Single sensor airborne data sources for forest inventories by tree species

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

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STUDIES OF DECAY IN RELATION TO TREE SPECIES, AGE, AND SITE AND THEIR APPLICATION IN FOREST INVENTORIES AND ESTABLISHMENT OF PRIORITY CUTTING SCHEDULES IN BRITISH COLUMBIA

The Forestry Chronicle ◽

10.5558/tfc32053-1 ◽

1956 ◽

Vol 32 (1) ◽

pp. 53-57 ◽

Cited By ~ 2

Author(s):

J. E. Browne

Keyword(s):

British Columbia ◽

Tree Species ◽

Forest Inventories

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The one hundred tree species prioritized for planting in the tropics and subtropics as indicated by database mining

10.5716/wp21001.pdf ◽

2021 ◽

Author(s):

Roeland Kindt ◽

◽

Ian K Dawson ◽

Jens-Peter B Lillesø ◽

Alice Muchugi ◽

...

Keyword(s):

Tree Species ◽

Global Analysis ◽

Tree Planting ◽

Data Sources ◽

Primary Concern ◽

Tropical Regions ◽

Starting Point ◽

Forest Reproductive Material ◽

Vote Count ◽

The One

A systematic approach to tree planting and management globally is hindered by the limited synthesis of information sources on tree uses and species priorities. To help address this, the authors ‘mined’ information from 23 online global and regional databases to assemble a list of the most frequent tree species deemed useful for planting according to database mentions, with a focus on tropical regions. Using a simple vote count approach for ranking species, we obtained a shortlist of 100 trees mentioned in at least 10 of our data sources (the ‘top-100’ species). A longer list of 830 trees that were mentioned at least five times was also compiled. Our ‘top-100’ list indicated that the family Fabaceae (syn. Leguminosae) was most common. The information associated with our mined data sources indicated that the ‘top-100’ list consisted of a complementary group of species of differing uses. These included the following: for wood (mostly for timber) and fuel production, human nutrition, animal fodder supply, and environmental service provision (varied services). Of these uses, wood was most frequently specified, with fuel and food use also highly important. Many of the ‘top-100’ species were assigned multiple uses. The majority of the ‘top-100’ species had weediness characteristics according to ‘attribute’ invasiveness databases that were also reviewed, thereby demonstrating potential environmental concerns associated with tree planting that need to be balanced against environmental and livelihood benefits. Less than half of the ‘top-100’ species were included in the OECD Scheme for the Certification of Forest Reproductive Material, thus supporting a view that lack of germplasm access is a common concern for trees. A comparison of the ‘top-100’ species with regionally-defined tree inventories indicated their diverse continental origins, as would be anticipated from a global analysis. However, compared to baseline expectations, some geographic regions were better represented than others. Our analysis assists in priority-setting for research and serves as a guide to practical tree planting initiatives. We stress that this ‘top-100’ list does not necessarily represent tree priorities for the future, but provides a starting point for also addressing representation gaps. Indeed, our primary concern going forward is with the latter.

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A Deep Fusion uNet for Mapping Forests at Tree Species Levels with Multi-Temporal High Spatial Resolution Satellite Imagery

Remote Sensing ◽

10.3390/rs13183613 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3613

Author(s):

Ying Guo ◽

Zengyuan Li ◽

Erxue Chen ◽

Xu Zhang ◽

Lei Zhao ◽

...

Keyword(s):

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Forest Type ◽

Species Level ◽

Forest Inventories ◽

Forest Classification ◽

Plantation Species ◽

Multi Temporal ◽

Artificial Intelligence Technology

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.

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Regression models of volume increment percentage in the Most represented stands of coniferous tree species in Serbia

Glasnik Sumarskog fakulteta ◽

10.2298/gsf0285025b ◽

2002 ◽

pp. 25-35 ◽

Cited By ~ 2

Author(s):

Stanisa Bankovic ◽

Milan Medarevic ◽

Damjan Pantic

Keyword(s):

Scots Pine ◽

Tree Species ◽

Forest Inventory ◽

Regression Models ◽

Diameter Increment ◽

Forest Inventories ◽

Volume Increment ◽

Large Scope ◽

Coniferous Tree Species

Considering the great significance of volume increment in forestry, it is understandable that there are numerous methods of its assessment. However, all these methods have some disadvantages, either the accuracy of the obtained results, too large scope of works of forest inventory (economicity), or the restriction only to stands of certain silvicultural type. To make the method of stand volume increment more economic and simplified, we defined regression models for volume increment percentage assessment in fir, spruce, Austrian pine and Scots pine stands in Serbia. Empirical data were fitted by four regression models for each tree species. The criteria for the final selection of models for the determination of volume increment percentage were the relevant statistic parameters of regression and correlation analysis, and the degree of concordance of "real" and fitted ("table") values of volume increment percentage. The selected models for the above tree species are Fir Spruce Austrian pine Scots pine In the practical work of the assessment of current volume increment in the stand, in regular forest inventories, the method of volume increment percentage should be implemented with correction factors for the fitting of "table" (obtained by this method) values of volume increment and "real" values (obtained by the method of diameter increment), on at least 10 % of the stands of the same or similar stand class (same or similar tree species and stand form). In this way, the costs of forest inventory would be reduced, and the obtained results would range within the limits of the required accuracy .

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THE OPPORTUNITIES AND CHALLENGES OF USING AIRBORNE LASER SCANNING FOR FOREST INVENTORIES IN LITHUANIA

Proccedings of International Scientific Conference "RURAL DEVELOPMENT 2017" ◽

10.15544/rd.2017.023 ◽

2018 ◽

Author(s):

Gintautas MOZGERIS ◽

Ina BIKUVIENĖ ◽

Donatas JONIKAVICIUS

Keyword(s):

Root Mean Square ◽

Tree Species ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Volume Estimation ◽

Target Point ◽

Mean Square ◽

Forest Inventories ◽

Airborne Laser ◽

Mean Square Errors

The aim of this study was to test the usability of airborne laser scanning (ALS) data for stand-wise forest inventories in Lithuania based on operational approaches from Nordic countries, taking into account Lithuanian forest conditions and requirements for stand-wise inventories, such as more complex forests, unified requirements for inventory of all forests, i.e. no matter the ownership, availability of supporting material from previous inventories and high accuracy requirements for total volume estimation. Test area in central part of Lithuania (area 2674 ha) was scanned using target point density 1 m-2 followed by measurements of 440 circular field plots (area 100–500 m2). Detailed information on 22 final felling areas with all trees callipered (total area 42.7 ha) was made available to represent forest at mature age. Updated information from conventional stand-wise inventory was made available for the whole study area, too. A two phase sampling with nonparametric Most Similar Neighbor estimator was used to predict point-wise forest characteristics. Total volume of the stand per 1 ha was predicted with an root mean square error of 18.6 %, basal area – 17.7 %, mean diameter – 13.6 %, mean height – 7.9 % and number of tree – 42.8 % at plot-level with practically no significant bias. However, the relative root mean square errors increased 2–4 times when trying to predict forest characteristics by three major groups of tree species – pine, spruce and all deciduous trees taken together. Main conclusion of the study was that accuracy of predicting volume using ALS data decreased notably when targeting forest characteristics by three major groups of tree species.

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SPATIAL SCALE EFFECTS OF SAMPLING ON THE INTERPOLATION OF SPECIES DISTRIBUTION MODELS IN THE SOUTHWESTERN AMAZON

Revista Árvore ◽

10.1590/0100-67622016000400005 ◽

2016 ◽

Vol 40 (4) ◽

pp. 617-625 ◽

Cited By ~ 1

Author(s):

Symone Maria de Melo Figueiredo ◽

Eduardo Martins Venticinque ◽

Evandro Orfanó Figueiredo

Keyword(s):

Forest Management ◽

Spatial Scale ◽

Species Distribution ◽

Tree Species ◽

Species Distribution Models ◽

Scale Effects ◽

Projection Area ◽

Distribution Models ◽

Forest Inventories ◽

Occurrence Data

ABSTRACT Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

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