Determining the Type and Starting Time of Land Cover and Land Use Change in Southern Ghana Based on Discrete Analysis of Dense Landsat Image Time Series

Shih, Hsiao-chien; Weeks, John R.; Coulter, Lloyd L.

doi:10.1109/jstars.2015.2504371

Cited by 17 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) NDBI; (4) NIR; (5) SWIR 1 or 2; (6) SWIR 2 or 1; (7) Red; (8) MSAVI; (9) Green; (10) Blue; (11) Ultra Blue. These observations are, in general, in accordance with the recent related literature, where spectral indices are widely used and proven to contribute significantly in remote sensing classification tasks [6,8,9,47,71,72] since their calculation reduces variability and enhances the separability of terrain classes [18]. NIR and SWIR bands as well as the Red one follow in high ranking, as expected, since they are specifically designed on these wavelength ranges to enhance the discrimination of different vegetation types, highlighting moisture and water content while the SWIR bands also provide thin cloud penetration capabilities [73].…”

Section: Assessing the Contribution Of Spectral Featuressupporting

confidence: 91%

“…Spectral features, in most cases referring to sensor multispectral bands but also spectral indices, have been widely used as the main set of input features for land cover classification in the recent literature [8,36,[47][48][49]. Based on our previous research efforts [43,44] and on the related bibliography, the seven atmospherically corrected L8 spectral bands were used, along with four selected spectral indices generally addressing vegetation, water and man-made regions.…”

Section: Classification Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover

et al. 2018

View full text Add to dashboard Cite

Detailed, accurate and frequent land cover mapping is a prerequisite for several important geospatial applications and the fulfilment of current sustainable development goals. This paper introduces a methodology for the classification of annual high-resolution satellite data into several detailed land cover classes. In particular, a nomenclature with 27 different classes was introduced based on CORINE Land Cover (CLC) Level-3 categories and further analysing various crop types. Without employing cloud masks and/or interpolation procedures, we formed experimental datasets of Landsat-8 (L8) images with gradually increased cloud cover in order to assess the influence of cloud presence on the reference data and the resulting classification accuracy. The performance of shallow kernel-based and deep patch-based machine learning classification frameworks was evaluated. Quantitatively, the resulting overall accuracy rates differed within a range of less than 3%; however, maps produced based on Support Vector Machines (SVM) were more accurate across class boundaries and the respective framework was less computationally expensive compared to the applied patch-based deep Convolutional Neural Network (CNN). Further experimental results and analysis indicated that employing all multitemporal images with up to 30% cloud cover delivered relatively higher overall accuracy rates as well as the highest per-class accuracy rates. Moreover, by selecting 70% of the top-ranked features after applying a feature selection strategy, slightly higher accuracy rates were achieved. A detailed discussion of the quantitative and qualitative evaluation outcomes further elaborates on the performance of all considered classes and highlights different aspects of their spectral behaviour and separability.

show abstract

Section: Assessing the Contribution Of Spectral Featuressupporting

confidence: 91%

Section: Classification Featuresmentioning

confidence: 99%

Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The classification features selected in this paper included spectral bands of the Landsat 8 OLI images, NDVI, and phenological variables. The selected spectral bands were Blue, Green, Red, Near Infrared-NIR, Shortwave Infrared-SWIR 1, and Shortwave Infrared-SWIR 2, which have demonstrated their potential in land-cover classification [82][83][84]. The NDVI was derived from Landsat 8 spectral images on 12 June 2016, 30 July 2016, and 16 September 2016.…”

Section: Classification Featuresmentioning

confidence: 99%

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

et al. 2018

View full text Add to dashboard Cite

Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.

show abstract

“…The changes in the climate of the earth such as the increase in the daily earth surface temperature and the need for monitoring its impacts on the earth's surface call for environmental monitoring approaches. Land cover is known as one of the fundamental terrestrial climate variables [1,2]. In the land cover mapping, detailed land cover maps are an essential input for various scientific associations working on climate change investigations, sustainable development, geomorphology, and social knowledge management of natural resources, and monitoring the agricultural lands.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

Jamali

2019

SN Appl. Sci.

View full text Add to dashboard Cite

Land use land cover change mapping has been used for monitoring environmental changes as an essential factor to study on the earth's surface land cover in the field of climate change phenomena such as floods and droughts. Remote sensing images have been suggested to present inexpensive and fine-scale data offering multi-temporal coverage. This tool is useful in the field of environmental monitoring, land-cover mapping, and urban planning. This study aims to evaluate eight machine learning algorithms for image classification implemented in WEKA and R programming language. Firstly, Landsat 8 OLI/TIRS Level-2 images based on eight machine learning techniques including Random Forest, Decision Table, DTNB, J48, Lazy IBK, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge), and Simple Logistic are classified. Then, obtained results are compared in term of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for land use land cover mapping. Among the eight machine learning algorithms used for image classification based on the training and test dataset, NN ge classifier is ranked first with values of 100, 0, and 0 for Overall Accuracy, Mean Absolute Error and Root Mean Squared Error respectively. All machine learning algorithms had an Overall Accuracy of more than 99% for the training dataset. On the other hand, for the test dataset, J48 and DTNB algorithms had the worst performance with values of 88.1188 and 76.9802 respectively for the Overall Accuracy.

show abstract

Determining the Type and Starting Time of Land Cover and Land Use Change in Southern Ghana Based on Discrete Analysis of Dense Landsat Image Time Series

Cited by 17 publications

References 13 publications

Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover

Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

Contact Info

Product

Resources

About