Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia

Sari, Inggit Lolita; Weston, Christopher J.; Newnham, Glenn; Volkova, Liubov

doi:10.3390/rs13081446

Cited by 25 publications

(15 citation statements)

References 40 publications

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“…Specifically, the method developed improved the ability to discriminate between native forest and oil palm and rubber plantations. Previous studies showed that the LCCA forest and nonforest map, which uses optical Landsat images, had lower overall accuracy (73-77%) due to misclassification of plantations [31]. The integration of Landsat-8 OLI and Sentinel-1 C-band increased land cover classification accuracy, resulting in an overall accuracy of 92%, with an increase in accuracy for the four classes (native forest, oil palm plantation, rubber plantation, and non-forest).…”

Section: Discussionmentioning

confidence: 98%

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Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

et al. 2021

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Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion.

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Section: Discussionmentioning

confidence: 98%

“…Kalimantan also has poor plantation extent mapping, including misclassification of oil palm and rubber plantations as native forest [13,31].…”

Section: Introductionmentioning

confidence: 99%

Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

et al. 2021

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“…At present, the most commonly used evaluation method is to use the true state of the image combined with field survey data for visual interpretation. Due to the large-scale planting areas and complex planting conditions, this type of evaluation method is limited by manpower and material resources [40,41]. In this study, the confusion matrix was used to test the accuracy of the classification results, and the total accuracy and kappa coefficient were used to evaluate six supervised classification methods.…”

Section: Evaluation Indexmentioning

confidence: 99%

Identification of Male and Female Parents for Hybrid Rice Seed Production Using UAV-Based Multispectral Imagery

Liu

Xiao³

et al. 2022

Agriculture

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Identifying and extracting male and female parent of hybrid rice and then accurately judging the spikelet flowering of male parents is the basis of hybrid rice pollination. Currently, male parent flowering information extraction for hybrid rice is basically obtained by manual recognition. In this study, remote sensing images of parental rice fields were obtained with a multispectral camera carried by a UAV (Umanned Aerial Vehicle). Six kinds of visible light vegetation indices and four kinds of multispectral vegetation indices, together with two classification methods, pixel-based supervised classification and sample-based object-oriented classification, were applied to identify the male and female parents of hybrid rice, after which the accuracies of the methods were compared. The results showed that the visible vegetation index had a better effect in pixel-based supervised classification. The kappa coefficient of ExGR (Excess Green minus Excess Red index) classification was 0.9256 and the total accuracy was 0.9552. The extraction accuracy was higher than that of the other vegetation indices and object-oriented classification. In pixel-based supervised classification, the maximum likelihood method achieved the highest identification accuracy and shortest calculation time. Taking the remote sensing images obtained with a UAV as a data source, maximum likelihood supervised classification based on ExGR index can more effectively and quickly identify the field information of male and female parents of hybrid rice so as to provide a reference for determining optimal pollination timing for hybrid rice in large-scale seed production farms.

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“…Remote sensing has been used for several decades to monitor forest extent and change, drawing on optical images such as those from the Landsat series of satellites (Romijn et al, 2015;Sari et al, 2021a). However, intense cloud cover limits the ability of optical images to detect temporal changes in vegetation cover and types in tropical countries (Massey et al, 2018;Sari et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Mapping Land Cover Time Series Using Landsat-8 and Sentinel-1 in South Kalimantan

Sari

Weston²,

Newnham³

et al. 2023

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

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Abstract. Remote sensing has been widely used for forest monitoring. However, most forest monitoring systems largely rely on optical images that limit temporal analyses due to cloud cover, particularly in the tropics. The current study used integration of optical Landsat-8 and Sentinel-1 SAR to produce individual year land cover classification maps for Kalimantan, Indonesia that differentiate between native forest and tree plantations, such as oil palm and rubber. We applied a Bayesian network to produce a time series of land cover classification that improved accuracy of individual year land cover maps. Accuracy assessment using a confusion matrix showed that final map had overall accuracy of 90%, while user's and producer's accuracy for each land cover class was above 85%, except non–forest, which had 76% producer's accuracy due to errors in the classification between young rubber plantations and non–forest. Improved maps will support Indonesia's national forest monitoring system and sustainable forest management.

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Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia

Cited by 25 publications

References 40 publications

Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

Identification of Male and Female Parents for Hybrid Rice Seed Production Using UAV-Based Multispectral Imagery

Mapping Land Cover Time Series Using Landsat-8 and Sentinel-1 in South Kalimantan

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