Changes in plant debris and carbon stocks across a subalpine forest successional series

Background As a structurally and functionally important component in forest ecosystems, plant debris plays a crucial role in the global carbon cycle. Although it is well known that plant debris stocks vary greatly with tree species composition, forest type, forest origin, and stand age, simultaneous investigation on the changes in woody and non-woody debris biomass and their carbon stock with forest succession has not been reported. Therefore, woody and non-woody debris and carbon stocks were investigated across a subalpine forest successional gradient in Wanglang National Nature Reserve on the eastern Qinghai-Tibet Plateau. Results Plant debris ranged from 25.19 to 82.89 Mg∙ha− 1 and showed a global increasing tendency across the subalpine forest successional series except for decreasing at the S4 successional stage. Accordingly, the ratios of woody to non-woody debris stocks ranged from 26.58 to 208.89, and the highest and lowest ratios of woody to non-woody debris stocks were respectively observed in mid-successional coniferous forest and shrub forest, implying that woody debris dominates the plant debris. In particular, the ratios of coarse to fine woody debris stocks varied greatly with the successional stage, and the highest and lowest ratios were found in later and earlier successional subalpine forests, respectively. Furthermore, the woody debris stock varied greatly with diameter size, and larger diameter woody debris dominated the plant debris. Correspondingly, the carbon stock of plant debris ranged from 10.30 to 38.87 Mg∙ha− 1 across the successional series, and the highest and lowest values were observed in the mid-coniferous stage and shrub forest stage, respectively. Most importantly, the carbon stored in coarse woody debris in later successional forests was four times higher than in earlier successional forests. Conclusions The stock and role of woody debris, particularly coarse woody debris, varied greatly with the forest successional stage and dominated the carbon cycle in the subalpine forest ecosystem. Thus, preserving coarse woody debris is a critical strategy for sustainable forest management.

Wide-area shrub forest map based on multi-sensor data and active learning

<p>In the past few decades, the occurrence of shrub forest dominated by the two species Green alder (Alnus viridis) and Dwarf mountain pine (Pinus mugo) has increased in the Swiss Alps. Up-to-date and area-wide information on its distribution is required for countrywide forest reporting (5 % of Swiss forest consists of shrub forest) and of great interest to the forestry sector. Such information helps to better understand forest succession and supports the evaluation and management of protection forests. Until now, this information has been based on estimates from the Swiss National Forest Inventory (NFI). Due to their sampling scheme that uses a regular grid, these data are not area-wide maps. However, new developments in remote sensing techniques in combination with high spatial and temporal resolution data have facilitated the production of maps over large areas, e.g. the whole of Switzerland (41’285 km<sup>2</sup>).</p><p>To map the shrub forest areas, we developed an approach that uses a Random Forest (RF) model, active learning techniques and data from multiple remote sensing sources. The training data was produced via aerial image interpretation of areas covered by shrub forest. We used predictor data from different sensors and technologies, complementing each other by their diverse sensitivity to properties of shrub forests. These data included airborne Digital Terrain (DTM) and Vegetation Height Models (VHM), and spaceborne Synthetic Aperture Radar (SAR) backscatter from the Sentinel-1 constellation and multispectral imagery from Sentinel-2. To improve mapping quality, an iterative and semi-automatic active learning technique was used to generate further training data.</p><p>The above outlined workflow enabled the production of a shrub forest map for the whole of Switzerland with a spatial resolution of 10 m. An accuracy assessment was performed using independent validation data of a total of 7’640 regularly distributed NFI plots. Mean shrub forest cover per plot (50 m x 50 m) was slightly underestimated by 1.5 % with a root mean square error of 10 %. The influence of the active learning was observed and revealed higher accuracies after each additional iteration of training data production. The proposed approach underscores the potential of multi-sensor data combined with active learning techniques to provide cost-effective and area-wide information on the occurrence of shrub forest in a manner complementary to the NFI measurements.</p>

Grossflächige Klassifikation von Gebüschwald mit Fernerkundungsdaten

Large-scale classification of shrub forest with remote sensing data Information on shrub forest distribution and development is important for a range of forestry- and ecologically-related questions, but current and area-wide datasets have been characterized by limited availability. In this study, the mapping of shrub forests dominated by green alder, mountain pine and hazel for the canton of Grison was investigated, based on available nationwide remote sensing data. Satellite data from Sentinel-1 and Sentinel-2, as well as a vegetation height and an elevation model were used. Training areas provided by the canton and supplemented by aerial imagery interpretation were used for a supervised classification with Random Forest, a decision tree-based machine learning algorithm. Independent validation of the results was carried out with data from the National Forest Inventory (NFI). Green alder and mountain pine forests were classified with high accuracy of 92.1% respectively 86.7%, whereas for hazel shrub forests, the internal model accuracy was only 66.7%. The resulting area expansion of the shrub forest was comparable with findings based on the NFI. A direct comparison with the NFI aerial imagery interpretation points revealed major discrepancies. The main reason for this is the different degree of spatial detail. However, NFI areas with a high percentage of shrubs were reliably classified as shrub forest. The method presented here underscores the potential of remote sensing data available throughout Switzerland for an essentially objective, costefficient and large-scale mapping of shrub forests with an accuracy applicable in practice.

Characteristics of rainfall and runoff in different extreme precipitation events in the Beijing mountain area

This study was based on a rainstorm that happened in Beijing on 20 July 2016. We analyzed the characteristics of rainfall and runoff during this rainstorm, compared it to rainstorm 721, and investigated why no surface runoff was observed during this rainstorm. A runoff plot experiment showed that almost all runoff consisted of deep interflow (40–60 cm). For runoff plots with identical vegetation, the slope was smaller, and the lag time of the deep interflow relative to the process of rainfall was shorter. The runoff yield of the deep interflow was inversely proportional to the slope. Compared to plots with pure tree forest and shrub forest, the interflow process curve of plots with coniferous and broad-leaved mixed forest was relatively gentle during the rainfall process. Thick litter layers, low antecedent moisture content of the soil, high gravel content of the soil, and the short duration of high intensity rainfall are the causes for the observed lack of surface runoff. To simultaneously prevent flooding and waterlogging, we propose to utilize vegetation to improve water storage at the reservoirs and to replenish the groundwater during cumulative rainstorms with a stable rain tendency.

Woody Shrubs as a Barrier to Invasion by Cogongrass (Imperata cylindrica)

Cogongrass invades forests through rhizomatous growth and wind-dispersed seeds. Increased density and abundance of woody vegetation along forest edges may strengthen biotic resistance to invasion by creating a vegetative barrier to dispersal, growth, or establishment of cogongrass. We evaluated differences in dispersal of cogongrass spikelets experimentally released from road edges into tallgrass-dominated and shrub-encroached longleaf pine forests (Pinus palustris). Average maximum dispersal distances were greater in the pine–tallgrass forest (17.3 m) compared to the pine–shrub forest association (9.4 m). Spikelets were more likely to be intercepted by vegetation in pine–shrub forests compared to pine–tallgrass forests. Results suggest that dense woody vegetation along forest edges will slow spread from wind-dispersed cogongrass seeds.

Modelle zur Biomasse- und Holzvolumenschätzung im Schweizer Gebüschwald | Modelling of biomass and wood volume in Swiss shrub forest

The results of the third National Forest Inventory indicate that shrub forest accounts for 5% of the total forest area in Switzerland. It grows almost exclusively in the subalpine zone and is dominated by the woody species Alnus viridis and Pinus mugo prostrata. As a consequence of global warming and the increasing demand for sustainable energy, there is a national and international interest to quantify wood volume and biomass in shrub forests. Therefore representative coppice shoots were measured in detail to establish allometric volume and biomass functions for Alnus viridis, Pinus mugo prostrata and Salix sp. For each coppice shoot the wood volume, the wet weight, the dry weight and the carbon mass was determined as a function of the base diameter. In the next step all coppice shoots of 49 sample plot areas were measured. A regression analysis shows the relationship between the degree of cover and wood volume, and hence the biomass, in a shrub forest stand. Shrub forest stands which consist of Alnus viridis and which have a degree of cover of 100% contain on average 74 m3/ha of above-ground wood volume. More than twice of that volume, namely 166 m3/ha, is contained in corresponding Pinus mugo prostrata shrub forest stands. In Swiss shrub forests, the average aboveground wood volume amounts to 66 m3/ha, corresponding to around 2 million tons of biomass. Based on the presented volume functions it will be possible to estimate the aboveground wood volume and biomass of shrub forest stands dominated by Alnus viridis and Pinus mugo prostrata with good results. Input variable is the degree of cover of the present woody species, which can be determined efficiently on aerial photography by image interpretation.

Changes in trophic abundance of soil arthropods along a grass-shrub-forest gradient

Ecological theory suggests that along productivity gradients, abundances of organisms within trophic levels will increase in a stepwise pattern from producers to consumers. To test this theory I investigated changes in abundance of soil arthropods at three trophic levels: microphytophages, represented by Collembola, predacious mites (Acari) that feed on Collembola, and three groups of macroarthropods (spiders, ants, and centipedes) that were observed to feed on mites. Changes in abundance were monitored along a gradient in vegetation structure from grass to shrub to forest in the Canadian prairies. I controlled for temporal variation in abundance among years and surveys within a year. As predicted, (i) numbers of Collembola did not change with increases in productivity; (ii) mite numbers were greatest in the shrublands; and (iii) numbers of macroarthropod predators increased from grassland to shrubland, and there was a nonsignificant increase in numbers of spiders and centipedes in forest habitat. Contrary to predictions, macroarthropod numbers were not significantly greater in forest habitat, and ant numbers actually declined. Possible explanations for the lack of increase in macroarthropod predator abundance in the forest habitat with the greatest productivity include decreased ground-level humidity and greater abundance of macroarthropod predators and parasites in forest environments.