The Suitability of PlanetScope Imagery for Mapping Rubber Plantations

Cui, Bin; Huang, Wenjiang; Ye, Huichun; Chen, Quanxi

doi:10.3390/rs14051061

Cited by 14 publications

(8 citation statements)

References 39 publications

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“…As the most commonly used evaluation method in image classification studies, this method adopted a standard accuracy evaluation matrix with n rows and n columns. The overall accuracy (OA) and Kappa coefficient are evaluation indices for the overall classification effect, and the user accuracy (UA) and producer accuracy (PA) are evaluation indices for the classification effect of different vegetation types [ 38 ].

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Section: Methodsmentioning

confidence: 99%

Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

Fang

Zhu

et al. 2023

Sensors

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Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.

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…”

Section: Methodsmentioning

confidence: 99%

Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

Fang

Zhu

et al. 2023

Sensors

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“…In this research, the basis for mapping allergenic tree species is commercial Plan-etScope (PS) data. The imagery used in this analysis was obtained from Planet Inc., the global leader in satellite constellations, which operates over 170 orbiting satellites [23]. The forest vegetation in the Zagreb county is characterized primarily by forests of pedunculate oak, narrow-leashed ash, black alder, willows, and poplars [34].…”

Section: Planetscope Satellite Imagery and Preprocessingmentioning

confidence: 99%

“…In this research, the basis for mapping allergenic tree species is commercial PlanetScope (PS) data. The imagery used in this analysis was obtained from Planet Inc., the global leader in satellite constellations, which operates over 170 orbiting satellites [23]. Many of these satellites are in a sun-synchronous orbit with 4-to 8-band radiance products at 3-4 m spatial resolution.…”

Section: Planetscope Satellite Imagery and Preprocessingmentioning

confidence: 99%

“…To overcome these issues, PlanetScope (PS) satellite imagery [22] offers significant benefits over previous RS data due to its daily temporal resolution and 3 m spatial resolution, providing an effective solution to overcome existing issues. Hence, PS data has been successfully used, for example, in mapping rubber plantations [23], snow-covered areas in forested mountain ecosystems [24], mapping of lava flows in barren regions [25], and land-cover classification in urban areas [20]. Furthermore, in 2019, PlanetScope announced the general commercial availability of the next generation of PS Monitoring product, which includes eight spectral bands, instead of previously offering four bands [26].…”

Section: Introductionmentioning

confidence: 99%

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Mapping of Allergenic Tree Species in Highly Urbanized Area Using PlanetScope Imagery—A Case Study of Zagreb, Croatia

Gašparović

Dobrinić

Pilaš

2023

Forests

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Mapping and identifying allergenic tree species in densely urbanized regions is vital for understanding their distribution and prevalence. However, accurately detecting individual allergenic tree species in urban green spaces remains challenging due to their smaller site and patchiness. To overcome these issues, PlanetScope (PS) satellite imagery offers significant benefits compared with moderate or high-resolution RS imagery due to its daily temporal resolution and 3 m spatial resolution. Therefore, the primary objectives of this research were to: assess the feasibility of mapping allergenic tree species in the highly urbanized area using high-resolution PS imagery; evaluate and compare the performance of the most important machine learning and feature selection methods for accurate detection of individual allergenic tree species. The research incorporated three classification scenarios based on ground truth data: The first scenario (CS1) used single-date PS imagery with vegetation indices (VI), while the second and third scenarios (CS2 and CS3) used multitemporal PS imagery with VI, and GLCM and VI, respectively. The study demonstrated the feasibility of using multitemporal eight-band PlanetScope imagery to detect allergenic tree species, with the XGB method outperforming others with an overall accuracy of 73.13% in CS3. However, the classification accuracy varied between the scenarios and species, revealing limitations including the inherent heterogeneity of urban green spaces. Future research should integrate high-resolution satellite imagery with aerial photography or LiDAR data along with deep learning methods. This approach has the potential to classify dominant tree species in highly complex urban environments with increased accuracy, which is essential for urban planning and public health.

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“…Omission Error (OE) and Commission Error (CE) are employed to identify the distribution of errors in the classification of smoke mapping from forest and land fires. The following equations are used to calculate OA, OE, and CE29 : the number of classes, N is the total number of samples, N_i and N_j are values related to elements (i,j) in the confusion matrix, N_kk is the number of samples correctly classified in class k, N_(k+) is the total number of samples in class k, and N_(+k) is the total number of samples classified as class k.…”

mentioning

confidence: 99%

Identification of smoke peatland fire using remote sensing data based on machine learning

Rahmi,

Nugroho,

Sofan

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Smoke information serves as a crucial marker for detecting peatland fires. Practically, smoke identification utilizing remote sensing satellites, based on visual interpretation techniques, proves inefficient in processing time and high subjectivity. The application of machine learning technology for smoke detection remains limited in the tropics, especially in peatland areas. This study aims to identify smoke from peatland fires using machine learning techniques. The dataset comprises Visible Infrared Imaging Radiometer Suite (VIIRS) images and the VIIRS's hotspots on September 11 st 2019, coinciding with a major peat fire incident in Indonesia. Various machine learning techniques were tested, encompassing Random Forest (RF), Classification and Regression Trees (CART), Support Vector Machine (SVM), Naive Bayes (NB), and Gradient Tree Boost (GTB). Object classification includes thin smoke, thick smoke, clouds/smoke, clouds, vegetation, water body, and bare land. The accuracy assessment involved both qualitative assessment based on true-color images and quantitative evaluation through the 70:30 sample splitting accuracy assessment method. The analysis of spectral distance for the seven object types reveals that band 5, 10, and 12 exhibit the highest value. Successfully identification of peatland fire smoke is achieved via supervised machine learning, particularly logic-based algorithms (RF, CART, GTB) and support vector machine methods (SVM), while statistical method (NB) yield comparatively less success. Qualitative validation using true-color VIIRS image indicates strong alignment between thick smoke and the RF all-bands approach. Quantitative validation, based on accuracy assessment with 1531 samples, establishes SVM as the most accurate method, boasting an overall accuracy of 0.93, followed by GTB at 0.91, RF at 0.90, and CART at 0.88.

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The Suitability of PlanetScope Imagery for Mapping Rubber Plantations

Cited by 14 publications

References 39 publications

Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

Mapping of Allergenic Tree Species in Highly Urbanized Area Using PlanetScope Imagery—A Case Study of Zagreb, Croatia

Identification of smoke peatland fire using remote sensing data based on machine learning

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