scholarly journals Delimitation and classification of coastal wetlands: Implications for the environmental management of the Colombian Continental Caribbean

2021 ◽  
Vol 50 (1) ◽  
pp. 121-140
Author(s):  
Santiago Millán ◽  
Jenny Rodríguez ◽  
Paula Sierra
Keyword(s):  
Coastal Wetlands ◽  
Flood Frequency ◽  
Water Bodies ◽  
Landsat 8 ◽  
Adaptation To Climate Change ◽  
Alos Palsar ◽  
Ndvi Index ◽  
Consultation Model ◽  
Uncertainty Models

This article describes the cartographic layer construction process of Colombian Caribbean coastal wetlands at a scale of 1:100,000 and the results obtained in terms of their quantification and typing. Two cartographic layers were constructed and subsequently joined, one of the permanent water bodies and another of temporary water bodies and associated coverages. The layers were generated by multitemporal analysis of 45 Landsat 8-OLI satellite images, based on the NDVI index, uncertainty models by superposition of cartographic attributes, and a flood frequency consultation model on ALOS PALSAR 1 images. As a result, 576,279 ha of coastal wetlands were delimited (1.9 % of total wetlands in Colombia), of which 20.4 % are within protected areas. The cartographic legend makes it possible to typify wetlands based on the coverage and temporality of water bodies; discriminates permanent wetlands (42.7 %) with five categories and temporary wetlands (57.3 %) with 15 categories, mostly distributed in seven large complexes. This study is the first description of the colombian Caribbean coastal wetlands based on a cartographic construction, is methodologically replicable, and will support decision-making in the planning of colombian Caribbean coastal areas, especially for risk management and ecosystem-based adaptation to climate change.

scholarly journals Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images

2019 ◽  
Vol 11 (16) ◽  
pp. 1927 ◽  
Author(s):  
Xiaoxue Wang ◽  
Xiangwei Gao ◽  
Yuanzhi Zhang ◽  
Xianyun Fei ◽  
Zhou Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Classification Accuracy ◽  
Coastal Wetlands ◽  
Land Cover Classification ◽  
Support Vector ◽  
Small Scale ◽  
Landsat 8

Wetlands are one of the world’s most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang—Worldview-2 and Landsat-8 images—were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

Identification of Agricultural Management Zones Through Clustering Algorithms with Thermal and Multispectral Satellite Imagery

2017 ◽  
Vol 25 (Suppl. 1) ◽  
pp. 121-140
Author(s):  
R. B. Arango ◽  
A. M. Campos ◽  
E. F. Combarro ◽  
E. R. Canas ◽  
I. Díaz
Keyword(s):  
Satellite Imagery ◽  
Precision Agriculture ◽  
Clustering Algorithms ◽  
Economic Benefits ◽  
Agricultural Management ◽  
Landsat 8 ◽  
Special Equipment ◽  
Management Zones ◽  
Ndvi Index ◽  
Cost Efficient

Precision Agriculture entails the appropriate management of the inherent variability of soil and crops, resulting in an increase of economic benefits and a reduction of environmental impact. However, site-specific treatments require maps of the soil variability to identify areas of land that share similar properties. In order to produce these maps, we propose a cost-efficient method that combines clustering algorithms with publicly available satellite imagery. The method does not require exploring the parcels with any special equipment or taking samples of the soil for laboratory analysis. The proposed method was tested in a case study for three vineyard parcels with topographical dissimilarities. The study compares different spectral and thermal bands from the Landsat 8 satellite as well as vegetation and moisture indices to determine which one produces the best clustering. The experimental results seem promising for identification of agricultural management zones. The findings suggest that thermal bands produce better clustering than those based on the NDVI index.

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning

2021 ◽  
Vol 18 (3) ◽  
pp. 1132
Author(s):  
Qijiao Xie ◽  
Qi Sun
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Urban Planning ◽  
Land Cover ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Water Bodies ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
The Impact

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

2018 ◽  
Vol 10 (11) ◽  
pp. 1751 ◽  
Author(s):  
Abderrahim Nemmaoui ◽  
Manuel A. Aguilar ◽  
Fernando J. Aguilar ◽  
Antonio Novelli ◽  
Andrés García Lorca
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Free Access ◽  
Landsat 8 ◽  
Horticultural Crops ◽  
Command Line Tool ◽  
Multi Temporal ◽  
Tree Models ◽  
Sentinel 2A

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

Sign in / Sign up

Export Citation Format

Share Document