Hierarchical classification of land use types using multiple vegetation indices to measure the effects of urbanization

Shishir, Sharmin; Tsuyuzaki, Shiro

doi:10.1007/s10661-018-6714-3

Cited by 13 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of the variables in the RF classifier was demonstrated. Compared to previous studies, which primarily relied on NDVI and EVI, we included additional indices to reduce soil and atmospheric effects, improving accuracy [45][46][47][48][49][50]. These VIs enhanced land cover classification for forests and other land types.…”

Section: Characteristics Of Clear-cut Areasmentioning

confidence: 99%

Detection and Analysis of Forest Clear-Cutting Activities Using Sentinel-2 and Random Forest Classification: A Case Study on Chungcheongnam-do, Republic of Korea

Choi,

Lee,

Park

et al. 2024

Forests

View full text Add to dashboard Cite

This study provides the methodology for the development of sustainable forest management activities and systematic strategies using national spatial data, satellite imagery, and a random forest machine learning classifier. This study conducts a regional province-scale approach that can be used to analyze forest clear-cutting in South Korea; we focused on the Chungcheongnam-do region. Based on spatial information from digital forestry data, Sentinel-2 satellite imagery, random forest (RF) classifier, and digital forest-type maps (DFTMs), we detected and analyzed the characteristics of clear-cut areas. We identified forest clear-cut areas (accounting for 2.48% of the total forest area). The methodology integrates various vegetation indices and the RF classifier to ensure the effective detection of clear-cut areas at the provincial level with an accuracy of 92.8%. Specific leaf area vegetation index (SLAVI) was determined as the most important factor for accurately detecting clear-cut areas. Moreover, using a DFTM, we analyzed clear-cutting characteristics in different forest types (including private, national, natural, and planted forests), along with age class and diameter-at-breast-height class. Our method can serve as a basis for forest management and monitoring by analyzing tree-cutting trends in countries with forest areas, such as Republic of Korea.

show abstract

Section: Characteristics Of Clear-cut Areasmentioning

confidence: 99%

Detection and Analysis of Forest Clear-Cutting Activities Using Sentinel-2 and Random Forest Classification: A Case Study on Chungcheongnam-do, Republic of Korea

Choi,

Lee,

Park

et al. 2024

Forests

View full text Add to dashboard Cite

show abstract

“…Some studies favor the use of multiple indices and advanced classification techniques (RandomForest, Boosting, DecisionTree, etc.) [4,[20][21][22][23][24]. Another study has proposed to optimize the weights in an NDVI equation form based on a genetic algorithm [25] but does not optimize the equation forms.…”

Section: Introductionmentioning

confidence: 99%

DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images

et al. 2021

View full text Add to dashboard Cite

The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evaluated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements.

show abstract

“…Hence, mapping the urban green space gives necessary information for its effective management and protection. Urban studies, city forest inventories, ecology management and other urban planning applications are the primary components for the accurate assessment and classification of tree species [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Green Cover (Tree) Analysis of Chennai Metropolitan Area Using High Resolution Satellite Imagery

Chinnappa¹,

Rajiah²,

Devarajan³

2021

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Sustainable urban environment addresses the issues associated with the urban development due to the increase in land use/land cover (LU/LC) and decrease in forest and agricultural areas. As vegetation plays a major role in ecological, social, health, and economic benefits to the inhabitants of a city, urban greeneries should be monitored through the remotely sensed data with high resolution imagery for a sustainable urban development. The present study investigates the green cover (tree) change of Chennai Metropolitan Area (CMA) through the satellite data of Resourcesat-1 LISS IV and Resourcesat-2 LISS IV. CMA consists of approximately 426 km 2 of urban area and 774 km 2 of sub-urban area with many LU/LC categories like settlements, vegetation and open spaces. Green cover mapping with the accuracy assessment was carried out by using both digital image processing technique and on-screen visual interpretation (Google Earth imagery). Vegetation indices and texture analysis were employed to extract the green cover for 2005 and 2012, to which the change detection was applied. The results reveal that the green cover area of CMA administrative boundary decreased approximately by 1.18% for 2005-2012 and the actual green cover for 2012 reduced by 8.62%. This analysis will be useful to the policy makers of urban development in planning infrastructure development projects and administrators of metropolitan cities, especially Chennai.

show abstract

Hierarchical classification of land use types using multiple vegetation indices to measure the effects of urbanization

Cited by 13 publications

References 56 publications

Detection and Analysis of Forest Clear-Cutting Activities Using Sentinel-2 and Random Forest Classification: A Case Study on Chungcheongnam-do, Republic of Korea

Detection and Analysis of Forest Clear-Cutting Activities Using Sentinel-2 and Random Forest Classification: A Case Study on Chungcheongnam-do, Republic of Korea

DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images

Green Cover (Tree) Analysis of Chennai Metropolitan Area Using High Resolution Satellite Imagery

Contact Info

Product

Resources

About