scholarly journals Developing General Equations for Urban Tree Biomass Estimation with High-Resolution Satellite Imagery

2019 ◽  
Vol 11 (16) ◽  
pp. 4347 ◽  
Author(s):  
Jindong Wu
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Urban Forest ◽  
Urban Trees ◽  
Sustainable Urban Development ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Urban Tree

Urban trees provide various important ecological services, the quantification of which is vital to sustainable urban development and requires accurate estimation of tree biomass. A limited number of allometric biomass equations, however, have been developed for urban species due to the prohibitive cost. Remote sensing has provided cost-effective means for estimating urban forest biomass, although the propagation of error in the estimation process is not well understood. This study aimed to offer a baseline assessment of the feasibility of estimating urban tree biomass with remote sensing-based general equations applicable to broad taxonomic groups by conducting a large urban tree inventory on a university campus. The biomasses of 191 trees of seven species from the inventory, separated into two categories (i.e., evergreen and deciduous), were calculated exclusively with urban-based species-specific allometric equations. WorldView-2 satellite imagery data were acquired to retrieve normalized difference vegetation index (NDVI) values at the location, crown, and stand levels. The results indicated that biomass correlated with NDVI in varying forms and degrees. The general equations at the crown level yielded the most accurate biomass estimates, while the location-level estimates were the least accurate. Crown-level spectral responses provided adequate information for delivering spatially explicit biomass estimation.

scholarly journals ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE

2016 ◽  
Vol XLI-B8 ◽  
pp. 73-77
Author(s):  
Ge Han ◽  
Wei Gong ◽  
Xiaohui Cui ◽  
Miao Zhang ◽  
Jun Chen
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Rough Set Theory ◽  
Evaluation Framework ◽  
Likelihood Method ◽  
Accurate Estimation ◽  
Support Vector ◽  
Ambient Atmosphere ◽  
Electrical Insulator ◽  
Severity Levels

The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.

scholarly journals ASSESSING URBAN FOREST CANOPY COVER IN GREAT PLAIN CONSERVATION AREA (DÜZCE CITY, TURKEY) BETWEEN 1984 AND 2015

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 539-544
Author(s):  
Z. Uçar ◽  
R. Eker ◽  
A. Aydin
Keyword(s):  
Ecosystem Services ◽  
Forest Canopy ◽  
Urban Forest ◽  
Canopy Cover ◽  
Tree Canopy ◽  
Urban Trees ◽  
Conservation Area ◽  
Urban Tree ◽  
Tree Canopy Cover ◽  
Urban Tree Canopy

Abstract. Urban trees and forests are essential components of the urban environment. They can provide numerous ecosystem services and goods, including but not limited to recreational opportunities and aesthetic values, removal of air pollutants, improving air and water quality, providing shade and cooling effect, reducing energy use, and storage of atmospheric CO2. However, urban trees and forests have been in danger of being lost by dense housing resulting from population growth in the cities since the 1950s, leading to increased local temperature, pollution level, and flooding risk. Thus, determining the status of urban trees and forests is necessary for comprehensive understanding and quantifying the ecosystem services and goods. Tree canopy cover is a relatively quick, easy to obtain, and cost-effective urban forestry metric broadly used to estimate ecosystem services and goods of the urban forest. This study aimed to determine urban forest canopy cover areas and monitor the changes between 1984–2015 for the Great Plain Conservation area (GPCA) that has been declared as a conservation Area (GPCA) in 2017, located on the border of Düzce City (Western Black Sea Region of Turkey). Although GPCA is a conservation area for agricultural purposes, it consists of the city center with 250,000 population and most settlement areas. A random point sampling approach, the most common sampling approach, was applied to estimate urban tree canopy cover and their changes over time from historical aerial imageries. Tree canopy cover ranged from 16.0% to 27.4% within the study period. The changes in urban canopy cover between 1984–1999 and 1999–2015 were statistically significant, while there was no statistical difference compared to the changes in tree canopy cover between 1984–2015. The result of the study suggested that an accurate estimate of urban tree canopy cover and monitoring long-term canopy cover changes are essential to determine the current situation and the trends for the future. It will help city planners and policymakers in decision-making processes for the future of urban areas.

scholarly journals Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew Gray ◽  
Monika Krolikowski ◽  
Peter Fretwell ◽  
Peter Convey ◽  
Lloyd S. Peck ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cell Density ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Algal Blooms ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Snow Algae ◽  
Antarctic Snow ◽  
Sentinel 2

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

scholarly journals The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan

2016 ◽  
Vol 6 (2) ◽  
pp. 69-81
Author(s):  
SENDI YUSANDI ◽  
I NENGAH SURATI JAYA
Keyword(s):  
Satellite Imagery ◽  
Mangrove Forest ◽  
Forest Biomass ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Mean Deviation ◽  
Landsat 8 ◽  
Estimation Model ◽  
West Kalimantan ◽  
Medium Resolution

Yusandi S, Jaya INS. 2016. The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan. Bonorowo Wetlands 6: 69-81. Mangrove forest is one of forest ecosystem types having the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as have a high capability on carbon sequestration. Up to now, however, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e., by using the moderate resolution imageries Landsat 8. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI has a considerably high correlation coefficient of larger than > 0.7071 with the stand biomass. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass is B=0.00023404 with the R² value of 77.1%. In general, the concession area of BSN Group (PT Kandelia Alam Semesta and PT Bina Ovivipari) have the potential of biomass ranging from 45 to 100 ton per ha.

Generalized additive models reveal among-stand variation in live tree biomass equations

2020 ◽  
Author(s):  
Jacob I. Levine ◽  
Perry de Valpine ◽  
John J. Battles
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Carbon Accounting ◽  
Stand Age ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Allometric Relationships ◽  
Tree Biomass ◽  
Live Tree

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted for sources of variation warrant a re-examination of allometric relationships using modern statistical techniques. We asked the following questions: (1) Is there among-stand variation in allometric relationships? (2) Is there nonlinearity in allometric relationships? (3) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (4) What are the implications for biomass estimation? To answer these questions, we synthesized a dataset of small trees from six different studies in the White Mountains of New Hampshire. We compared the performance of generalized additive models (GAMs) and linear models and found that GAMs consistently outperform linear models. The best-fitting model indicates that allometries vary among both stands and species and contain subtle nonlinearities which are themselves variable by species. Using a planned contrasts analysis, we were able to attribute some of the observed among-stand heterogeneity to differences in stand age. However, variability in these results point to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live-tree biomass estimation.

scholarly journals Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

2020 ◽  
Vol 12 (18) ◽  
pp. 3017
Author(s):  
Shirisa Timilsina ◽  
Jagannath Aryal ◽  
Jamie B. Kirkpatrick
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Urban Trees ◽  
Tree Cover ◽  
Urban Tree ◽  
Object Based ◽  
Parcel Size ◽  
Tree Coverage ◽  
The Relationship ◽  
Urban Tree Cover

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

scholarly journals Spatio-Temporal Analysis of Satellite Imagery (NDVI) to Identify Terroir and Vineyard Yeast Differences According to Appellation of Origin (AOP) and Biogeographic Origin

J ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 244-256
Author(s):  
Sergio Vélez ◽  
Enrique Barajas ◽  
Pilar Blanco ◽  
José Antonio Rubio ◽  
David Castrillo
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Analysis ◽  
Landsat 8 ◽  
Appellation Of Origin ◽  
Core Concepts ◽  
Spatio Temporal ◽  
Plant Habitat

Terroir is one of the core concepts associated with wine and presumes that the land from which the grapes are grown, the plant habitat, imparts a unique quality that is specific to that growing site. Additionally, numerous factors can influence yeast diversity, and terroir is among the most relevant. Therefore, it can be interesting to use Remote Sensing tools that help identify and give helpful information about the terroir and key characteristics that define the AOP (Appellation of Origin). In this study, the NDVI (Normalized Difference Vegetation Index) calculated from Landsat 8 imagery was used to perform a spatio-temporal analysis during 2013, 2014, and 2015 of several vineyards belonging to four different AOP in Galicia (Spain). This work shows that it is possible to use Remote Sensing for AOP delimitation. Results suggest: (i) satellite imagery can establish differences in terroir, (ii) the higher the NDVI, the higher the yeast species richness, (iii) the relationship between NDVI, terroir, and yeasts shows a stable trend over the years (Pearson’s r = 0.3894, p = 0.0119).

scholarly journals ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE

2016 ◽  
Vol XLI-B8 ◽  
pp. 73-77
Author(s):  
Ge Han ◽  
Wei Gong ◽  
Xiaohui Cui ◽  
Miao Zhang ◽  
Jun Chen
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Rough Set Theory ◽  
Evaluation Framework ◽  
Likelihood Method ◽  
Accurate Estimation ◽  
Support Vector ◽  
Ambient Atmosphere ◽  
Electrical Insulator ◽  
Severity Levels

The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.

scholarly journals Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245784
Author(s):  
Jérôme Théau ◽  
Étienne Lauzier-Hudon ◽  
Lydiane Aubé ◽  
Nicolas Devillers
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Bare Soil ◽  
The Other ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Estimation Model

Grasslands are among the most widespread ecosystems on Earth and among the most degraded. Their characterization and monitoring are generally based on field measurements, which are incomplete spatially and temporally. The recent advent of unmanned aerial vehicles (UAV) provides data at unprecedented spatial and temporal resolutions. This study aims to test and compare three approaches based on multispectral imagery acquired by UAV to estimate forage biomass or vegetation cover in grasslands. The study site is composed of 30 pasture plots (25 × 50 m), 5 bare soil plots (25 x 50), and 6 control plots (5 × 5 m) on a 14-ha field maintained at various biomass levels by grazing rotations and clipping over a complete growing season. A total of 14 flights were performed. A first approach based on structure from motion was used to generate a volumetric-based biomass estimation model (R2 of 0.93 and 0.94 for fresh biomass [FM] and dry biomass [DM], respectively). This approach is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2 (0.1 kg DM/m2). The Green Normalized Difference Vegetation Index (GNDVI) was selected to develop two additional approaches. One is based on a regression biomass prediction model (R2 of 0.80 and 0.66 for FM and DM, respectively) and leads to an accurate estimation at levels of FM lower than 3 kg/m2 (0.6 kg DM/m2). The other approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. These three approaches are relatively simple to use and applicable in an operational context. They are also complementary and can be adapted to specific applications in grassland characterization.

scholarly journals Urban Trees Programs from Municipal Officials' Perspective: Evidence from Alabama, U.S.

2012 ◽  
Vol 38 (4) ◽  
pp. 160-167
Author(s):  
Yaoqi Zhang ◽  
Bin Zheng
Keyword(s):  
Urban Forest ◽  
Large Population ◽  
Urban Trees ◽  
Tree Planting ◽  
Property Value ◽  
Ecological Benefits ◽  
Policy Makers ◽  
Urban Tree ◽  
Municipal Employees ◽  
Household Incomes

Using survey data, this study explored Alabama municipal employees and policy makers’ perception of urban trees, financing, governing, and information sharing regarding urban forest management. Results suggest that the importance of urban trees is widely recognized by local municipal employees and policymakers. They also believe that urban trees would increase property value and promote community pride. Ecological benefits were, however, less valued. Alabama, U.S. cities spent less than USD $60,000 per year on tree planting, tree maintenance, debris, and tree removal. Cities with a large population usually appear to spend more on urban trees; likewise, cities with higher household incomes and lower poverty rates would have higher expenditures on urban tree programs. Relatively minor differences were found among the three types of employees and administrators: mayors, council members, and administrators. The results indicate many municipal officials were not aware of, or informed about, related agencies providing urban tree management services.

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