Tree canopy cover estimation by means of remotely sensed data for large geographical areas: overview, available data, and proposal (original) (raw)
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A new global 1‐km dataset of percentage tree cover derived from remote sensing
Global Change …, 2000
Accurate assessment of the spatial extent of forest cover is a crucial requirement for quantifying the sources and sinks of carbon from the terrestrial biosphere. In the more immediate context of the United Nations Framework Convention on Climate Change, implementation of the Kyoto Protocol calls for estimates of carbon stocks for a baseline year as well as for subsequent years. Data sources from country level statistics and other ground-based information are based on varying de®nitions of`forest' and are consequently problematic for obtaining spatially and temporally consistent carbon stock estimates. By combining two datasets previously derived from the Advanced Very High Resolution Radiometer (AVHRR) at 1 km spatial resolution, we have generated a prototype global map depicting percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type (broadleaf and needleleaf). The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial datasets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. The percentage tree cover dataset is available through the Global Land Cover Facility at the University of Maryland at http://glcf.umiacs.umd.edu.
Estimation of tree canopy cover in evergreen oak woodlands using remote sensing
Forest Ecology and Management, 2006
The montado/dehesa landscapes of the Iberian Peninsula are savannah-type open woodlands dominated by evergreen oak species (Quercus suber L. and Q. ilex ssp. rotundifolia). Scattered trees stand over an undergrowth of shrubs or herbaceous plants. To partition leaf area index between trees and the herbaceous/shrubby understorey requires good estimates of tree canopy cover and is of key importance to understand the ecology and the changes in land cover. The two vegetation components differ in phenology as well as in radiation and rainfall interception, water and CO 2 fluxes. The main goal of this study was to estimate tree canopy cover in a montado/dehesa region of southern Portugal (Alentejo) using remote sensed data. For this purpose we developed empirical models combining measurements obtained through the analysis of aerial photos and reflectance from Landsat Thematic Mapper (TM) individual channels, vegetation indices, and the components of the Kauth-Thomas (K-T) transformation. A set of 142 plots was designed, both in the aerial photos and in the satellite data. Several simple and multiple linear regression models were adjusted and validated. A subset of 75% of the data (n = 106) was used for model fitting, and the remainder (n = 36) was used for model assessment. The best linear equation includes Landsat TM channels 3, 4, 5 and 7 (r 2 = 0.74), but the Normalised Difference Vegetation Index (NDVI), the components of the K-T transformation, and the Atmospherically Resistant Vegetation Index (ARVI) also performed well (r 2 = 0.72, 0.70, and 0.69, respectively). The statistics of prediction residuals and tests of model validation indicates that these were also the models with better predictive capability. These results show that detection of low/medium tree canopy cover in this type of land cover (i.e. evergreen oak woodlands) can be accomplished with the help of high and medium spatial resolution satellite imagery. #
2008
This study presents a methodology for derivation of fractional canopy cover, detection of main tree species, and extraction of forest stands using logistic regression, airborne remote sensing data and field samples. In a first step, canopy height models (CHMs) are generated using medium point density LiDAR DSM and DTM and a high-quality matching DSM. Then, fractional canopy covers are calculated using logistic regression models and explanatory variables from LiDAR and matching CHM, whereas the latter produced better results due to higher quality and was therefore further used in this study. Based on this fractional canopy cover, main tree species and forest stands are modelled using logistic regression and airborne digital sensor data ADS40 and CIR aerial image data as input variables. Good accuracy for the extraction of canopy cover, distinction between coniferous and deciduous trees and classification of five main tree species (kappa = 0.7 to 0.9) were obtained but classification of additional three deciduous tree species was less accurate. The extraction of forest stands produced visually satisfactory results but this method suffers from some limitations and further research is needed. The present study reveals that the extracted forest attributes may be helpful to support stereo-image interpretation and field surveys in the frame of the Swiss National Forest Inventory (NFI) and may also be useful for updating existing forest masks and forest management and protection tasks.
Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data
Remote Sensing, 2011
Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC. OPEN ACCESS Remote Sens. 2011, 3 2244
International Journal of Remote Sensing, 2008
Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a lot of interest in extracting both canopy structural parameters from remote sensing. The LAI is generally estimated with spectral information from remotely sensed images at relatively coarse spatial resolution. There has been much less success in estimating tree crown size with remote sensing. The recent availability of abundant high spatial resolution imagery from space offers new potential for extracting LAI and tree crown size, particularly in the spatial domain. This study found that the spatial information in Ikonos imagery is highly valuable in estimating both tree crown size and LAI. When the conifer-and hardwooddominated stands are pooled, tree crown sizes of conifer stands relate best to the ratio of image variance at 262 m spatial resolution to that at 363 m spatial resolution, while LAI relates best to image variance at 464 m spatial resolution. When the conifer-and hardwood-dominated stands are separated, image spatial information estimates tree crown size much better for conifer-dominated stands than for the hardwood-dominated stands, while the relationship between image spatial information and LAI is strengthened after the two types of stands are combined. Tree crown size is more sensitive to image spatial resolution than LAI. Image variance is more useful in estimating LAI than normalized difference vegetation index (NDVI) and simple ratio vegetation index (SRVI). Combining both spatial and spectral information provides some improvement in estimating LAI compared with using spatial information alone. Therefore, future efforts to estimate canopy structure with high resolution imagery should also use image spatial information.
Forest canopy structure derived from spatial and spectral high resolution remote sensing data
2005
Forest canopy structure can be described by a variety of biophysical parameters, for example leaf area index (LAI) and fractional cover (fcover). These parameters are derived currently from remotely sensed data only with limited accuracy. The retrieval of biophysical parameters is often conducted by empirical models based on vegetation indices (VI) exploiting the spectral information but ignoring the spatial dimension contained in remote sensing data. However, texture information provided by high spatial resolution data can be potentially used as additional information related to the forest structure and might improve the models for the retrieval of biophysical parameters. The aim of this research is to evaluate several methods to combine spectral and textural information to derive the best retrieval method of LAI and fcover from spectral and high spatial resolution remote sensing data in a coniferous forest in the Swiss National Park.
Remotely Sensed Tree Characterization in Urban Areas: A Review
Remote Sensing, 2021
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 o...
Forest cover indicator based on multi-scale remote sensing information
Forests cover nearly half of Canada's landmass. While forested lands are often viewed as areas of wood production, forests also provide wildlife habitat and ecosystem mechanisms to clean air and water, and sequester carbon. Measuring the area of forested land in Canada on a regular basis provides an indicator of the availability of these important ecosystem services. This study examines the capability of coarse spatial resolution satellite data to quantify forest cover based on crown closure estimates. Field data, high and medium resolution remote sensing imageries, and a canopy radiative transfer model (Five-Scale) are used to assess the mapping potential of 1-km data, such as AVHRR, VGT and MODIS. The main challenge of the research is in the transition zone between boreal forest and the tundra, where few inventory data are available and the trees are found in clusters. The results will be used in a Canada-wide forest indicator that aimed at monitoring yearly changes in the for...
Assessing urban forest canopy cover using airborne or satellite imagery
With the availability of many sources of imagery and various digital classification techniques, assessing urban forest canopy cover is readily accessible to most urban forest managers. Understanding the capability and limitations of various types of imagery and classification methods is essential to interpreting canopy cover values. An overview of several remote sensing techniques used to assess urban forest canopy cover is presented. A case study comparing canopy cover percentages for Syracuse, New York, U.S. interprets the multiple values developed using different methods. Most methods produce relatively similar results, but the estimate based on the National Land Cover Database is much lower.
2009
Canopy or vegetative crown cover is a popular structural characteristic that is described in both remote sensing and ecological research. There are, however, numerous methods and statistical measures described that can be used for the estimation and description of the proportion of canopy cover. Most methods of calculating vegetative cover from remotely sensed data are based on pixel-based measures in the form of ratios and indices. When calculating cover estimates from high spatial resolution (HSR) imagery the pixel-based methods also provide values of cover for non-canopy or betweencanopy data especially in woodland regions such as tropical savanna. The non-canopy values are included in any calculations of cover. Therefore it can be argued that these measures are not truly a representation of canopy cover but of cover in general. This paper describes an object-based method for calculating canopy cover by extracting tree crowns from HSR imagery over tropical savanna woodland. Cover estimates from this method are then compared with those obtained from pixel-based statistical and ratio measures from medium resolution and HSR data. Relationships between the various measures are described.