private forest” and “national forest”, with the latter further classified into five types: government-managed forest, community forest, leasehold forest, religious forest, and protection forest. Managed forests, such as community, leasehold, and religious forests, are crucial in promoting sustainable resource utilization and supporting local livelihoods. Protected forests contribute significantly to biodiversity conservation and are crucial ecological habitats [45]. Nepal covers about 23.39% of its land area as protected areas, aiming to conserve biodiversity and maintain terrestrial carbon stocks. Forests, which cover approximately 45.3% of Nepal’s total land area [45], serve a significant amount of AGB and store about 1055 million tons of atmospheric carbon [45]. Nepal has over 22,000 community forest groups (CFs), representing 3 million households nationwide. These groups manage over 2.4 million hectares of forests, equivalent to about one-third of Nepal’s forest cover (https://mofe.gov.np/, accessed on 9 September 2022). These forests play a crucial role in sequestering carbon and mitigating potential greenhouse gas emissions in the region through their biomass.

While ALS has been increasingly used for estimating and mapping AGB in Nepal [46,47,48] to support the REDD+ implementation, there is limited information about the spatial distribution of AGB across different forest types and management regimes. The underlying factors that influence AGB, particularly in managed forests, are not well understood. LiDAR technology has the capability to capture detailed vegetation structure and topography at high resolutions [49] to provide reliable estimates of AGB and forest carbon stock at the landscape level [50,51,52]. Combined with ancillary data sources, LiDAR can offer valuable insights into the spatial variation of AGB estimates and understand the factors that control it [53]. Therefore, the study aimed to estimate aboveground biomass (AGB) and map its spatial pattern in the managed forest of Nepal, specifically focusing on the Sagarnath Forest Development Project. The study also sought to investigate the influence of climatic and topographic variables on AGB spatial distribution and identify the main driving factors. The study focuses on the following questions: (1) What are the distribution patterns of forest AGB within the study area? (2) What are the determinants of forest AGB in the study area? How do topography, climate, and soil factors influence AGB levels in the forests? Understanding the determinants of forest AGB in study sites is crucial for improving forest carbon management practices and accurately estimating carbon storage. By establishing relationships between AGB and environmental factors, such as topography, climate, and soil characteristics, the study enhances our understanding of how these factors impact AGB dynamics in forest ecosystems.

In our study, the random forest (RF) model was used to estimate and understand the variability and spatial distribution of AGB in the managed forest. Powell et al. [41] highlighted the RF model’s effectiveness, surpassing the performance of multiple linear regression. The application of the RF model not only provided estimates for predictor variables but also allowed for an assessment of their relative importance and the visualization of non-linear relationships through partial dependence plots (Figure 7). The RF model is capable of modeling non-linear relationships without requiring explicit assumptions about the functional form of the relationship and has been widely employed in forest AGB estimation [18]. The predicted AGB in the study varied from 0 to 446 ton/ha with a mean of 120 ton/ha, which closely aligned with the mean AGB of the field plots (Figure 4). However, the average AGB (120 ton/ha) of trees was lower than the AGB (190 ton/ha) estimated in the forest of the Terai region of Nepal [45]. This difference in estimates can be because the samples cover the entire Terai region and possibly a more mature forest with a more diverse species composition compared to our study site. Moreover, this study explained the spatial distribution of AGB using the AGB map and all the explanatory variables (Figure 4 and Figure 5). The spatial distribution of AGB values in the study area showed higher values in the northeast and southwest regions, gradually decreasing towards the northwest. The study found that the factors influencing the spatial pattern of AGB were not uniform throughout the entire study area. The variables such as land use land cover (LULC), precipitation, temperature, and elevation were identified as having higher relative importance percentages in explaining AGB patterns. Conversely, variables like slope and aspect had a lesser influence on AGB variation (Figure 6). The main factors influencing the variability in AGB distribution were found to be land use land cover, MAP, and MAT, collectively explaining 64% of the variability in AGB patterns (refer to Figure 6). The vegetation density, water availability, and temperature conditions emerge as essential factors significantly influencing AGB levels across our study area.

Past studies have highlighted the influence of various factors such as topography, species composition, climate, elevation, and soil fertility on the spatial distribution of aboveground biomass (AGB) at the regional scale [72,73,74,75,76]. In our study area, while considering land use land cover, the AGB increased with a higher percentage of land use land cover in managed forests, especially with trees. The increase in the number of trees is a result of reforestation efforts, such as planting trees in the harvested area (logging) and sustainable forest management practices, including selective logging (thinning), proper harvesting methods, and ensuring natural regeneration. These practices have led to the growth of new trees and promoted the growth and sustainability of forests, resulting in higher AGB. With regard to climatic variables, precipitation, and temperature explained non-linear effects on AGB in the study site, respectively (Figure 7). Bowman et al. [77,78] study in Australian temperate and subtropical eucalyptus forests found that plants require temperatures that encourage growth while minimizing transpiration or autotrophic respiration. This indicates the importance of maintaining optimal temperature conditions for plants to maximize their growth potential. Lewis et al. [79] found an increase in AGB in African tropical forests with precipitation during the driest nine months of the year and a decrease during the wettest three months of the year. Malla et al. [71] reported a positive effect on AGB of the precipitation of the driest month and the maximum temperature of the warmest month in the forests throughout Nepal. The positive effect of precipitation during the driest month suggests that ensuring water availability during periods of rainfall can contribute to increased growth in the growing season [36], resulting in higher AGB. Similarly, the positive influence of maximum temperature during the warmest months indicates the importance of favorable temperature conditions for promoting forest growth and forest biomass accumulation. The different climatic conditions can affect the dynamics of AGB throughout the year. Previous studies, together with our results, show that precipitation and temperature can have both positive and negative effects on the AGB distribution in forests. However, other factors, such as soil characteristics, nutrient availability, disturbance regimes, and species composition, also interact with temperature and precipitation to influence AGB patterns.

When considering slope, Du et al. [80] indicated that vegetation on higher slopes tends to experience less human disturbance, allowing these areas to be better preserved, fostering abundant forest growth, and promoting biomass accumulation. In terms of aspect, studies conducted by Fan et al. [81,82] have demonstrated that the south-, southwest-, west-, and northwest-facing slopes are often referred to as sunny slopes. These aspects receive a greater amount of sunlight, leading to increased rates of photosynthesis and greater vegetation productivity. As a result, the amount of AGB in these aspects tends to be higher compared to other aspects. Regarding elevation, higher elevations are often associated with cooler temperatures and increased moisture availability [42]. These favorable conditions create an environment conducive to plant growth and the accumulation of biomass. Furthermore, elevated regions may exhibit distinct soil characteristics, nutrient availability, and vegetation compositions, which can contribute to increased AGB levels.

In our study area, the AGB was most abundant at the higher altitudes, particularly in areas dominated by soil type 2 (comprising Udorthents, Usotorthents, and Haplaquents). These regions are less conducive to agricultural activities and have limited accessibility via road networks. Previous studies have also indicated a positive relationship between altitude and AGB in similar areas [34,83]. Similarly, Nepal et al. [84] reported increasing AGB of trees with increasing elevation in the subtropical forest of Nepal. The elevation gradient is associated with changes in temperature, precipitation, and forest-type succession [85]. The elevation of our research site, typically ranging from 99 to 214 m above sea level, suggests that a significant climate change is unlikely to occur. Contrary to the findings of several studies [86,87,88,89,90] that indicate a decline in AGB with increasing elevation, we observe an opposing trend. This discrepancy can be attributed to the relatively narrow range of elevation (99 m to 214 m) encompassing the forested areas within our study site.

Regarding the road feature, the presence of a road has a negative impact on AGB up to a certain distance, potentially due to factors such as increased human disturbance and land conversion near roads. However, beyond a specific threshold distance, the negative effects diminish, or other factors such as reduced human activity or improved environmental conditions lead to an increase in AGB. The contribution of distance to the nearest road is consistent with [91], who observed lower AGB in the distance from the forests to the road up to 2000 m, while higher AGB was found for longer distances. AGB distribution is likely to be higher in areas with less human disturbance [92,93].

Soil properties play a significant role in influencing the AGB of tropical forests [39,79,94,95]. Various soil properties, such as pH, organic matter, total nitrogen, total phosphorus, and others, are analyzed to assess their impact. Within our study area, soil type 2 contributed more to AGB than soil type 4 (consisting of Haplaquents, Haplaqepts, and Eutrocrepts). The soil type 2 exhibits higher organic matter content, enhanced water-holding capacity, and improved nutrient availability [96], thereby fostering greater plant growth and biomass accumulation. Moreover, these soil types possess superior drainage and aeration properties, which facilitate root development and nutrient uptake. Conversely, soil type 4 exhibits lower organic matter content, diminished water retention capacity, and limited nutrient availability. These characteristics can impede plant growth and biomass production within these soil types. Our findings regarding the impact of soil on AGB align with previous studies. However, it is important to note that soil type alone may not be the sole determinant of AGB. Other factors, such as climate, topography, land use, and vegetation composition can also interact with soil type to influence AGB patterns. The complex interplay of these factors should be considered when understanding the dynamics of AGB in forest ecosystems.