scholarly journals Biomass and density estimation of mangrove vegetation using Landsat ETM+: Case study on Muara Gembong protection forest, Bekasi, West Java

2011 ◽  
Vol 1 (2) ◽  
pp. 80-95 ◽  
Author(s):  
OKTAMA FORESTIAN ◽  
LILIK BUDI PRASETYO ◽  
CECEP KUSMANA
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Density Estimation ◽  
Rice Field ◽  
Accuracy Assessment ◽  
West Java ◽  
Land Cover Types ◽  
Protection Forest ◽  
Mangrove Vegetation

Forestian O, Prasetyo LB, Kusmana C. 2011. Biomass and density estimation of mangrove vegetation using Landsat ETM+: Case study on Muara Gembong protection forest, Bekasi, West Java. Bonorowo Wetlands 1: 80-95. The study focused on Muara Gembong Mangrove Forest, Bekasi District of West Java Province. The purposes of this study were to determined the extent, potential biomass, and density of mangrove vegetation using Landsat ETM+ data in 2001 and 2010. The data is processed by several stages include: strip-filling, importing, stacking layer, subsetting, geographic correction, radiometric correction, image classification, estimation of mangrove biosystem character, and accuracy assessment. Land cover types found in Landsat scene of the region consists of nine class image classification categories, i.e. sea 1, sea 2, mangroves, open/built area, rice field 1, mixed farms, rice field 2, ponds, and rivers. Landsat images were classified by supervised classification techniques with maximum likelihood method. Each class of land cover types created 10 training area. Value of the inter-class separability (transformed divergence separability) image 2001 and 2010 more than 1,900 while the overall accuracy and Kappa accuracy respectively of 99.8% and 99.63% for image in 2001; 99.61 % and 99.39% for the image in 2010. Based on the accuracy assessment, which is classified image of 2010 has the highest overall classification accuracy of 83.33%, while the kappa statistic overalls worth 77.29 wide were in Karang Gading (78.99%), Tanjung Rejo (63.76%), while the lowest one was in Paluh Sibaji (20.58%) and Rugemuk (26.43%). The highest salinity was found at sub districts of Labuhan Deli and Hamparan Perak, while middle salinity at sub district of Percut Sei Tuan, while of sub district of Pantai Labu the salinity were from low level to middle. Base on the analysis of the vegetation closeness and canopy width, the condition of coastal region of Deli Serdang regency mangroves were destroyed.

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 Accuracy assessment of Globeland30: a case study of Ontario,Canada

2021 ◽  
Author(s):  
Rishita Rangarh
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Landsat Imagery ◽  
Global Scale ◽  
Data Set ◽  
Error Matrix ◽  
Successful Model ◽  
Land Cover Data ◽  
Made In

GlobeLand30 is the world’s first 30m high resolution land cover data set (Chen et al. 2014) and has been a successful model of Big-Data mining from a host of Landsat imagery, thereby contributing to and enhancing the existing global geospatial knowledge base (GlobeLand30 2014). As there is a lot of uncertainty and errors in the global land cover data, therefore it becomes very difficult to validate land cover on a global scale. Efforts on validating Globeland30 data have been made in various parts of the world in the past and will continue to be done. The objective of this project is to validate GlobeLand30 data set by carrying out a case study in Ontario, Canada. The adopted methodology for doing validation is by using cell-to-cell benchmarking (Maria et al. 2015), thereby deriving Error Matrix, and its derivatives, which includes overall accuracy, user accuracy, producer accuracy and kappa coefficient. The results show that an overall accuracy of 84.14% is obtained for GlobeLand30 data with consideration of shadows, which is relatively a high percentage number indicating that the GlobeLand30 data classification is highly accurate for Ontario, Canada. Keywords: land cover; GlobeLand30; accuracy assessment; Ontario

scholarly journals Assessing Land Cover Changes and CO2 Emissions in Tropical Forests, 1998-2016: A Case Study of the Sungai Wain Protection Forest

2019 ◽  
Vol 28 (5) ◽  
pp. 3597-3604
Author(s):  
Askar Askar ◽  
Narissara Nuthammachot ◽  
Tri Sayektiningsih ◽  
Hermudananto Hermudananto
Keyword(s):  
Land Cover ◽  
Co2 Emissions ◽  
Tropical Forests ◽  
Land Cover Changes ◽  
Protection Forest

scholarly journals Reintroducing species when threats still exist: assessing the suitability of contemporary landscapes for island endemics

Oryx ◽  
2020 ◽  
pp. 1-8
Author(s):  
Nicole Frances Angeli ◽  
Lee Austin Fitzgerald
Keyword(s):  
Land Cover ◽  
Spatial Configuration ◽  
Virgin Islands ◽  
Suitable Habitat ◽  
Land Cover Types ◽  
Habitat Conversion ◽  
Binomial Mixture ◽  
Herpestes Auropunctatus ◽  
Statistical Similarity

Abstract Reintroducing species into landscapes with persistent threats is a conservation challenge. Although historic threats may not be eliminated, they should be understood in the context of contemporary landscapes. Regenerating landscapes often contain newly emergent habitat, creating opportunities for reintroductions. The Endangered St Croix ground lizard Pholidoscelis polops was extirpated from the main island of St Croix, U.S. Virgin Islands, as a result of habitat conversion to agriculture and predation by the small Indian mongoose Herpestes auropunctatus. The species survived on two small cays and was later translocated to two islands. Since the 1950s, new land-cover types have emerged on St Croix, creating a matrix of suitable habitat throughout the island. Here we examined whether the new habitat is sufficient for a successful reintroduction of the St Croix ground lizard, utilizing three complementary approaches. Firstly, we compared a map from 1750 to the current landscape of St Croix and found statistical similarity of land-cover types. Secondly, we determined habitat suitability based on a binomial mixture population model developed as part of the programme monitoring the largest extant population of the St Croix ground lizard. We estimated the habitat to be sufficient for > 142,000 lizards to inhabit St Croix. Thirdly, we prioritized potential reintroduction sites and planned for reintroductions to take place during 2020–2023. Our case study demonstrates how changing landscapes alter the spatial configuration of threats to species, which can create opportunities for reintroduction. Presuming that areas of degraded habitat may never again be habitable could fail to consider how regenerating landscapes can support species recovery. When contemporary landscapes are taken into account, opportunities for reintroducing threatened species can emerge.

scholarly journals PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 91-93 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Sentinel 2

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

scholarly journals LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 13-16 ◽  
Author(s):  
M. Cavur ◽  
H. S. Duzgun ◽  
S. Kemec ◽  
D. C. Demirkan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Remotely Sensed Data ◽  
Vector Machines ◽  
Kappa Value ◽  
Sentinel 2

<p><strong>Abstract.</strong> Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.</p>

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