scholarly journals Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 565
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Robert Shea Duerler ◽  
Xinwei Yang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Tree Species ◽  
Regional Distribution ◽  
Google Earth ◽  
Pinus Tabulaeformis ◽  
Accurate Information ◽  
Natural Forests ◽  
Distribution Models ◽  
Google Earth Engine ◽  
Populus Spp

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species’ distribution models and environmental factors based on the classification results rather than field survey plots in further studies.

scholarly journals Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

2021 ◽  
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Barjeece Bashir ◽  
Yiyu Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Learning Algorithm ◽  
Google Earth ◽  
Accurate Information ◽  
Natural Forests ◽  
Source Information ◽  
Forestry Management ◽  
Google Earth Engine ◽  
Total Forest Area

Abstract Background Accurate information on tree species is much in demand for forestry management and further investigations on biodiversity and forest ecosystem services. Over regional or large areas, discriminating tree species at high resolution is deemed challenging by lack of representative features and computational power. Methods A novel methodology to delineate the explicit spatial distribution of dominated six tree species (Pinus, Quercus, Betula, Populus, Larch, and Apricot) and one residual class using the analysis-ready large volume multi-sensor imagery within Google Earth Engine (GEE) platform is demonstrated and used to map a 10 m classification with detail analysis of spatial pattern for an area covering over 90,000 km 2 between 41° N and 45° N. Random Forest (RF) algorithm built into GEE was used for tree species mapping, together with the multi-temporal features extracted from Sentinel-1/2 and topographic imagery data. The composition of tree species in natural forests and plantations in city and county-level were performed in detail afterwards. Results The proposed model achieved a reliable overall agreement (77.5%, 0.71 kappa), and the detailed analysis on the spatial distributing of targeted species indicated that the plantations (Pinus, Populus, Larch, and Apricot) outnumber natural forests (Quercus and Betula) by 6%, and they were mainly grown in the northern and southern regions, respectively. Moreover, Arhorchin had the largest total forest area of over 4,500 km 2 , while Hexingten and Aohan ranked first in natural forest and plantation area, and the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. Conclusions It is our belief that combined multi-source information of the machine learning algorithm within cloud platforms is beneficial to map a reliable spatial tree species over large areas on a fine scale. High-resolution tree species information based on online tools could be more easily considered for practical forestry management and further studies on forest ecosystems.

scholarly journals Forest ecological monitoring of the Shiyang River basin based on Google Earth Engine

2021 ◽  
Vol 932 (1) ◽  
pp. 012011
Author(s):  
Y Wang
Keyword(s):  
River Basin ◽  
Terrestrial Ecosystems ◽  
Google Earth ◽  
Ecological Environment ◽  
Ecological Quality ◽  
Natural Forests ◽  
Shiyang River Basin ◽  
Natural Factors ◽  
Google Earth Engine

Abstract The Shiyang River basin is a typical inland arid region and one of the most fragile and sensitive areas of terrestrial ecosystems in China, and it is important to understand its ecological changes in a timely and accurate manner. This article selects the Shiyang River basin forest as the research area and uses Google Earth Engine (GEE) to evaluate and monitor the ecological environment quality of the Shiyang River basin from 1990 to 2020. The geographical detector model (GDM) was also used to analyse the sensitivity of the forest ecological environment to three natural factors: elevation, temperature and altitude. The results showed that the ecological quality of the natural forest is significantly better than that of the man-made forest area, and the ecological quality grade is higher. The forest change area RSEI has a large annual variation in ecological quality and is vulnerable to external factors. Among the influencing natural factors, the sensitive factors of precipitation and altitude are both greater than 84%. The temperature sensitivity of natural forests is stronger than that of man-made forests, ranging from 66% to 92% overall.

scholarly journals Rapid Mapping and Annual Dynamic Evaluation of Quality of Urban Green Spaces on Google Earth Engine

2021 ◽  
Vol 10 (10) ◽  
pp. 670
Author(s):  
Qiang Chen ◽  
Cuiping Zhong ◽  
Changfeng Jing ◽  
Yuanyuan Li ◽  
Beilei Cao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Urban Area ◽  
Google Earth ◽  
Green Spaces ◽  
Dynamic Evaluation ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Rapid Mapping ◽  
Google Earth Engine

In order to achieve the United Nations 2030 Sustainable Development Goals (SDGs) related to green spaces, monitoring dynamic urban green spaces (UGSs) in cities around the world is crucial. Continuous dynamic UGS mapping is challenged by large computation, time consumption, and energy consumption requirements. Therefore, a fast and automated workflow is needed to produce a high-precision UGS map. In this study, we proposed an automatic workflow to produce up-to-date UGS maps using Otsu’s algorithm, a Random Forest (RF) classifier, and the migrating training samples method in the Google Earth Engine (GEE) platform. We took the central urban area of Beijing, China, as the study area to validate this method, and we rapidly obtained an annual UGS map of the central urban area of Beijing from 2016 to 2020. The accuracy assessment results showed that the average overall accuracy (OA) and kappa coefficient (KC) were 96.47% and 94.25%, respectively. Additionally, we used six indicators to measure quality and temporal changes in the UGS spatial distribution between 2016 and 2020. In particular, we evaluated the quality of UGS using the urban greenness index (UGI) and Shannon’s diversity index (SHDI) at the pixel level. The experimental results indicate the following: (1) The UGSs in the center of Beijing increased by 48.62 km2 from 2016 to 2020, and the increase was mainly focused in Chaoyang, Fengtai, and Shijingshan Districts. (2) The average proportion of relatively high and above levels (UGI > 0.5) in six districts increased by 2.71% in the study area from 2016 to 2020, and this proportion peaked at 36.04% in 2018. However, our result revealed that the increase was non-linear during this assessment period. (3) Although there was no significant increase or decrease in SHDI values in the study area, the distribution of the SHDI displayed a noticeable fluctuation in the northwest, southwest, and northeast regions of the study area between 2016 and 2020. Furthermore, we discussed and analyzed the influence of population on the spatial distribution of UGSs. We found that three of the five cold spots were located in the east and southeast of Haidian District. Therefore, the proposed workflow could provide rapid mapping and dynamic evaluation of the quality of UGS.

scholarly journals A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

2021 ◽  
Vol 13 (14) ◽  
pp. 2792
Author(s):  
Fugen Jiang ◽  
Chuanshi Chen ◽  
Chengjie Li ◽  
Mykola Kutia ◽  
Hua Sun
Keyword(s):  
Spatial Distribution ◽  
Urban Forest ◽  
Southern China ◽  
Simulation Method ◽  
Google Earth ◽  
Forest Carbon ◽  
Landsat 8 ◽  
Carbon Density ◽  
Spatial Simulation ◽  
Google Earth Engine

Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.

scholarly journals Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Ruiliang Pu
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Method Development ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Airborne Lidar ◽  
Accurate Information ◽  
Natural Forests ◽  
Future Directions ◽  
The Status

Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.

scholarly journals Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China

2022 ◽  
Vol 14 (1) ◽  
pp. 225
Author(s):  
Lijing Han ◽  
Jianli Ding ◽  
Jinjie Wang ◽  
Junyong Zhang ◽  
Boqiang Xie ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Drip Irrigation ◽  
Google Earth ◽  
Cotton Field ◽  
Arid Zones ◽  
Landsat 8 ◽  
Land Resource ◽  
Google Earth Engine ◽  
Cotton Fields ◽  
Sentinel 2

Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas.

scholarly journals Obtención de coberturas del suelo agropecuarias en imágenes satelitales Sentinel-2 con la inyección de imágenes de dron usando Random Forest en Google Earth Engine

2020 ◽  
pp. 49
Author(s):  
M. Ramírez ◽  
L. Martínez ◽  
M. Montilla ◽  
O. Sarmiento ◽  
J. Lasso ◽  
...  
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Spatial Resolution ◽  
Supervised Classification ◽  
Reduction Process ◽  
Google Earth ◽  
Accurate Information ◽  
Pass Filter ◽  
Google Earth Engine ◽  
Sentinel 2

<p><span lang="EN-US">To obtain accurate information on land cover changes in the agricultural sector, we propose a supervised classification method that integrates Sentinel-2 satellite imagery with images surveyed from Remote Piloted Aircraft Systems (RPAS). The methodology was implemented on the Google Earth Engine platform. Initially, the Sentinel-2 imagery collection was integrated into a single image through a median reduction process. Subsequently, the high-pass filter (HPF) pansharpening image fusion method was applied to the thermal spectral bands to obtain a final spatial resolution of 10 m. To perform the integration of the two image sources, the RPAS image was normalized by using a 5X5 gaussian texture filter and the pixel was resampled to five times its original size. This procedure was performed iteratively until reaching the spatial resolution of the Sentinel-2 imagery. Besides, the following inputs were added to the classification: the spectral indices calculated from the Sentinel-2 and RPAS bands (e.g. NDVI, NDWI, SIPI, GARI); altimetric information and slopes of the zone derived from the SRTM DEM. The supervised classification was done by using the Random Forest technique (Machine Learning). The land cover seed reference to perform the classification was manually captured by a thematic expert, then, this reference was distributed in 70% for the training of the Random Forest algorithm and in 30% to validate the classification. The results show that the incorporation of the RPAS image improves thematic accuracy indicators by an average of 3% compared to a classification made exclusively with Sentinel-2 imagery.</span></p>

Spatial Distribution and Key Prevention Areas of Ephemeral Gully under the 'Grain for Green Project' in Loess Plateau, China

2021 ◽  
Author(s):  
Yuan Zhong ◽  
Chunmei Wang ◽  
Guowei Pang ◽  
Qinke Yang ◽  
Zitian Guo ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Loess Plateau ◽  
Average Length ◽  
Regional Distribution ◽  
Google Earth ◽  
The Loess Plateau ◽  
Grain For Green ◽  
Ephemeral Gully ◽  
Grain For Green Project

&lt;p&gt;Soil erosion is an important threat in the high-quality development of the Loess Plateau of China, and Ephemeral Gully (EG) erosion is an important erosion type. Answering the distribution characteristics of EG at the regional scale is an important basis for EG control. The regional distribution of EG and the areas that still at high risk of EG development after the 'Grain for Green Project' since more than 20 years ago remain poorly understood. This study aimed to solve the above problems by using visual interpretation based on sub-meter Google Earth images in 137 systematically selected small watersheds in the Loess Plateau. The EG density, length, land use of the hillslope where each EG existed, and other parameters were obtained and analyzed using the GIS method. The spatial distribution of EG density, average length, and spatial correlation in the Loess Plateau was explored. The current EG distribution and key prevention areas in the Loess Plateau were identified. The results showed that: (1) EGs were found in 46 surveyed watersheds accounting for 33.6% of the total watershed number, with an EG density average value of 3.41km/km&lt;sup&gt;2&lt;/sup&gt; and maximum value of 21.92 km/km&lt;sup&gt;2&lt;/sup&gt;. The average number of EG was 60.32/km&lt;sup&gt;2&lt;/sup&gt;. EG length was mainly distributed in 20 ~ 60 m, with an average length of 63.31 m; The critical slope length of EGs was mainly 40 ~ 60 m, with an average 56.20 m. (2) The watersheds with EGs were mainly located in the north-central, the west, and northwest of the Loess Plateau. EG erosion is extremely strong in loess hilly and gully region, and moderate in loess plateau gully region.(3) 38.3% of EG was distributed in cropland; 35.3% distributed in grassland; 22.8% distributed in forest land. After the 'Grain for Green Project', the EGs that were still distributed on cropland were a more important threat to soil erosion and need better prevention efforts. EGs located on cropland were still widely distributed in many areas of Loess Plateau, such as the northwest of Yan 'an City in the middle and upper reaches of Beiluo River, Suide and Luliang in the lower reaches of Wuding River, at the junction of Dingxi and Huining and in Qingyang area. This research would help in a more reasonable distribution of erosion control practices in the Loess Plateau.&lt;/p&gt;

Climate resilience through natural regeneration in degraded natural forests of south-eastern hilly region of Bangladesh

2019 ◽  
Vol 48 (3) ◽  
pp. 417-425
Author(s):  
Md Khayrul Alam Bhuiyan ◽  
Md Akhter Hossain ◽  
Abdul Kadir Ibne Kamal ◽  
Mohammed Kamal Hossain ◽  
Mohammed Jashimuddin ◽  
...  
Keyword(s):  
Tree Species ◽  
Natural Regeneration ◽  
Diversity Indices ◽  
Acacia Auriculiformis ◽  
Quantitative Structure ◽  
Natural Forests ◽  
Climate Resilience ◽  
Hilly Region ◽  
Major Cluster ◽  
Ecological Dominance

A study was conducted by using 5m × 5m sized 179 quadrates following multistage random sampling method for comparative regenerating tree species, quantitative structure, diversity, similarity and climate resilience in the degraded natural forests and plantations of Cox's Bazar North and South Forest Divisions. A total of 70 regenerating tree species were recorded representing maximum (47 species) from degraded natural forests followed by 43 species from 0.5 year 39 species from 1.5 year and 29 species from 2.5 year old plantations. Quantitative structure relating to ecological dominance indicated dominance of Acacia auriculiformis, Grewia nervosa and Lithocarpus elegans seedlings in the plantations whereas seedlings of Aporosa wallichii, Suregada multiflora and Grewia nervosa in degraded natural forests. The degraded natural forests possess higher natural regeneration potential as showed by different diversity indices. The dominance-based cluster analysis showed 2 major cluster of species under one of which multiple sub-clusters of species exists. Poor plant diversity and presence of regenerating exotic species in the plantations indicated poor climate resilience of forest ecosystem in terms of natural regeneration.

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