Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India

Mishra, Prabuddh Kumar; Rai, Aman; Chand, Suresh

doi:10.1016/j.ejrs.2019.02.001

Cited by 190 publications

(116 citation statements)

References 43 publications

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“…The kappa statistics value is a measure of the agreement between classification and reference data (Wang et al 2012;Mishra et al 2019). Landis and Koch (1977) ranked the kappa values, ranging from − 1 to 1, into three groups: (1) greater than 0.80 represented strong agreement (2) between 0.40 and 0.80 represented moderate agreement, and (3) less than 0.40 represented poor agreement between the classification and reference data.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

2020

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Background: Forest plays an important role in climate regulation and carbon sequestration. Komto Forest is one of the remnant natural forests found in Guto Gida district of East Wollega zone, Ethiopia, has been supporting the local community for construction, energy and household furniture. Currently, influenced by land use land cover change (LULCC), this forest has been declining at an alarming rate. Detecting LULCC and understanding the driving forces has important for supporting decision making processes. We examine variation in forest cover dynamics over the period 1991-2012 using Landsat TM image of 1991, ETM + of 2002 and OLI-TIRS of 2019. Results: The LULCC detection results show that a dramatic increase of agricultural land from (24.78%) in 1991 to (33.5%) in 2019 with annual expansion rate (23.68%) per annum, where forest cover declined by 20.1% in 1991 and 37.38% in 2019 with annual decreasing rate of 4.18% per annum. Our finding indicates the increment of agricultural land, grassland, and settlement, while the dense and open forest cover shows a declining trend. The declining of forest coverage is likely to cause unpleasant environment and affects human wellbeing. Conclusions: The massive declined in forest cover change are often associated with agricultural expansion in the periphery of the forest. Timber exploitation and charcoal production are other problems that contribute for the declining of forest coverage. Overall, our results suggest the need of participatory forest management and public awareness creation to sustain the Komto remnant forest.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

2020

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“…In this study, we have improved the accuracy of the LULC classification based on the mosaic cloud-free Landsat 8 satellite imagery that can be obtained from GEE, and its popular method for filling gaps in cloudy images using median metrics or the temporal aggregation method [ 36 ]. ML, RF, and SVM, which have been widely used for image classification [ 36 , 39 , 40 , 42 , 43 ], were employed as methods to classify LULC in the study area. We chose the Citarum, Ciliwung, and Cisadane watersheds as test case study areas to be able to understand and implement the proposed method; these areas are included in the 15 national priority watersheds in Indonesia.…”

Section: Discussionmentioning

confidence: 99%

“…The approach assumes that the probability of the model class distribution is multivariate normal. In detail, the maximum likelihood classifier formulation is presented in equations ( 1 ) and ( 2 ) [ 1 , 2 , 7 , 38 – 40 ]:

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Section: Methodsmentioning

confidence: 99%

Improvement in the Accuracy of the Postclassification of Land Use and Land Cover Using Landsat 8 Data Based on the Majority of Segment-Based Filtering Approach

Yulianto

Nugroho

Chulafak

et al. 2021

The Scientific World Journal

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Improvement in the accuracy of the postclassification of land use and land cover (LULC) is important to fulfil the need for the rapid mapping of LULC that can describe the changing conditions of phenomena resulting from interactions between humans and the environment. This study proposes the majority of segment-based filtering (MaSegFil) as an approach that can be used for spatial filters of supervised digital classification results. Three digital classification approaches, namely, maximum likelihood (ML), random forest (RF), and the support vector machine (SVM), were applied to test the improvement in the accuracy of LULC postclassification using the MaSegFil approach, based on annual cloud-free Landsat 8 satellite imagery data from 2019. The results of the accuracy assessment for the ML, RF, and SVM classifications before implementing the MaSegFil approach were 73.6%, 77.7%, and 77.5%, respectively. In addition, after using this approach, which was able to reduce pixel noise from the results of the ML, RF, and SVM classifications, there were increases in the accuracy of 81.7%, 85.2%, and 84.3%, respectively. Furthermore, the method that has the best accuracy RF classifier was applied to several national priority watershed locations in Indonesia. The results show that the use of the MaSegFil approach implemented on these watersheds to classify LULC had a variation in overall accuracy ranging from 83.28% to 89.76% and an accuracy improvement of 6.41% to 15.83%.

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“…blue, green, red, and near-infrared). Because of its high spatial resolution, recent studies have used PlanetScope images in validating land use and land cover maps generated using moderate-resolution images (Ha, Tuohy, Irwin, & Tuan, 2018;Mishra, Rai, & Rai, 2019). A fishnet with grid size of 300 m x 300 m was generated from the pixels of the Sentinel-3 image.…”

Section: Planetscope Imagerymentioning

confidence: 99%

Linear Spectral Unmixing of Sentinel-3 Imagery for Urban Land Cover - Land Surface Temperature (Lst) Analysis: A Case Study of Metro Manila, Philippines

Cruz

Blanco

Babaan

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission IVKEY WORDS: urbanization, urban heat island, land cover fractions, land surface temperature, remote sensing ABSTRACT:The advancement of remote sensing technologies is a huge advantage in various environmental applications including the monitoring of the rapid development in an urban area. This study aims to estimate the composition of the different classes (vegetation, impervious surfaces, soil) in Metro Manila, Philippines using a 300-meter spatial resolution Sentinel-3 Ocean and Land Colour Instrument image. The relationship between these land cover fractions with the spatial distribution of land surface temperature at this scale is evaluated. Sentinel-3 image has a higher spectral resolution (i.e. 21 bands), as compared with other Landsat and Sentinel missions, which is a requirement for an accurate cover mapping. Linear Spectral Unmixing (LSU), a subpixel classification method, was employed in identifying the fractional components in the image based on their spectral characteristics. Field survey using spectroradiometer was conducted to acquire spectral signatures of an impervious surface, vegetation, and soil which were used as the endmembers in the unmixing process. To assess the accuracy of the resulting vegetation fractional image, this was compared with a separate land cover pixel-based classification result using a 3-meter high spatial resolution PlanetScope image and with another vegetation index product of Sentinel-3. The results indicate that the recently available Sentinel-3 image can accurately estimate vegetation fraction with R 2 = 0.84 and 0.99, respectively. In addition, the land surface temperature (LST) retrieved from Climate Engine is negatively correlated with the vegetation fraction cover (R 2 = 0.81) and positively correlated with the impervious surface fraction cover (R 2 = 0.66). Soil, on the other hand, has no correlation with the LST.

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Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India

Cited by 190 publications

References 43 publications

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

Improvement in the Accuracy of the Postclassification of Land Use and Land Cover Using Landsat 8 Data Based on the Majority of Segment-Based Filtering Approach

Linear Spectral Unmixing of Sentinel-3 Imagery for Urban Land Cover - Land Surface Temperature (Lst) Analysis: A Case Study of Metro Manila, Philippines

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