scholarly journals Small but visible: Predicting rare bryophyte distribution and richness patterns using remote sensing-based ensembles of small models

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0260543
Author(s):  
Carlos Cerrejón ◽  
Osvaldo Valeria ◽  
Jesús Muñoz ◽  
Nicole J. Fenton
Keyword(s):  
Remote Sensing ◽  
Rare Species ◽  
Boreal Forests ◽  
Prediction Accuracy ◽  
Spatial Relationship ◽  
Development Planning ◽  
Conservation Strategies ◽  
Spatial Concordance ◽  
Richness Patterns ◽  
Small Models

In Canadian boreal forests, bryophytes represent an essential component of biodiversity and play a significant role in ecosystem functioning. Despite their ecological importance and sensitivity to disturbances, bryophytes are overlooked in conservation strategies due to knowledge gaps on their distribution, which is known as the Wallacean shortfall. Rare species deserve priority attention in conservation as they are at a high risk of extinction. This study aims to elaborate predictive models of rare bryophyte species in Canadian boreal forests using remote sensing-derived predictors in an Ensemble of Small Models (ESMs) framework. We hypothesize that high ESMs-based prediction accuracy can be achieved for rare bryophyte species despite their low number of occurrences. We also assess if there is a spatial correspondence between rare and overall bryophyte richness patterns. The study area is located in western Quebec and covers 72,292 km2. We selected 52 bryophyte species with <30 occurrences from a presence-only database (214 species, 389 plots in total). ESMs were built from Random Forest and Maxent techniques using remote sensing-derived predictors related to topography and vegetation. Lee’s L statistic was used to assess and map the spatial relationship between rare and overall bryophyte richness patterns. ESMs yielded poor to excellent prediction accuracy (AUC > 0.5) for 73% of the modeled species, with AUC values > 0.8 for 19 species, which confirmed our hypothesis. In fact, ESMs provided better predictions for the rarest bryophytes. Likewise, our study revealed a spatial concordance between rare and overall bryophyte richness patterns in different regions of the study area, which have important implications for conservation planning. This study demonstrates the potential of remote sensing for assessing and making predictions on inconspicuous and rare species across the landscape and lays the basis for the eventual inclusion of bryophytes into sustainable development planning.

scholarly journals The Modeling and Forecasting of Carabid Beetle Distribution in Northwestern China

Insects ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 168
Author(s):  
Xueqin Liu ◽  
Hui Wang ◽  
Dahan He ◽  
Xinpu Wang ◽  
Ming Bai
Keyword(s):  
Generalized Additive Models ◽  
Additive Models ◽  
Carabid Beetle ◽  
Conservation Strategies ◽  
Environmental Predictors ◽  
Temperate Steppe ◽  
Temperature And Precipitation ◽  
Steppe Ecosystems ◽  
Modeling And Forecasting ◽  
Richness Patterns

Beetles are key insect species in global biodiversity and play a significant role in steppe ecosystems. In the temperate steppe of China, the increasing degeneration of the grasslands threatens beetle species and their habitat. Using Generalized Additive Models (GAMs), we aimed to predict and map beetle richness patterns within the temperate steppe of Ningxia (China). We tested 19 environmental predictors including climate, topography, soil moisture and space as well as vegetation. Climatic variables (temperature, precipitation, soil temperature) consistently appeared among the most important predictors for beetle groups modeled. GAM generated predictive cartography for the study area. Our models explained a significant percentage of the variation in carabid beetle richness (79.8%), carabid beetle richness distribution seems to be mainly influenced by temperature and precipitation. The results have important implications for management and conservation strategies and also provides evidence for assessing and making predictions of beetle diversity across the steppe.

scholarly journals Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Lake Basin ◽  
Arid Areas ◽  
Annual Average ◽  
Machine Learning Methods ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska

2011 ◽  
Vol 37 (6) ◽  
pp. 596-611 ◽  
Author(s):  
Hans-Erik Andersen ◽  
Jacob Strunk ◽  
Hailemariam Temesgen ◽  
Donald Atwood ◽  
Ken Winterberger
Keyword(s):  
Remote Sensing ◽  
Boreal Forests ◽  
Forest Biomass ◽  
Interior Alaska ◽  
Biomass Resources

Detecting a keystone species European aspen in boreal forests with airborne hyperspectral, LiDAR and UAV data with machine learning methods

2021 ◽  
Author(s):  
Timo Kumpula ◽  
Janne Mäyrä ◽  
Anton Kuzmin ◽  
Arto Viinikka ◽  
Sonja Kivinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Boreal Forests ◽  
Tree Level ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
European Aspen

&lt;p&gt;Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km&lt;sup&gt;2&lt;/sup&gt; contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724&amp;#8211;727 nm) and shortwave infrared (1520&amp;#8211;1564 nm and 1684&amp;#8211;1706 nm) (Viinikka et al. 2020; M&amp;#228;yr&amp;#228; et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.&lt;/p&gt;

Between-taxon matching of common and rare species richness patterns

2015 ◽  
Vol 24 (12) ◽  
pp. 1476-1486 ◽  
Author(s):  
C. J. Reddin ◽  
J. H. Bothwell ◽  
J. J. Lennon
Keyword(s):  
Species Richness ◽  
Rare Species ◽  
Richness Patterns

scholarly journals Forest carbon management and carbon trading: A review of Canadian forest options for climate change mitigation

2011 ◽  
Vol 87 (05) ◽  
pp. 625-635 ◽  
Author(s):  
Denise Golden ◽  
M.A. Smith ◽  
Stephen Colombo
Keyword(s):  
Climate Change ◽  
Boreal Forests ◽  
Policy Implications ◽  
Conservation Strategies ◽  
Emissions Reduction ◽  
Carbon Trading ◽  
Management Objectives ◽  
Trade Offs ◽  
Mitigate Climate Change ◽  
Forest C

Forests have significant potential to mitigate climate change. Canada has 30% of the world's boreal forests. The ratification of the Kyoto Protocol commoditized carbon (C) on an international scale. To achieve Canada's emission reduction targets and mitigate climate change, the potential of forest C offset projects and forest C trading is being evaluated. Carbon trading and forest C management have economic and policy implications and potential trade-offs in other forest management objectives. We discuss how forest C management and trading can contribute to global efforts for atmospheric greenhouse gas emissions reduction through either utilization and/or conservation strategies.

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

2012 ◽  
Vol 118 ◽  
pp. 60-72 ◽  
Author(s):  
Chaoyang Wu ◽  
Jing M. Chen ◽  
Ankur R. Desai ◽  
David Y. Hollinger ◽  
M. Altaf Arain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
North America ◽  
Boreal Forests ◽  
Light Use Efficiency ◽  
Modis Imagery ◽  
Use Efficiency
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