Estimation of Crown Closure and Tree Density Using Landsat TM Satellite Images in Mixed Forest Stands

Kahriman, Aydın; Günlü, Alkan; Karahalil, Uzay

doi:10.1007/s12524-013-0355-3

Cited by 17 publications

(14 citation statements)

References 16 publications

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“…The R 2 values for aspen forests in this study were higher than those in similar studies of homogenous and broad-leaved forested landscapes in Iran studied by [4] as well. We also found similar results for spruce-fir mixed forests (R 2 = 0.63) compared to those that were reported for mixed forests in Turkey [22].…”

Section: Predicting Tree Densitysupporting

confidence: 90%

“…Because forest variables have been correlated to remotely sensed reflectance patterns, they can also be used as a source of biophysical information [3,4,20,21]. For example, the information recorded in individual spectral bands (and a combination of those) by the Landsat Thematic Mapper (TM) sensor has been demonstrated to be a good predictor of understory species, tree seedling density, shrub and grass cover and height, size diversity, age, and biomass of overstory species in Yellowstone lodgepole pine forests in the USA [20], of canopy cover and tree density in mixed conifer forests in the USA [22] and in northern Turkey [23], and of diameter at breast height (DBH), height, canopy closure, and basal area in the tropical forests of Sulawesi, Indonesia [3]. In addition to the use of individual TM bands as predictors of forest variables and parameters, one or sometimes a combination of indices calculated from these bands have also been used extensively to predict biophysical attributes at larger geographic scales.…”

Section: Introductionmentioning

confidence: 99%

“…For example, a combination of Landsat TM sensor values and field data has been used to estimate structural parameters in conifer forests of Canadian Northwest Territories [36,39]. A combination of NDVI and several other vegetation indices have been used to assess structural diversity in areas such as the Atlantic rainforest of southeastern Brazil (R 2 = 0.45 to 0.89) [40], relatively homogenous forests in northeast Iran (R 2 = 0.69 to 0.74) [4], North American lodgepole pine forests in Yellowstone National Park (R 2 = 0.46 to 0.80) [20], and mixed forest stands of Turkey (R 2 = 0.67 to 0.70) [22]. These and other scientific assessments have corroborated the effectiveness of using ratios between reflectance bands from satellite images to estimate structural properties in a wide variety of forest types and settings.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Tree Density in Forests of the Southwestern USA Using Landsat 8 Data

Humagain

Portillo-Quintero

Cox

et al. 2017

Forests

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Abstract:The increase of tree density in forests of the American Southwest promotes extreme fire events, understory biodiversity losses, and degraded habitat conditions for many wildlife species. To ameliorate these changes, managers and scientists have begun planning treatments aimed at reducing fuels and increasing understory biodiversity. However, spatial variability in tree density across the landscape is not well-characterized, and if better known, could greatly influence planning efforts. We used reflectance values from individual Landsat 8 bands (bands 2, 3, 4, 5, 6, and 7) and calculated vegetation indices (difference vegetation index, simple ratios, and normalized vegetation indices) to estimate tree density in an area planned for treatment in the Jemez Mountains, New Mexico, characterized by multiple vegetation types and a complex topography. Because different vegetation types have different spectral signatures, we derived models with multiple predictor variables for each vegetation type, rather than using a single model for the entire project area, and compared the model-derived values to values collected from on-the-ground transects. Among conifer-dominated areas (73% of the project area), the best models (as determined by corrected Akaike Information Criteria (AICc)) included Landsat bands 2, 3, 4, and 7 along with simple ratios, normalized vegetation indices, and the difference vegetation index (R 2 values for ponderosa: 0.47, piñon-juniper: 0.52, and spruce-fir: 0.66). On the other hand, in aspen-dominated areas (9% of the project area), the best model included individual bands 4 and 2, simple ratio, and normalized vegetation index (R 2 value: 0.97). Most areas dominated by ponderosa, pinyon-juniper, or spruce-fir had more than 100 trees per hectare. About 54% of the study area has medium to high density of trees (100-1000 trees/hectare), and a small fraction (4.5%) of the area has very high density (>1000 trees/hectare). Our results provide a better understanding of tree density for identifying areas in need of treatment and planning for more effective treatment. Our analysis also provides an integrated method of estimating tree density across complex landscapes that could be useful for further restoration planning.

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Section: Predicting Tree Densitysupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Tree Density in Forests of the Southwestern USA Using Landsat 8 Data

Humagain

Portillo-Quintero

Cox

et al. 2017

Forests

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“…Remote sensing is an effective tool to estimate the stand density. Results from relevant literature are shown in Table 4, where the remote sensing data used included airborne [40] and terrestrial LiDAR [41], optical imagery [42][43][44], and SAR data [45]. The study reported by Lee and Lucas [40] is most directly comparable to ours as they also implemented airborne LiDAR data (Optech ALTM 1020), while the stand densities were estimated by computing the height-scaled crown openness index for LiDAR data of white cypress pine in the coniferous forest.…”

Section: Discussionmentioning

confidence: 85%

“…Three reported studies were founded using optical imagery to estimate stand density. Kahriman, et al [42] used Landsat TM satellite imagery to estimate the stand density based on establishing multiple regression between vegetation indices, including the soil adjusted vegetation index and difference vegetation index, and stand density for a mixed forest of Pine and beech. Furthermore, their reported minimum RMSE value was 0.83 trees/100 m 2 .…”

Section: Discussionmentioning

confidence: 99%

Estimating Stand Density in a Tropical Broadleaf Forest Using Airborne LiDAR Data

Lee

Wang

2018

Forests

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Forest-related statistics, including forest biomass, carbon sink, and the prevention of forest fires, can be obtained by estimating stand density. In this study, a dataset with the laser pulse density of 225.5 pulses/m2 was obtained using airborne laser scanning in a tropical broadleaf forest. Three digital surface models (DSMs) were generated using first-echo, last-echo, and highest first-echo data. Three canopy height models (CHMs) were obtained by deducting the digital elevation model from the three DSMs. The cell sizes (Csizes) of the CHMs were 1, 0.5, and 0.2 m. In addition, stand density was estimated using CHM data and following the local maximum method. The stand density of 35 sample regions was acquired via in-situ measurement. The results indicated that the root-mean-square error (RMSE) ranged between 1.68 and 2.43; the RMSE difference was only 0.78, indicating that stand density was effectively estimated in both cases. Furthermore, regression models were used to correct the error in stand density estimations; the RMSE after correction was called RMSE′. A comparison of the RMSE and RMSE′ showed that the average value decreased from 12.35 to 2.66, meaning that the regression model could effectively reduce the error. Finally, a comparison of the effects of different laser pulse densities on the RMSE value showed that, in order to obtain the minimum RMSE for stand density, the laser pulse density must be greater than 10, 30, and 125 pulses/m2 at Csizes of 1, 0.5, and 0.2 m, respectively.

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