Season-dependence of remote sensing indicators of tree species diversity

2014 ◽  
Vol 5 (5) ◽  
pp. 404-412 ◽  
Author(s):  
Eduardo Eiji Maeda ◽  
Janne Heiskanen ◽  
Koen W. Thijs ◽  
Petri K.E. Pellikka
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Tree Species ◽  
Tree Species Diversity

scholarly journals Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1047 ◽  
Author(s):  
Ying Sun ◽  
Jianfeng Huang ◽  
Zurui Ao ◽  
Dazhao Lao ◽  
Qinchuan Xin
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Species Diversity ◽  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Diversity Index ◽  
Tree Species Diversity ◽  
Individual Tree ◽  
Rgb Images

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

scholarly journals Discerning Effects of Climate Variability, Local Land Degradation and Tree Species Diversity on Remote Sensing Derived Resilience and Resistance of Dry Afromontane Forest 

2020 ◽  
Author(s):  
Hadgu Hishe ◽  
Louis Oosterlynck ◽  
Kidane Giday ◽  
Wanda De Keersmaecker ◽  
Ben Somers ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Species Diversity ◽  
Tree Species ◽  
Tree Diversity ◽  
Tree Species Diversity ◽  
Factors Associated ◽  
Afromontane Forest ◽  
Growth Stability ◽  
Dry Afromontane Forest

Abstract Introduction: Anthropogenic disturbances are increasingly affecting the vitality of tropical dry forests. The future condition of this important biome will depend on its capability to resist, and recover from these disturbances. So far, the temporal stability of dryland forests is rarely studied, but could serve as a basis for forest management and restoration. Methodology: In a degraded dry Afromontane forest in northern Ethiopia, we explored remote sensing derived indicators of forest stability, using MODIS satellite derived NDVI time series from 2001 to 2018. Resilience, resistance and variability were measured using the anomalies (remainders) after time series decomposition into seasonality, trend and remainder components. Growth stability was calculated using the integral of the undecomposed NDVI data. These NDVI derived stability indicators were then related to environmental factors of climate, topography, soil, tree species diversity, and disturbance, obtained from a systematic grid of field inventory plots, using boosted regression trees in R. Resilience and resistance were adequately predicted by these factors with an R2 of 0.67 and 0.48, respectively, but the models for variability and growth stability were weaker. Precipitation of the wettest month, distance from settlements and slope were the most important factors associated with resilience, explaining 51% of the effect. Altitude, temperature seasonality and humus accumulation were the significant factors associated with the resistance of the forest, explaining 61% of the overall effect. A positive effect of tree diversity on resilience was also significant, except that the impact of species evenness declined above a threshold value of 0.70, indicating that perfect evenness reduced the resilience of the forest. Conclusion: A combination of climate, topographic variables and disturbance indicators controlled the stability of the dry forest. Tree diversity is an important component that should be considered in the management and restoration programs of such degraded forests. If local disturbances are alleviated the recovery time of dryland forests could be shortened, which is vital to maintain the ecosystem services these forests provide to local communities and global climate change.

scholarly journals Tree Species Diversity, Composition and Aboveground Biomass Across Dry Forest Land-Cover Types in Coastal Ecuador

2021 ◽  
Vol 14 ◽  
pp. 194008292199541
Author(s):  
Xavier Haro-Carrión ◽  
Bette Loiselle ◽  
Francis E. Putz
Keyword(s):  
Species Diversity ◽  
Land Cover ◽  
Community Composition ◽  
Aboveground Biomass ◽  
Tree Species ◽  
Dry Forest ◽  
Stem Density ◽  
Tree Species Diversity ◽  
Land Cover Types ◽  
Schizolobium Parahyba

Tropical dry forests (TDF) are highly threatened ecosystems that are often fragmented due to land-cover change. Using plot inventories, we analyzed tree species diversity, community composition and aboveground biomass patterns across mature (MF) and secondary forests of about 25 years since cattle ranching ceased (SF), 10–20-year-old plantations (PL), and pastures in a TDF landscape in Ecuador. Tree diversity was highest in MF followed by SF, pastures and PL, but many endemic and endangered species occurred in both MF and SF, which demonstrates the importance of SF for species conservation. Stem density was higher in PL, followed by SF, MF and pastures. Community composition differed between MF and SF due to the presence of different specialist species. Some SF specialists also occurred in pastures, and all species found in pastures were also recorded in SF indicating a resemblance between these two land-cover types even after 25 years of succession. Aboveground biomass was highest in MF, but SF and Tectona grandis PL exhibited similar numbers followed by Schizolobium parahyba PL, Ochroma pyramidale PL and pastures. These findings indicate that although species-poor, some PL equal or surpass SF in aboveground biomass, which highlights the critical importance of incorporating biodiversity, among other ecosystem services, to carbon sequestration initiatives. This research contributes to understanding biodiversity conservation across a mosaic of land-cover types in a TDF landscape.

Diversification of tree stands as a means to manage pests and diseases in boreal forests: myth or reality?

2006 ◽  
Vol 36 (2) ◽  
pp. 324-336 ◽  
Author(s):  
Julia Koricheva ◽  
Harri Vehviläinen ◽  
Janne Riihimäki ◽  
Kai Ruohomäki ◽  
Pekka Kaitaniemi ◽  
...  
Keyword(s):  
Species Diversity ◽  
Tree Species ◽  
Boreal Forests ◽  
Tree Mortality ◽  
Tree Species Diversity ◽  
Mixed Stands ◽  
Tree Species Composition ◽  
Pests And Diseases ◽  
Pest Outbreaks ◽  
Disease Epidemics

Pure forest stands are widely believed to be more prone to pest outbreaks and disease epidemics than mixed stands, leading to recommendations of using stand diversification as a means of controlling forest pests and pathogens. We review the existing evidence concerning the effects of stand tree-species diversity on pests and pathogens in forests of the boreal zone. Experimental data from published studies provide no overall support for the hypothesis that diversification of tree stands can prevent pest outbreaks and disease epidemics. Although beneficial effects of tree-species diversity on stand vulnerability are observed in some cases, in terms of reductions in damage, these effects are not consistent over time and space and seem to depend more on tree-species composition than on tree-species diversity per se. In addition, while mixed stands may reduce the densities of some specialized herbivores, they may be more attractive to generalist herbivores. Given that generalist mammalian herbivores cause considerable tree mortality during the early stages of stand establishment in boreal forests, the net effect of stand diversification on stand damage is unlikely to be positive.

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