scholarly journals Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 130 ◽  
Author(s):  
Yan Meng ◽  
Banghua Cao ◽  
Peili Mao ◽  
Chao Dong ◽  
Xidong Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Tree Species ◽  
Black Locust ◽  
Robinia Pseudoacacia ◽  
Remote Sensing Data ◽  
Image Data ◽  
Google Earth ◽  
Percentage Decrease ◽  
Forestry Resources

Located in the Mount Tai state-owned forest farm, this study adopted Landsat multispectral remote sensing data in 2000 and 2016 on the GEE (Google Earth Engine) platform and selected four phases of images each year according to the phenological period. By dealing with the current situation map of forestry resources in 2000 and the field survey data in 2016, the samples of tree species distribution in 2000 and 2016 were obtained. On the basis of topographic correction with the empirical rotation model, this study used the random forest (RF) classifier to classify tree species from remote sensing images in 2000 and 2016, achieving high classification accuracy. The results showed that, after 16 years of evolution, the percentage of pine species in the forest decreased from 55.69% to 50.22%, with a percentage decrease as high as 5.47%. The percentage of black locust (Robinia pseudoacacia) increased from 10.15% in 2000 to 13.75% in 2016, with an increase of 3.60%. Quercus also had a positive growth in the area. This result reflected the expansion of black locust.

scholarly journals HORIZONTAL ACCURACY ASSESSMENT OF GOOGLE EARTH DATA OVER TYPICAL REGIONS OF AUSTRALIA USING WORLDVIEW

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 763-768
Author(s):  
J. Guo ◽  
H. J. Tu ◽  
H. Li ◽  
Y. Zhao ◽  
J. Zhou
Keyword(s):  
Remote Sensing ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Remote Sensing Data ◽  
Image Data ◽  
Research Area ◽  
Google Earth ◽  
Accuracy Evaluation ◽  
Australian Continent ◽  
Assessment Results

Abstract. Since the release of Google Earth image data, it has been the most widely used remote sensing data worldwide, and its accuracy evaluation has also been the focus of historical research. However, the researchers found that Google Earth's image accuracy assessment results have obvious regional characteristics. This article uses the Australian continent as the research area and WorldView-2 remote sensing images as reference data to study the accuracy evaluation results of Google Earth data. The research shows that the overall accuracy of the assessment area in Australia is better. The areas with the best overall accuracy appear in the western coastal areas, with an accuracy range of 0.7–1.4; the accuracy assessment results in the central desert area are also better, with the accuracy range 1.4–2.2, and the areas with the worst accuracy appear in the western mountains and hills of 14.5 and 17.1.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 692
Author(s):  
MD Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Andrea Galli ◽  
Giuseppe Modica ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Tree Species ◽  
Carbon Stock ◽  
Dominant Species ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Lidar Data ◽  
Urban Tree ◽  
Sensing Data

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.

scholarly journals A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 747
Author(s):  
Yanghua Di ◽  
Zhiguo Jiang ◽  
Haopeng Zhang
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Visual Categorization ◽  
Class Differences ◽  
Fine Grained ◽  
Ship Classification

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

Debris-induced stagnation and ensuing morphological evolution of a central Himalayan glacier

2021 ◽  
Author(s):  
Purushottam Kumar Garg ◽  
Aparna Shukla ◽  
Santosh Kumar Rai ◽  
Jairam Singh Yadav
Keyword(s):  
Remote Sensing ◽  
Morphological Evolution ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Lateral Moraine ◽  
Central Himalaya ◽  
Ablation Zone ◽  
Glacier Surface ◽  
Glacier System ◽  
Debris Cover

<p>This study presents field evidences (October 2018) and remote sensing measurements (2000-2020) to show stagnant conditions of lower ablation zone (LAZ) of the ‘companion glacier’, central Himalaya, India and its implication on the morphological evolution. The Companion glacier is named so as it accompanied the Chorabari glacier (widely studied benchmark glacier in the central Himalaya) in the distant past. Supraglacial debris thickness, supraglacial ponds anf other morphological features (e.g. lateral moraine height, supraglacial mounds) were measured/observed in the field. Glacier area, length, debris extent, surface elevation change and surface ice velocity were estimated using satellite remote sensing data from Landsat-TM/ETM+/OLI, Sentinel-MSI, Terra-ASTER and SRTM, Cartosat-1 and Google Earth images. Results show that the glacier has very small accumulation area and it is mainly fed by avalanches. The headwall of glacier is very steep which causes frequent avalanches leading to voluminous debris addition to the glacier system. Consequently, about 80% area of the glacier is debris-covered. The debris is very thick in the LAZ exceeding several meters in the LAZ and comprised of big boulders making debris thickness measurements practically impossible particularly in the snout region. However, debris thickness decreases with increasing distance from the snout and is in the order of 20-40 cm at about 2.5 km upglacier. The huge debris cover has protected the glacier ice from rapid melting. That’s why surface lowering of the glacier is less as compared to nearby Chorabari glacier. Moreover, due to (a) less mass supply from upper reaches and (b) huge debris cover, the glacier movement is very slow. The movement is too low that is allowed vegetation (some big grasses with wooded stems) to grow and survive on the glacier surface. The slow moving LAZ also causing bulging on the upper ablation zone (UAZ). Consequently, several mounds have developed on the UAZ. Thin debris slides down from mounds exposing the ice underneath for melting. Owing to these processes, spot melting is now a dominant mechanism of glacier wastage in the companion glacier. Thus, it can be summarized that careful field observations along with remote sensing estimates can be very important for understanding the glacier evolution.</p>

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Biotropica ◽  
2018 ◽  
Vol 50 (5) ◽  
pp. 758-767 ◽  
Author(s):  
Pablo Pérez Chaves ◽  
Kalle Ruokolainen ◽  
Hanna Tuomisto
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Tree Species ◽  
Model Tree ◽  
Lowland Amazonia

scholarly journals Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6227 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Data Science ◽  
Remote Sensing Data ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Sensing Data ◽  
Tree Species Classification ◽  
Selection Of

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

scholarly journals Estimation of forest cover restoration damaged by Siberian silkmoth using remote sensing data and maps

2019 ◽  
Vol 75 ◽  
pp. 02005
Author(s):  
Elena Fedotova
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Tree Species ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Deciduous Forests ◽  
Sensing Data ◽  
Current State

The current state of the land cover has been estimated in the territories where in different years (1885, 1955, 1995) the forests were damaged by Siberian silkmoth. Dark-needle taiga is restored through the change of tree species. In 20 years in areas of dark-needle taiga there are graminoid communities, in 60 years we have deciduous forests there, and in 130 - dark needle forests, but not everywhere.

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

Sign in / Sign up

Export Citation Format

Share Document