Understanding multi-temporal urban forest cover using high resolution images

Canetti, Aline; Garrastazú, M. C.; Mattos, P. P. de; Braz, E. M.; Netto, Sylvio Péllico

doi:10.1016/j.ufug.2017.10.020

Cited by 24 publications

(13 citation statements)

References 21 publications

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“…Our findings in the Laoshan Region likely differ from the results for the entire NJNA, due to the variability in topographic, social, and economic factors [47]. Although forest cover maps developed with high-resolution remote sensing imagery with intrinsic differences between image capturing sensors and external differences in model parameter settings and driving factors might improve the accuracy of fragmentation mapping [25,30,40,66], multi-sources, temporal data, and algorithms, including LiDAR, advanced mapping models, in situ inventories, and biodiversity data are required to enhance our understanding of the causes and consequences of forest fragmentation [19,47,66,67]. Nevertheless, monitoring the abrupt changes in forest interior, especially for intact forest, can assist effective conservation efforts in urban forest ecosystems [12,68,69].…”

Section: Uncertainties and Future Workcontrasting

confidence: 58%

Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

Shen

Mao

et al. 2020

Remote Sensing

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Accurate acquisition of the spatiotemporal distribution of urban forests and fragmentation (e.g., interior and intact regions) is of great significance to contributing to the mitigation of climate change and the conservation of habitat biodiversity. However, the spatiotemporal pattern of urban forest cover changes related with the dynamics of interior and intact forests from the present to the future have rarely been characterized. We investigated fragmentation of urban forest cover using satellite observations and simulation models in the Nanjing Laoshan Region of Jiangbei New Area, Jiangsu, China, during 2002–2023. Object-oriented classification-based land cover maps were created to simulate land cover changes using the cellular automation-Markov chain (CA-Markov) model and the state transition simulation modeling. We then quantified the forest cover change by the morphological change detection algorithm and estimated the forest area density-based fragmentation patterns. Their relationships were built through the spatial analysis and statistical methods. Results showed that the overall accuracies of actual land cover maps were approximately 83.75–92.25% (2012–2017). The usefulness of a CA-Markov model for simulating land cover maps was demonstrated. The greatest proportion of forest with a low level of fragmentation was captured along with the decreasing percentage of fragmented area from 81.1% to 64.1% based on high spatial resolution data with the window size of 27 pixels × 27 pixels. The greatest increase in fragmentation (3% from 2016 to 2023) among the changes between intact and fragmented forest was reported. However, intact forest was modeled to have recovered in 2023 and restored to 2002 fragmentation levels. Moreover, we found 58.07 km2 and 0.35 km2 of interior and intact forests have been removed from forest area losses and added from forest area gains. The loss rate of forest interior and intact area exceeded the rate of total forest area loss. However, their approximate ratio (1) implying the loss of forest interior and intact area would have slight fragmentation effects on the remaining forests. This analysis illustrates the achievement of protecting and restoring forest interior; more importantly, excessive human activities in the surrounding area had been avoided. This study provides strategies for future forest conservation and management in large urban regions.

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Section: Uncertainties and Future Workcontrasting

confidence: 58%

Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

Shen

Mao

et al. 2020

Remote Sensing

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“…This is not unexpected as these bands serve the vegetation monitoring purpose of the Sentinel-2A sensor, one of its major applications [44]. As reflectance at these bands is related to vegetation cellular structure [57] and varies with vegetation type, it is useful to use these bands to discriminate urban forest types [68]. We discuss textural features in Section 4.3.…”

Section: Interpretation Of Feature Importancementioning

confidence: 99%

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Zhou

Chen

et al. 2019

Forests

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Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination.

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“…Datasets used in many previous studies on urban forests, in individual cities, were of a very high resolution: Satellite for observation of Earth (SPOT) images with 10 m/2.5 m resolution [46], QuickBird images with a 0.61 m resolution [47], SPOT-5 images with 5 m resolution [21], and multispectral images with 1 m resolution [22]. However, these data were not open access, resulting in the high cost of mapping at the country scale.…”

Section: Datamentioning

confidence: 99%

“…Much effort has been made in measuring urban forest cover; for example, field surveys [18], remote sensing [19][20][21][22], and drone shooting [23] have all been used to gain information on urban forest cover. Among them, remote sensing is efficient and useful in mapping forested urban areas for large scale-maps: Canetti et al [21] used RapidEye and Satellite for observation of Earth (SPOT 5) images to quantify multi-temporal urban forest cover in Araucaria (a city in Brazil), based on the support vector machine algorithms; Chen et al [24] drew the urban green space in the neighborhoods of five Chinese megacities using Google Earth images with the spatial resolution of 0.26 m; Fan et al [22] quantified the tree canopy of Cook County in the United States, using multispectral images with the spatial resolution of 1 m from the National Agriculture Imagery Program and Light Detecting and Ranging data. However, these explorations mainly focus on a single city, and there are, at present, no data on Chinese urban forest cover with a high spatial resolution at the national scale.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 Images with Google Earth Engine

Duan

Tan

Guo

et al. 2019

Forests

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Urban forests are vitally important for sustainable urban development and the well-being of urban residents. However, there is, as yet, no country-level urban forest spatial dataset of sufficient quality for the scientific management of, and correlative studies on, urban forests in China. At present, China attaches great importance to the construction of urban forests, and it is necessary to map a high-resolution and high-accuracy dataset of urban forests in China. The open-access Sentinel images and the Google Earth Engine platform provide a significant opportunity for the realization of this work. This study used eight bands (B2–B8, B11) and three indices of Sentinel-2 in 2016 to map the urban forests of China using the Random Forest machine learning algorithms at the pixel scale with the support of Google Earth Engine (GEE). The 7317 sample points for training and testing were collected from field visits and very high resolution images from Google Earth. The overall accuracy, producer’s accuracy of urban forest, and user’s accuracy of urban forest assessed by independent validation samples in this study were 92.30%, 92.27%, and 92.18%, respectively. In 2016, the percentage of urban forest cover was 19.2%. Nearly half of the cities had an urban forest cover between 10% and 20%, and the average percentage of large cities whose urban populations were over 5 million was 24.8%. Cities with less than half of the average were mainly distributed in northern and western parts of China, which should be focused on in urban greening planning.

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Understanding multi-temporal urban forest cover using high resolution images

Cited by 24 publications

References 21 publications

Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 Images with Google Earth Engine

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