scholarly journals Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

2021 ◽  
Vol 11 (23) ◽  
pp. 11348
Author(s):  
Huaqiao Xing ◽  
Jingge Niu ◽  
Chang Liu ◽  
Bingyao Chen ◽  
Shiyong Yang ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Remote Sensing Data ◽  
Classification Systems ◽  
Forest Monitoring ◽  
Climatic Zones ◽  
Forest Mapping ◽  
Border States ◽  
Forest Policies ◽  
Spatial Consistency ◽  
Landscape Types

Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest consistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000–3500 m above sea level and 26°–35° slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries.

scholarly journals Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

2019 ◽  
Vol 8 (2) ◽  
pp. 56 ◽  
Author(s):  
Maliheh Arekhi ◽  
Cigdem Goksel ◽  
Fusun Balik Sanli ◽  
Gizem Senel
Keyword(s):  
Vegetation Index ◽  
Data Retrieval ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Spring Season ◽  
Landsat 8 Oli ◽  
Atmospheric Effect ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

scholarly journals Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset

2020 ◽  
Vol 12 (21) ◽  
pp. 3479
Author(s):  
Yuan Gao ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Xidong Chen ◽  
Jun Mi ◽  
...  
Keyword(s):  
European Union ◽  
Land Cover ◽  
Hydrological Cycle ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
The European Union ◽  
Mountain Areas ◽  
Land Cover Types ◽  
Spatial Consistency ◽  
The Eu

Land-cover plays an important role in the Earth’s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.

scholarly journals Spatiotemporal dynamics of wetted soils across a polar desert landscape

2014 ◽  
Vol 27 (2) ◽  
pp. 197-209 ◽  
Author(s):  
Zachary L. Langford ◽  
Michael N. Gooseff ◽  
Derrick J. Lampkin
Keyword(s):  
Remote Sensing ◽  
Regional Climate ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Dry Valleys ◽  
Cold Desert ◽  
Land System ◽  
Spatial Consistency ◽  
Free Region ◽  
Desert Landscape

AbstractLiquid water is scarce across the landscape of the McMurdo Dry Valleys (MDV), Antarctica, a 3800 km2ice-free region, and is chiefly associated with soils that are adjacent to streams and lakes (i.e. wetted margins) during the annual thaw season. However, isolated wetted soils have been observed at locations distal from water bodies. The source of water for the isolated patches of wet soil is potentially generated by a combination of infiltration from melting snowpacks, melting of pore ice at the ice table, and melting of buried segregation ice formed during winter freezing. High resolution remote sensing data gathered several times per summer in the MDV region were used to determine the spatial and temporal distribution of wet soils. The spatial consistency with which the wet soils occurred was assessed for the 2009–10 to 2011–12 summers. The remote sensing analyses reveal that cumulative area and number of wet soil patches varies among summers. The 2010–11 summer provided the most wetted soil area (10.21 km2) and 2009–10 covered the least (5.38 km2). These data suggest that wet soils are a significant component of the MDV cold desert land system and may become more prevalent as regional climate changes.

scholarly journals A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1211
Author(s):  
Adeel Ahmad ◽  
Sajid Rashid Ahmad ◽  
Hammad Gilani ◽  
Aqil Tariq ◽  
Na Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Data ◽  
Constitutional Amendment ◽  
Forest Mapping ◽  
Remote Sensing Classification ◽  
Sensing Data ◽  
Forest Assessment ◽  
Before And After ◽  
Supervised Image Classification

This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers' citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

scholarly journals Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data

10.29007/jvz3 ◽  
2018 ◽  
Author(s):  
Mohamed Mostafa Mohamed ◽  
Samy Elmahdy
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Confusion Matrix ◽  
Land Development ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Spectral Angle Mapper ◽  
Fast Pace

Dubai is a rapidly urbanizing emirate with land development succeeding at a fast pace. The present study aims to develop a low-cost classifier based on the spectral angle mapper (SAM) and image difference (ID) algorithms. The proposed approach was developed in order to improve Land use/ Land cover (LULC) classification maps for the purpose of monitoring and analysing LULC change during the period from 2000 to 2015 for the Emirate of Dubai. The approach starts by collecting 320 training samples from high resolution images such as QuickBird with a spatial resolution of 60 cm followed by applying a 3×3 spatial convulsion filter, majority/ minority analysis, sieving classes and clump map of the produced LULC maps. After that, the accuracy of the maps were assigned using confusion matrix. The accuracy assessment demonstrated that the targeted 2000, 2005,2010 and 2015 LULC maps have 88.125%, 89.069%, 90.122% and 96.096% accuracy, respectively. The results exhibited that the built-up areas increased by 233.72 km2 (5.81%) from 2000 to 2005 and keeps to increase even up and till the present time. The results also showed that the changes in the periods 2000-2005 and 2010-2015 confirmed that net vegetation area loses were more obvious from 2005 to 2005 than from 2010 to 2015, reducing from 47.618 km2 to 40,820 km2, respectively. This study is of great help to urban planners and decision makers.

scholarly journals Monitoring coniferous forests using remote sensing data (on the example of Tukhlyanske forestry)

2021 ◽  
Vol 42 (II) ◽  
pp. 99-108
Author(s):  
Kh. BURSHTYNSKA ◽  
◽  
Y. DEKALIUK ◽  
Keyword(s):  
Remote Sensing ◽  
Biological Diversity ◽  
Remote Sensing Data ◽  
Forest Monitoring ◽  
Practical Significance ◽  
Coniferous Forests ◽  
Maximum Probability ◽  
Structure Detection ◽  
Uav Images

The purpose of the work is to consider the state of coniferous forests of the Tukhlyanske forestry of the Precarpathian region. Changes in land cover, pollution of air, water and soil, and deterioration of their quality, loss of biological diversity occur for forest ecosystems at the regional and global levels. Climate change, rising temperatures and declining rainfall are provoking the development of pests that are most common in coniferous forests. Remote sensing technologies allow to create forest monitoring systems, including determination of plantation structure, detection of changes in forests due to fires, deforestation, environmental problems, in particular forest drying. The method of detecting changes in forests is based on the use of high-resolution space imagery and on the processing of images obtained from unmanned aerial vehicles to identify healthy, dried and partially damaged by drying conifers in test areas. The result of the study is an image obtained by the method of controlled classification. The accuracy of the classification depends on the choice of signatures, and for that the UAV images are used. Scientific novelty and practical significance. A method for the identification of different states of coniferous forests using the method of controlled classification by the algorithm of maximum probability is proposed. The choice of class signatures is fundamental to solving the problem. The technique can be applied in various structures of forestry

scholarly journals Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment

2019 ◽  
Vol 11 (20) ◽  
pp. 2420 ◽  
Author(s):  
Brian Alan Johnson ◽  
Shahab Eddin Jozdani
Keyword(s):  
Land Cover ◽  
Thermal Environment ◽  
Accuracy Assessment ◽  
Remote Sensing Data ◽  
Physical Characteristics ◽  
Anthropogenic Heat ◽  
Climate Zone ◽  
Local Climate ◽  
Local Climate Zone ◽  
Map Accuracy

Local climate zone (LCZ) maps are increasingly being used to help understand and model the urban microclimate, but traditional land use/land cover map (LULC) accuracy assessment approaches do not convey the accuracy at which LCZ maps depict the local thermal environment. 17 types of LCZs exist, each having unique physical characteristics that affect the local microclimate. Many studies have focused on generating LCZ maps using remote sensing data, but nearly all have used traditional LULC map accuracy metrics, which penalize all map classification errors equally, to evaluate the accuracy of these maps. Here, we proposed a new accuracy assessment approach that better explains the accuracy of the physical properties (i.e., surface structure, land cover, and anthropogenic heat emissions) depicted in an LCZ map, which allows for a better understanding of the accuracy at which the map portrays the local thermal environment.

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 Monitoring Urban Expansion Using Remote-Sensing Data Aided by Google Earth Engine

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Majid Aghlmand ◽  
Gordana Kaplan
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Support Vector ◽  
Sensing Data ◽  
Google Earth Engine ◽  
Landsat Satellite ◽  
The City

Urbanizationis accompanied by rapid social and economic development, while the process of urbanization causes the degradation of the natural ecology. Direct loss in vegetation biomass from areas with a high probability of urban expansion can contribute to the total emissions from tropical deforestation and land-use change. Monitoring of urban expansion is essential for more efficient urban planning, protecting the ecosystem and the environment. In this paper, we use remote sensing data aided by Google Earth Engine (GEE) to evaluate the urban expansion of the city of Isfahan in the last thirty years. Thus, in this paper we use Landsat satellite images from 1986 and 2019, integrated into GEE, implementing Support vector machine (SVM) classification method. The accuracy assessment for the classified images showed high accuracy (95-96%), while the results showed a significant increase in the urban area of the city of Isfahan, occupying more than 70% of the study area. For future studies, we recommend a more detailed investigation about the city expansion and the negative impacts that may occur due to urban expansion.

Sign in / Sign up

Export Citation Format

Share Document