scholarly journals Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia

2018 ◽  
Vol 10 (11) ◽  
pp. 1796 ◽  
Author(s):  
Georgy Rybakov ◽  
Jussi Peuhkurinen ◽  
Petri Latva-Käyrä ◽  
Maria Villikka ◽  
Sanna Sirparanta ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Forest Inventory ◽  
Basal Area ◽  
Bayesian Regression ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Medium Resolution ◽  
The Mean ◽  
Field Plots

The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite imagery Landsat 8 from the leaf-on and leaf-off seasons. The research bases are on field plots and satellite data that are acquired for the real operational forest inventory, performed for industrial purposes during summer–fall 2015. Sparse Bayesian regression was used to estimate linear regression models between field-measured variables and features derived from satellite data. Coniferous/deciduous mapping was done, applying maximum likelihood classification. The study reported the root mean square error for the mean volume and basal area under 25% for both the plot level and compartment level. The overall accuracy of the forest-type classification in coniferous, mixed coniferous/deciduous, and deciduous classes was 71.6%. The features of Landsat 8 images from both seasons were selected in almost every model, indicating that the use of satellite imagery from different seasons improved the estimation accuracy. It has been shown that the combination of camera relascope-based field data and medium-resolution satellite imagery gives accurate enough results that compare well with previous studies in that field, and provide fast and solid data about forests of large areas for efficient investment decision making.

scholarly journals A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2233
Author(s):  
Rasa Janušaitė ◽  
Laurynas Jukna ◽  
Darius Jarmalavičius ◽  
Donatas Pupienis ◽  
Gintautas Žilinskas
Keyword(s):  
Satellite Imagery ◽  
Morphological Characteristics ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Multi Scale ◽  
Medium Resolution ◽  
Wide Range ◽  
Position Index ◽  
Local Relief ◽  
Length Width

Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (R2 = 0.999 and 0.997) with an average RMSE of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors.

scholarly journals Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

2019 ◽  
Vol 11 (15) ◽  
pp. 1803 ◽  
Author(s):  
John Hogland ◽  
Nathaniel Anderson ◽  
David L. R. Affleck ◽  
Joseph St. Peter
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Forest Type ◽  
Longleaf Pine ◽  
Basal Area ◽  
Pinus Palustris ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Inventory Data ◽  
Forest Inventory Data

This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy > 0.80; square root basal area per ha residual standard errors < 1; natural log transformed trees per ha < 1.081). Model estimates depicting spatially explicit, fine resolution raster surfaces of forest characteristics for multiple coniferous and deciduous species across the study area were created and made available to the public in an online raster database. These products can be integrated with existing tabular, vector and raster databases already being used to guide longleaf pine conservation and restoration in the region.

scholarly journals Comparison of true mangrove stands in Dudepo and Ponelo Islands, North Gorontalo District, Indonesia

2018 ◽  
Vol 20 (1) ◽  
pp. 259-266
Author(s):  
FAIZAL KASIM ◽  
MIFTAHUL KHAIR KADIM ◽  
SITTI NURSINAR ◽  
ZULKIFLI KARIM ◽  
ALDIN LAMALANGO
Keyword(s):  
Similarity Index ◽  
Basal Area ◽  
Current Status ◽  
Survey Method ◽  
Landsat 8 ◽  
Importance Value Index ◽  
Importance Value ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
The Mean

Kasim F, Kadim MK, Nursinar S, Karim Z, Lamalango A. 2019. Comparison of true mangrove stands in Dudepo and Ponelo Islands, North Gorontalo District, Indonesia. Biodiversitas 20: 259-266. This study aimed to investigate and compare the current status of mangrove areas, as well as the composition and species diversity of mangrove stands in both regions of Dudepo and Ponelo Islands. The results showed that the mangrove areas calculated using the segmentation method in classifying image of Landsat-8 OLI (acquisition on September 2017) were 279.46 ha (Dudepo Island) and 113.35 ha (Ponelo Island) respectively. A total of 13 species of true mangrove were recorded from both islands, using survey method from 9 transect lines (TL), with a distance ranging from 40 to 210 meters (1-6 quadrats) per transect. The mean densities of trees were 2133 ± 329.78 ha-1 (Dudepo Island) and 2111 ± 234.28 ha-1 (Ponelo Island), while those of saplings and seedlings were 58 ± 13.48 ha-1 and 1425 ± 113.96 ha-1 (Dudepo Island), and 79 ± 14.51 ha-1 and 2963 ± 443.22 ha-1 (Ponelo Island). The mean diameter and basal area were 19.73 ± 10.65 cm and 84.22 ± 67.67 m2ha-1 (Dudepo Island), 17.04 ± 1.46 cm and 60.07 ± 15.12 m2ha-1 (Ponelo Island), respectively. The Importance Value Index (IVI) ranged between 3.97-114.87 (Dudepo Island) and 6.04-82.18 (Ponelo Island). The dominant and codominant species based on IVI in both islands were Rhizophora apiculata Blume and R. stylosa Griff. The indexes of diversity, richness, and evenness of mangrove species in both islands were 0.34-1.70, 0.48-1.18, 0.47-0.94 (trees), 0.00-1.10, 0.00-1.82, 0.00-1.00 (saplings), and 0.00.-1.48, 0.00-1.44, 0.72-1.00 (seedlings), respectively. The Bray-Curtis similarity index between Dudepo and Ponelo Islands, based on the overall values of community attributes, was 0.75.

scholarly journals Prediction of dendrometric variables by optical data in Pinus taeda L. in the south of Brazil

2021 ◽  
Vol 8 (2) ◽  
pp. 1433-1443
Author(s):  
Carla Talita Pertille ◽  
Marcos Felipe Nicoletti ◽  
Larissa Regina Topanotti ◽  
Luís Paulo Baldiserra Schorr
Keyword(s):  
Pinus Taeda ◽  
Forest Inventory ◽  
Vegetation Indices ◽  
Basal Area ◽  
Optical Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pinus Taeda L ◽  
South Of Brazil ◽  
Pinus Spp

The objective of this work was to estimate the basal area and volume of a Pinus taeda L. settlement located in Santa Catarina, correlating data from an orbital image of the Landsat-8 / OLI sensor and forest inventory. In this sense, a forest research was carried out, with a random sampling process using the fixed area method. 20 circular parcels of 400 m² were allocated. An orbital image of the Landsat-8 / OLI sensor was used and 10 average vegetation indices per plot were calculated. These were correlated as variables of volume and basal area per plot, decorative by the forest inventory. The index with the best correlation for the volume was GNDVI with 0.47 and for a basal area, the MVI with 0.51. The adjustment of the regression models showed adjusted R² indices of 0.5639 and Syx of 13.31% for volume, and 0.5213 and 11.93% for the basal area. It was possible to estimate the volume and basal area of the stands through the spectral data, however, it is recommended that this same technique be tested in other species of the genus Pinus spp. and with high spatial resolution media.

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.

scholarly journals CITIZEN SCIENCE FOR EARTH OBSERVATION: APPLICATIONS IN ENVIRONMENTAL MONITORING AND DISASTER RESPONSE

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1221-1226 ◽  
Author(s):  
V. Kotovirta ◽  
T. Toivanen ◽  
R. Tergujeff ◽  
T. Häme ◽  
M. Molinier
Keyword(s):  
Water Quality ◽  
Citizen Science ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Forest Biomass ◽  
Water Quality Monitoring ◽  
Earth Observation ◽  
Quality Monitoring ◽  
Landsat 8

Citizen science is a promising way to increase temporal and spatial coverages of in-situ data, and to aid in data processing and analysis. In this paper, we present how citizen science can be used together with Earth observation, and demonstrate its value through three pilot projects focusing on forest biomass analysis, data management in emergencies and water quality monitoring. We also provide recommendations and ideas for follow-up activities. <br><br> In the forest biomass analysis pilot, in the state of Durango (Mexico), local volunteers make in-situ forest inventory measurements with mobile devices. The collected data is combined with Landsat-8 imagery to derive forest biomass map of the area. The study area includes over 390 permanent sampling plots that will provide reference data for concept validation and verification. <br><br> The emergency data management pilot focuses in the Philippines, in the areas affected by the typhoons Haiyan in November 2013 and Hagupit in December 2014. Data collected by emergency workers and citizens are combined with satellite data (Landsat-8, VHR if available) to intensify the disaster recovery activities and the coordination efforts. Simple processes for citizens, nongovernmental organisations and volunteers are developed to find and utilize up to date and freely available satellite imagery for coordination purposes and for building new not-for-profit services in disaster situations. <br><br> In the water quality monitoring pilot, citizens around the Baltic Sea area contribute to the algae situation awareness by collecting algae observations using a mobile application. In-situ observations are compared with surface algal bloom products based on the satellite imagery, e.g. Aqua MODIS images with 500 meter resolution. As an outcome, the usability of the citizen observations together with satellite data in the algae monitoring will be evaluated.

scholarly journals A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

2020 ◽  
Vol 12 (15) ◽  
pp. 2365
Author(s):  
Xidong Chen ◽  
Liangyun Liu ◽  
Yuan Gao ◽  
Xiao Zhang ◽  
Shuai Xie
Keyword(s):  
Satellite Imagery ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Low Resolution ◽  
Spectral Bands ◽  
Optical Images ◽  
Medium Resolution ◽  
Training Samples ◽  
Land Surfaces ◽  
Sentinel 2

Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.

scholarly journals Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

2019 ◽  
Vol 11 (2) ◽  
pp. 122 ◽  
Author(s):  
Zhongbin Li ◽  
David Roy ◽  
Hankui Zhang ◽  
Eric Vermote ◽  
Haiyan Huang
Keyword(s):  
Aerosol Optical Depth ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Urban Environments ◽  
Landsat 8 ◽  
Urban Aerosol ◽  
Medium Resolution ◽  
Aerosol Monitoring ◽  
Sentinel 2A ◽  
Sentinel 2

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

Assessment of the potential of different types of thinning based on the analysis of satellite data Landsat 8

2015 ◽  
Vol 5 (1) ◽  
pp. 97-109
Author(s):  
Жафяров ◽  
Arutur Zhafyarov ◽  
Дорощенкова ◽  
Elvira Doroshchenkova ◽  
Сидоренков ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Forest Inventory ◽  
Preliminary Analysis ◽  
Field Experiments ◽  
Remote Sensing Data ◽  
Forest Plantations ◽  
Landsat 8 ◽  
Access To Data ◽  
Definition Of Information

Restriction of access to data of forest inventory leads to difficulties in the implementation of scientific developments in the field of forestry, as a significant part of them until the business implementation passes a way of testing and improvements carried out actually on the enthusiasm of developers. Taking into account difficulty in obtaining forest inventory data for research in the article there is an example of the definition of information on forest plantations on the basis of the decryption freeware remote sensing data from Landsat 8 (OLI). One advantage of the processing of these data is their accessibility to any territory of Russia, as well as the presence of a significant number of spectral channels, which allows using different methods of analysis of satellite imagery to determine the characteristics of forest stands. Based on the information assignment of forestry activities for the care of forests is made and the proportion of various activities in the overall system of care for forests is assessed. The results show the possibility of using modern methods of processing of satellite data for a preliminary analysis of forest resources in a certain area in order to obtain information on forest plantations. When working to verify the results reference areas were used laid down in the stands of different composition. When conducting field experiments we focused on the naturally-formed plantings in place of clearcuts. Data of test plots were associated with automatic classification standards that are being implemented in the program Envi. In areas with a lack of data on test plots verification was carried out using partially materials of forest inventory, as well as of remote shooting of high-resolution 1.2 m placed in the public domain on Google and Yandex services. The results show the possibility of a preliminary analysis of the potential destination of different activities of forest care based on freely available data of Earth remote sensing (ERS).

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