scholarly journals UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates?

2018 ◽  
Author(s):  
Martin B. Bagaram ◽  
Diego Giuliarelli ◽  
Gherardo Chirici ◽  
Francesca Giannetti ◽  
Anna Barbati
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Forest Inventory ◽  
Forest Canopy ◽  
Mixed Forest ◽  
Canopy Gaps ◽  
Forest Biodiversity ◽  
Biodiversity Monitoring ◽  
Forest Types ◽  
Turkey Oak

Forest canopy gaps are important for the ecosystem dynamics. Depending on tree species, small canopy openings might be also associated to intra-crown porosity and to space between crowns. Yet, little is known on the relationships between the fine-scaled pattern of canopy openings and biodiversity features. This research explored the possibility of i)- mapping forest canopy gaps from a very high resolution orthomosaic (10 cm), processed from a versatile imaging platform such as unmanned aerial vehicles (UAV), ii)- to derive patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. This is attempted in a test area of 240 ha covered by temperate deciduous forest types in Central Italy and containing 50 forest inventory plots of about 530 m2. Correlation and linear regression techniques were used to explore relationships between patch metrics and understorey (density, development and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m2) can be faithfully extracted from UAV RGB imagery, using the red band and contrast split segmentation. Highest correlations were observed in the mixed forest (beech and turkey oak), while beech forest had the poorest ones and turkey oak forest displayed intermediate results. Moderate to strong linear relationships were found between gap metrics and understorey variables in mixed forest type, with adjusted R2 from linear regression ranging from 0.52 to 0.87. Equally good results, in the same forest types, were observed for forest habitat biodiversity variables (0.52<adjusted R2<0.79) with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.

scholarly journals Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 163 ◽  
Author(s):  
Yan Zhu ◽  
Zhongke Feng ◽  
Jing Lu ◽  
Jincheng Liu
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Mixed Forest ◽  
Accurate Estimation ◽  
Landsat 8 ◽  
Forest Types ◽  
Landsat 8 Oli ◽  
Inventory Data ◽  
Forest Inventory Data

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

scholarly journals The use of environmental DNA in monitoring aquatic biodiversity for conservation: a review of challenges and opportunities

2021 ◽  
Vol 4 ◽  
Author(s):  
PJ Stephenson
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Community Composition ◽  
Natural Resource ◽  
Environmental Dna ◽  
Camera Traps ◽  
Biodiversity Monitoring ◽  
Aquatic Biodiversity ◽  
Resource Managers ◽  
Global Biodiversity

Evidence-based decision-making in conservation and natural resource management is often constrained by lack of robust biodiversity data. Technology offers opportunities for enhanced data collection, with satellite-based remote sensing increasingly complemented by Earth-based sensors such as camera traps, acoustic recording devices and drones. In aquatic as well as terrestrial systems, environmental DNA is increasingly promoted as a tool to monitor species diversity and community composition. But if conservationists and natural resource managers are to know when to use eDNA, they need to understand its relative advantages and disadvantages, and when it can be used with or instead of other tools. In this paper, I expand on two recent publications (Stephenson 2020; Stephenson et al. 2020) to review lessons learned from the application of eDNA, especially metabarcoding, to the monitoring of aquatic biodiversity for conservation and to identify factors affecting its relevance and applicability. Over the past decade there have been many advances in technological solutions for biodiversity monitoring. eDNA and various remote sensing tools offer opportunities to create the enabling conditions for enhanced biodiversity monitoring, and are becoming cheaper and easier to use for scientists, public and private sector resource managers, and citizen scientists. Nonetheless, a number of challenges need to be addressed to, for example, improve the standardisation of tool use and to enhance capacity for the use, storage, sharing and analysis of huge volumes of data, especially in high-biodiversity countries. More studies comparing the relative efficiency and cost-effectiveness of different tools with different species in different habitats would help managers choose the right tools for their needs and capacity and better integrate them into monitoring schemes. eDNA is becoming the go-to option for the monitoring of aquatic species diversity and community composition and has also proven successful in some terrestrial settings. eDNA is especially useful for monitoring species that are in low densities or difficult to observe with traditional observer-based methods; indeed, several studies show eDNA metabarcoding techniques have a much better detection probability overall for taxa such as amphibians and fish. In some cases, eDNA has been shown to complement other tools when used together, by either increasing animal detection probabilities or increasing the number of indicators that can be measured at one site. This suggests that, in future, more effort should be made to test the effectiveness of integrating eDNA with one or more other tools to enhance the efficiency and effectiveness of measuring indicators and to increase the diversity of species detected. For example, eDNA could be combined with camera traps for monitoring vertebrates visiting waterholes. Testing multiple tools would also provide better opportunity to quantify when and how traditional observer-based methods can complement the technological solutions and when they are more cost-effective. However, it is noteworthy that, in general, the taxa for which data are most lacking, such as invertebrates, plants and fungi, are still those less easily monitored by eDNA and other new technologies. This suggests a focus only on technological solutions for biodiversity monitoring may perpetuate existing taxonomic data biases. I conclude by discussing the international policy context and the relevance of eDNA for monitoring global biodiversity indicators. Several opportunities exist to integrate eDNA into monitoring programmes to measure government, business and civil society contributions towards delivery of the post-2020 global biodiversity framework and the Sustainable Development Goals.

scholarly journals National Forest Inventory Contributions to Forest Biodiversity Monitoring

2012 ◽  
Vol 58 (3) ◽  
pp. 257-268 ◽  
Author(s):  
Gherardo Chirici ◽  
Ronald E. McRoberts ◽  
Susanne Winter ◽  
Roberta Bertini ◽  
Urs-Beat Brändli ◽  
...  
Keyword(s):  
Forest Inventory ◽  
National Forest Inventory ◽  
National Forest ◽  
Forest Biodiversity ◽  
Biodiversity Monitoring

scholarly journals Integrated Segmentation Approach with Machine Learning Classifier in Detecting and Mapping Post Selective Logging Impacts Using UAV Imagery

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Aisyah Marliza Muhmad Kamarulzaman ◽  
Wan Shafrina Wan Mohd Jaafar ◽  
Khairul Nizam Abdul Maulud ◽  
Siti Nor Maizah Saad ◽  
Hamdan Omar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forest Inventory ◽  
Template Matching ◽  
Forest Canopy ◽  
Environmental Changes ◽  
Canopy Gaps ◽  
Selective Logging ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Logging Impacts

Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification—support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices.

scholarly journals On the occurrence of bryophytes and macrolichens in different lowland rain forest types at Mabura Hill, Guyana

1990 ◽  
Vol 3 (1) ◽  
pp. 29-35
Author(s):  
J. H.C. Cornelissen ◽  
S. R. Gradstein
Keyword(s):  
Rain Forest ◽  
Forest Canopy ◽  
Mixed Forest ◽  
Evergreen Forest ◽  
Forest Types ◽  
Lowland Rain Forest ◽  
White Sand ◽  
Dry Evergreen Forest ◽  
Canopy Effect ◽  
Vertical Distributions

A floristic and ecological study of bryophytes and macrolichens in different lowland rain forest types around Mabura Hill, Guyana, South America, yielded 170 species: 52 mosses, 82 liverworts and 36 macrolichens. Lejeuneaceae account for about 30% of the species and are the dominant cryptogamic family of the lowland rain forest. Special attention was paid to the flora of the forest canopy, by using mountaineering techniques. It appeared that 50% of the bryophyte species and 86% of the macrolichens occurred exclusively in the canopy. Dry evergreen ‘walaba’ forest on white sand is particularly rich in lichens whereas the more humid ‘mixed’ forest on loamy soil is characterized by a rather rich liverwort flora. More species are exclusive to the mixed forest than to dry evergreen forest due to the ‘canopy effect’, i.e. the occurrence of xerophytic species in the outer canopy of both dry and humid forests. Furthermore, canopy species have wider vertical distributions on trees in the dry evergreen forest than in the mixed forest, due to the more open canopy foliage of the dry evergreen forest.

scholarly journals Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

2022 ◽  
Vol 14 (2) ◽  
pp. 364
Author(s):  
Zhilong Xi ◽  
Huadong Xu ◽  
Yanqiu Xing ◽  
Weishu Gong ◽  
Guizhen Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Gradient Boosting ◽  
Forest Types ◽  
Height Estimation ◽  
Sensing Data ◽  
Continuous Forest ◽  
Estimation Models

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

scholarly journals A Remote Sensing and GIS-Based Model of Habitat as a Predictor of Biodiversity

1994 ◽  
Vol 18 ◽  
pp. 3-16
Author(s):  
Diane Debinski ◽  
Kelly Kindscher
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Species Distribution ◽  
Animal Species ◽  
Habitat Type ◽  
Distribution Patterns ◽  
Remotely Sensed ◽  
Forest Types ◽  
Remotely Sensed Data ◽  
Habitat Specificity

Conservation biologists need better methods for predicting species diversity. This research investigated some new methods to analyze biodiversity patterns through the use of Geographic Information Systems and remote sensing technologies. We tested the correlation between remotely sensed habitat types and species distributions. The goal was not to do away with ground-based fieldwork, but rather to optimize and focus fieldwork by using GIS and remotely sensed data as tools for making the work more accurate and specific. Our research was conducted at a fine (30 x30 m) landscape scale using on-the ground locations of birds, butterflies, and plants in the northwest portion of the Greater Yellowstone Ecosystem. Three remotely sensed forest types (distinguished by species density and coverage) and six remotely sensed meadow types (ranging from xeric to hydric) were surveyed and coverage data were collected for grasses, shrubs, forbs and trees. Presence/absence data were collected for birds and butterflies. The objectives of this research were: 1) to determine the extent of the correlation between spectral reflectance patterns and plant or animal species distribution patterns, and 2) to test the spatial correspondence of species diversity "hotspots" among taxonomic groups. Field surveys in 1993 and 1994 validated the vegetation density, cover, and moisture gradients expected from satellite data interpretation. Both tree species composition and diameter at breast height were significant in discriminating among forest types. Twenty-two species of grasses and forbs were significant in distinguishing among meadow types. However, a smaller percentage of the animal species was significantly correlated with one habitat type. In order to find a strong correlation between species distribution patterns and remotely sensed data, a species must be moderately common and show some habitat specificity. Hotspots of species diversity coincided for shrubs, grasses, forbs, birds, and butterflies and were found in mesic meadows.

scholarly journals Insect diversity across an afro-tropical forest biodiversity hotspot

2021 ◽  
Vol 25 (2) ◽  
pp. 221-228
Author(s):  
Jan Christian Habel ◽  
Elisabeth Koc ◽  
Roland Gerstmeier ◽  
Axel Gruppe ◽  
Sebastian Seibold ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Forest Type ◽  
Mixed Forest ◽  
Biodiversity Hotspot ◽  
Eastern Africa ◽  
Insect Diversity ◽  
Forest Biodiversity ◽  
Forest Types ◽  
Relative Abundances ◽  
Taxonomic Groups

Abstract Tropical forests host a remarkable proportion of global arthropod diversity. Yet, arthropod communities living in tropical forests are still poorly studied, particularly for dry forests of Eastern Africa. The aim of this study was to analyse community structures, species richness and relative abundances of insects across a heterogeneous forest consisting of various forest types. We collected insects in the lower canopies with light traps across the Arabuko Sokoke forest, part of the East African coastal forest biodiversity hotspot in southeast Kenya. Sampling was conducted across three forest types and along the forest edge. In total we collected > 250,000 individuals. We grouped these individuals into orders, and beetles into (sub)families. Representatives of the taxonomically well-known beetle families Cerambycidae, Tenebrionidae and Scolytinae were further determined to species level. We subsequently classified these groups into guilds according to their ecological requirements and life-histories. Relative abundances of arthropods strongly differed among taxonomic groups and forest types. Evenness was highest in the heterogeneous natural Brachystegia forest type. The mixed forest type and the forest edges showed intermediate degrees of evenness, while the structurally homogenous Cynometra forest showed comparatively low degrees of evenness. Implications for insect conservation We found that taxonomic and guild compositions strongly differed among the forest types. Our findings reveal that structural heterogeneity of a forest is the major driver of insect diversity, community composition, and relative abundance. Our study underlines that the preservation of all three forest types is crucial to maintain the complete diversity of arthropods across all taxonomic groups.

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