Effects Of Topographic Conditions Research Articles

Quantifying the Spatiotemporal Variation of NPP of Different Land Cover Types and the Contribution of Its Associated Factors in the Songnen Plain

Net primary productivity (NPP) of vegetation is considered an important indicator for ecological stability and is the main object for analyzing the factors influencing the terrestrial carbon cycle. Recent studies have made clear the changes in the NPP of vegetation and its influencing factors at various scales. However, the variations in NPP based on different land cover types under various natural conditions, along with their driving factors, remain not well understood. In this study, spatial overlay analysis was used to investigate the link among climatic, soil moisture (SM), and topographic parameters and NPP of various land cover types after analyzing the spatial and temporal trends of NPP in the Songnen Plain from 2001 to 2020. Additionally, the contribution of each influence factor to the NPP of different land cover types was calculated using the elastic net regression model. The elastic net regression model eliminates the multicollinearity among the influencing factors while maintaining the model stability, and the R2 of all lands is greater than 0.62, which can effectively quantify the contribution of each influencing factor to NPP. The results show a continuously increasing trend of the overall NPP in the research area over the selected 20 years, and NPP increased most significantly in forest land (FOR). Precipitation (PRE) and NPP showed high correlations in all the different land cover types, while the correlations between NPP and other influencing factors were significantly different. In addition, we found that perennials led to a more significant degree of NPP enhancement, and the effect of topographic conditions on NPP was mainly reflected in differences in moisture conditions due to surface runoff. From the results of the modeling calculations, the cumulative contribution of PRE to NPP ranks first in all land types and is the most vital influencing factor of NPP in the Songnen Plain. SM was an important influence, but the contribution of NPP was greater in land classes with shallow root systems. The results of the study revealed the positive transformation relationship of NPP among land cover types in ecologically fragile areas, which provides a reference for ecological restoration and rationalization of land use structure in zones such as intertwined agricultural and pastoral zones.

Age and size outperform topographic effects on growth-climate responses of trees in two Central European coniferous forest types

Trees do not respond to climatic conditions uniformly, but instead show individualistic growth responses. The extent of and causes behind this within-stand variability represents significant uncertainty in predictions of how forests will respond to future climate change. We analyzed patterns of individualistic tree growth within two types of conifer stands of Central Europe – high-elevation Picea abies and low-elevation Pinus sylvestris forests. We quantified the relative effect of age, size, and topographic conditions on variability in growth patterns and climate-growth responses using principal component analysis and linear models, considering both year-to-year and decadal growth variability. Our results show that Picea abies stands with dominant temperature limitation exhibit greater growth coherency than Pinus sylvestris stands characterized by drought-limited growth. Growth variability and individual climate-growth responses in both forest types were mainly driven by tree size and age, while the effect of topographic conditions was less important. The effect of size and age variables was dominant considering decadal growth trends, whereas intermediate importance of topographic variables was observed for high-frequency growth variability and climate-growth responses. Our results highlight that between-tree variability in climatic signal and growth trends also reflects the specific distribution of the age/size and topographic parameters within the stand. We suggest careful selection of datasets used for large-scale assessments of growth trends and climate-growth responses which should consider the age and size representation of sampled trees.

Integrating lidar with Landsat data for subalpine temperate forest aboveground carbon estimation

Accurate forest carbon accounting forms a basis for promoting the development of ecosystem service markets including forest carbon sinks. However, carbon assessments over large forest areas are challenging. Difficulties are compounded by the lack of adequate field observations especially in mountainous regions. In this study, we describe the development of a two-phase sampling framework to evaluate regional aboveground carbon density (ACD) of subalpine temperate forests in northwestern China that includes integrating ground plots, airborne lidar metrics, and Landsat images. During the first phase, an accurate, lidar-derived, ACD inventory network of a representative forested zone (Dayekou Basin) was established on the basis of a modified allometric model by adding crown coverage (CC) as a supplementary variable; cross-validated R2 was 0.88 and root mean square error (RMSE) was 14.7 Mg C ha−1. The outcomes of this step enabled the extension of quasi-field plots required for the representative carbon evaluations and the amplification of the range of observed values. Further integration of lidar measures and optical Landsat data by using the partial least squares regression (PLSR) method was conducted in the subsequent phase. The final model developed for broad-scale estimates explained 76% of the variance in forest ACD and produced a mean bias error of 27.9 Mg C ha−1. Aboveground carbon stocks for the whole ecoregion averaged 77.2 Mg ha−1, which generated an uncertainty of 13%. Visual patterns revealed a systematic overestimation for low ACD values and an underestimation in those regions with high carbon density. Potential errors in our carbon estimates could be associated with the saturation of optical signals, accuracy of land-cover map, and effects of topographic conditions. Overall, the double-sampling method demonstrated promising means for carbon accounting over large areas in a spatially-explicit manner and provided a good first approximation of carbon quantities for the forests in the ecoregion. Our study illustrated the potential for the use of lidar sampling in facilitating scaling of field surveys to a larger spatial extent than ground-based practices by supplying accurate biophysical measurements (e.g. heights).

Effect of Topographic Conditions on Grass Growth and Water Stress under Glasshouse Conditions

Effect of Topographic Conditions on Grass Growth and Water Stress under Glasshouse Conditions