Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves

Valderrama-Landeros, Luis; Flores-Verdugo, Francisco; Rodríguez-Sobreyra, Ranulfo; Kovacs, John M.; Flores‐de‐Santiago, Francisco

doi:10.1016/j.jenvman.2020.111617

Cited by 43 publications

(17 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of the planet's forests with different levels of biodiversity, species composition, and the degradation level is carried out around the world using optical satellite images and new remote sensing methods. For instance, the studies of tropical forests [24,25] of Europe [26] and eastern countries [27] involve the specification of the areas of land covered by forest vegetation.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Forests of the Natural Park „Kandry-Kul” using the Earth Remote Sensing Data

Sultanova¹,

Odintsov²,

Baiturina³

et al. 2022

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

The study aims to substantiate the monitoring of protective forests actively used in recreational activities. Monitoring of the plantings state was carried out based on landscape and forest inventory and using geographic information systems, in particular Landsat satellite data allowing to cover the entire object area simultaneously and analyze the state of the object plantings over 30 years. The research involved the plantings of the natural park "Kandry-kul" located on the Belebeevskaya upland of the Southern Urals. Ground investigations and the results of processing satellite images allowed the conclusion that natural and artificial mixed coniferous-deciduous forests are basic. Artificial stands are mainly represented by young pine trees of IA-II growth class. Their sanitary and hygienic assessment is average since silver birch stands contain dropsy. Few landscaped and planted park areas and poorly developed pathway network have a negative impact on the plant community structure and reforestation. Thus, using technical capabilities of satellite systems working based on wide band electromagnetic radiation and ground inventory studies makes it possible to obtain data on the forest ecosystems state with high territorial and temporal resolution and use them when monitoring recreational facilities to solve forestry problems.

show abstract

Section: Resultsmentioning

confidence: 99%

Assessment of Forests of the Natural Park „Kandry-Kul” using the Earth Remote Sensing Data

Sultanova¹,

Odintsov²,

Baiturina³

et al. 2022

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…GPC out-performed widely used MLCAs such as RF, support vector regression, or neural networks. Each of these classifiers were evaluated as top-performing against other common classifiers in earlier studies [ 30 , 90 , 91 ]. The outstanding accuracy reached by GPC is remarkable; as in this study, we did not aim for detecting the usual thematic land covers with distinct spectral behaviors (e.g., water, land, vegetation).…”

Section: Discussionmentioning

confidence: 99%

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

et al. 2022

View full text Add to dashboard Cite

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO’s software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land–grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO’s MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.

show abstract

“…The images were downloaded freely from the European Space Agency 1 and the United States Geological Survey (USGS 2 ). Sentinel-2, with a state-of-the-art sensor of 13 spectral bands and a 2∼5 days re-entry cycle, makes it possible to capture detailed spatiotemporal changes in a vegetation community (Drusch et al, 2012;Valderrama-Landeros et al, 2020). Images acquired in 1987-1990, 1999-2000, 2009-2010, and 2018 were used for the years 1990, 2000, 2010, and 2018, respectively, for high cloud coverage of some images (Supplementary Table 1).…”

Section: Data Acquisitionmentioning

confidence: 99%

Mangrove Loss and Gain in a Densely Populated Urban Estuary: Lessons From the Guangdong-Hong Kong-Macao Greater Bay Area

et al. 2021

View full text Add to dashboard Cite

Understanding the recent changes in mangrove adjacent to mega-cities is critical for conservation, management, and policymaking in coastal zones with fast population growth and global change. Here we investigated mangrove area changes in one of the world’s largest urban areas near the main estuaries in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Mangrove area changes are quantified for the period 1990–2018 by analyzing multiple sources of satellite images by classification algorithms. We found that estuarine mangrove area dynamics are driven by human actions and are contrasting between these two periods. (1) During 1990–2000, the estuarine mangrove area approximately decreased from 11.5 to 6.9 km2, among which the bulk part was transformed into aquaculture ponds (41.1%) and built-up area (29.9%). (2) During 2000–2018, the estuarine mangrove area rapidly increased to 18.3 km2 resulting from the protection and restoration efforts. Proportions of mangrove occurring in nature reserves increased from 37.5% in 1990 to >80% in the 2000s. Two major mangrove expansion ways, natural establishment (NE, in protected areas without any human interference) and human afforestation (HA) accounted almost equally (53.1 and 46.9%) for the gained estuarine mangrove area during 2010–2018. A future projection according to the current mangrove increasing rate suggests that all the low-lying land that is theoretically suitable for mangrove afforestation would be used up by 2060. Although afforestation has contributed to important gains in mangrove quantity, we highlight that it may also imply decreased habitat quality. It has resulted in a great occupation of high tidal mudflats and a loss of their valuable ecosystem services, and it may lead to spreading of non-native species, e.g., Sonneratia apetala, used in afforestation programs. Future restoration approaches should adopt more eco-friendly strategies, like reversing (abandoned) aquaculture ponds to native mangrove forests. Knowledge obtained from the GBA in this study may be also instrumental to ecological restorations for mangrove forests in other urbanized estuaries.

show abstract

Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves

Cited by 43 publications

References 54 publications

Assessment of Forests of the Natural Park „Kandry-Kul” using the Earth Remote Sensing Data

Assessment of Forests of the Natural Park „Kandry-Kul” using the Earth Remote Sensing Data

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Mangrove Loss and Gain in a Densely Populated Urban Estuary: Lessons From the Guangdong-Hong Kong-Macao Greater Bay Area

Contact Info

Product

Resources

About