Evaluating the Woody Species Diversity by Means of Remotely Sensed Spectral and Texture Measures in the Urban Forests

Özkan, Ulaş Yunus; Özdemir, İbrahim; Sağlam, Serhun; Yeşil, Ahmet; Demirel, Tufan

doi:10.1007/s12524-016-0550-0

Cited by 13 publications

(10 citation statements)

References 44 publications

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“…Yet, despite the universally known utilities of urban flora, urban forests are still subject to abiotic constraints and disturbances [16]. Furthermore, the urban population growth at 4% rate per decade by 2050 [17], doesn't help to improve the situation.…”

Section: Introductionmentioning

confidence: 99%

Phytodiversity and Spatial Development of Urban Flora in Lokossa, Benin

Zakari¹,

Djego²

2020

IJNREM

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The need for sustainable and green cities highlights the importance to improve plant species diversity in urban areas. We assessed the phytodiversity of three management units (strata) to enhance plant species conservation planning in Lokossa City. A forest inventory was conducted in each stratum based on one-hectare sample plots. Fifty plots were distributed following a stratified random sampling approach. We found that 53 plants species belonging to 46 genera and 22 families were established in the strata. However, the flora of the city was mixed with 50.94% of native plant species. The dominant families were Leguminosae, Arecaceae, Malvaceae, Moraceae, Combretaceae, Lamiaceae and Myrtaceae. The ten most important plant species accounted for 69.31% of the total abundance. Khaya senegalensis (Desv.) A. Juss. and Mangifera indica L. were dominant plant species. Ubiquitous species accounted for 54.72% of species pool and 82.92% of all individuals. The flora of residential zone was more diversified than those in road buffer and institutional zones. Which contributed to 60.27% of all individuals. The road buffer and institutional zones flora were quite similar. Therefore, we suggested that the planting and monitoring projects should be detailed and budgeted with reference to plausible scientific knowledge in the city development plan. The creation of participatory botanical gardens at the city neighborhoods scale. The integration of the flora of residential zone in species conservation projects in order to increase global diversity and storage of biomass in the city.

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Section: Introductionmentioning

confidence: 99%

Phytodiversity and Spatial Development of Urban Flora in Lokossa, Benin

Zakari¹,

Djego²

2020

IJNREM

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“…Landsat and Sentinel provide openaccess data of higher spectral resolution, which makes them a powerful data source for vegetation analysis [29], compared with for instance QuickBird, which, despite its high spatial resolution, only covers the wavelength corresponding to the visible and nearinfrared (VNIR) (Table 3) [85]. Some moderate-resolution satellites (10-30 m pixel size [89]), such as RapidEye, Landsat-OLI (Operational Land Imager), and Sentinel (Table 3), were used to evaluate the urban forest diversity based on the texture and vegetation indices in four (8%) of the reviewed studies [44,49,72,74]. For instance, Ozkan et al [44], found that spectral and textural properties derived from satellite imagery can be related to woody species diversity and ES assessment in the urban forests even using moderate spatial resolution imagery (Landsat (30 m) and RapidEye (5 m)).…”

Section: Satellite Imagerymentioning

confidence: 99%

“…The scientific community has progressed in its use of multiple remotely sensed information sources, including satellite imagery [35][36][37], light detection and ranging or laser scanning detection and ranging (LiDAR) [8,21,36,[38][39][40][41], aerial imagery [7,11,42,43], and digital ground-level images (GLI) [44][45][46]. Although satellites, such as Landsat and Sentinel, do not have the spatial resolution necessary for tree characterization (below four or three meters, according to some authors [10,47,48]), in recent decades, satellite sensors with advanced capabilities have been developed.…”

Section: Introductionmentioning

confidence: 99%

Remotely Sensed Tree Characterization in Urban Areas: A Review

et al. 2021

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Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.

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“…The texture properties were determined according to the Gray Level Co-occurrence Matrix (GLCM) (Haralick et al 1973). GLCM is widely utilized to describe the textural properties in remote sensing studies (Kayitakire et al 2006;Maltamo et al, 2006;Shamsoddini et al, 2013;Ozdemir and Donoghue 2013;Ozkan et al 2016Ozkan et al ,2020Meng 2016;Zhao et al 2018). There are many texture properties defined by GLCM in three main groups as Contrast, Orderliness and Descriptive (Ozdemir and Karnieli 2011).…”

Section: Predictor Variablesmentioning

confidence: 99%

“…Satellite images have important advantages such as broad spatial coverage and temporally high frequency (García et al 2018;Ustin and Middleton 2021). Numerous methods have been developed using image features such as reflectance values, texture features and vegetation indices derived from satellite images to estimate forest structural parameters (Ozdemir and Karnieli 2011;Ozkan et al 2016). LiDAR (Light Detection and Ranging) data, an active remote sensing technology, has significant potential for the prediction of forest structural variables.…”

Section: Introductionmentioning

confidence: 99%

The influence of window size on remote sensing-based prediction of forest structural variables

Özkan

Demirel

2021

Ecol Process

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Background Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image. Results In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size was optimal for N and H, the 600 m2 window size was optimal for V and the 1000 m2 window size was optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands. Conclusion This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

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Evaluating the Woody Species Diversity by Means of Remotely Sensed Spectral and Texture Measures in the Urban Forests

Cited by 13 publications

References 44 publications

Phytodiversity and Spatial Development of Urban Flora in Lokossa, Benin

Phytodiversity and Spatial Development of Urban Flora in Lokossa, Benin

Remotely Sensed Tree Characterization in Urban Areas: A Review

The influence of window size on remote sensing-based prediction of forest structural variables

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