Estimate Canopy Cover Research Articles

Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds

This study investigates the potential of unmanned aerial vehicles (UAVs) to measure and monitor structural properties of forests. Two remote sensing techniques, airborne laser scanning (ALS) and structure from motion (SfM) were tested to capture three-dimensional structural information from a small multi-rotor UAV platform. A case study is presented through the analysis of data collected from a 30 × 50 m plot in a dry sclerophyll eucalypt forest with a spatially varying canopy cover. The study provides an insight into the capabilities of both technologies for assessing absolute terrain height, the horizontal and vertical distribution of forest canopy elements, and information related to individual trees. Results indicate that both techniques are capable of providing information that can be used to describe the terrain surface and canopy properties in areas of relatively low canopy closure. However, the SfM photogrammetric technique underperformed ALS in capturing the terrain surface under increasingly denser canopy cover, resulting in point density of less than 1 ground point per m2 and mean difference from ALS terrain surface of 0.12 m. This shortcoming caused errors that were propagated into the estimation of canopy properties, including the individual tree height (root mean square error of 0.92 m for ALS and 1.30 m for SfM). Differences were also seen in the estimates of canopy cover derived from the SfM (50%) and ALS (63%) pointclouds. Although ALS is capable of providing more accurate estimates of the vertical structure of forests across the larger range of canopy densities found in this study, SfM was still found to be an adequate low-cost alternative for surveying of forest stands.

A note on estimating canopy cover from digital cover and hemispherical photography

Fast and accurate estimates of canopy cover are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used in forestry to estimate canopy cover. In this paper the accuracy of canopy cover estimates from two widely used canopy photographic methods, hemispherical photography (DHP) and cover photography (DCP) was evaluated. Canopy cover was approximated in DHP as the complement of gap fraction data at narrow viewing zenith angle range (0°–15°), which was comparable with that of DCP. The methodology was tested using artificial images with known canopy cover; this allowed exploring the influence of actual canopy cover and mean gap size on canopy cover estimation from photography. DCP provided robust estimates of canopy cover, whose accuracy was not influenced by variation in actual canopy cover and mean gap size, based on comparison with artificial images; by contrast, the accuracy of cover estimates from DHP was influenced by both actual canopy cover and mean gap size, because of the lower ability of DHP to detect small gaps within crown. The results were replicated in both DHP and DCP images collected in real forest canopies. Finally, the influence of canopy cover on foliage clumping index and leaf area index was evaluated using a theoretical gap fraction model. The main findings indicate that DCP can overcome the limits of indirect techniques for obtaining unbiased and precise estimates of canopy cover, which are comparable to those obtainable from direct, more labour-intensive techniques, being therefore highly suitable for routine monitoring and inventory purposes.

A comparison of two sampling approaches for assessing the urban forest canopy cover from aerial photography

Two different sampling approaches for estimating urban tree canopy cover were applied to two medium-sized cities in the United States, in conjunction with two freely available remotely sensed imagery products. A random point-based sampling approach, which involved 1000 sample points, was compared against a plot/grid sampling (cluster sampling) approach that involved a 1.83m square grid of points embedded within 0.04ha circular plots. The imagery products included aerial photography from the U.S. Department of Agriculture National Agricultural Imagery Program (viewed within ArcGIS), and Google Earth imagery. For Tallahassee, Florida, the estimate of tree canopy cover was 48.6–49.1% using Google Earth imagery and 44.5–45.1% using NAIP imagery within ArcGIS. Statistical tests suggested that the two sampling approaches produced significantly different estimates using the two different imagery sources. For Tacoma, Washington, the estimated tree canopy cover was about 19.2–20.0% using Google Earth imagery and 17.3–18.1% when using NAIP imagery in ArcGIS. Here, there seemed to be no significant difference between the random point-based sampling efforts when used with the two different image sources, while the opposite was true when using the plot/grid sampling approach. However, our findings showed some similarities between the two sampling approaches; hence, the random point-based sampling approach might be preferred due to the time and effort required, and because fewer opportunities for classification problems might arise. Continuous review of urban canopy cover estimation procedures suggested by organizations such as the Climate Action Reserve and others can provide society with information on the accuracy and effectiveness resource assessment methods employed for making wise decisions about climate change and carbon management.

Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV

Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA) and to the CIE L*a*b* colour space to obtain estimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography).The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution.We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications.

Canopeo: a new tool to measure green canopy cover

Canopeo: a new tool to measure green canopy cover

Field test of canopy cover estimation by hemispherical photographs taken with a smartphone

AbstractAimTo test the Canopy Cover (CaCo) index for forest vegetation research estimation of canopy cover from hemispherical photographs, and introduce a new Android smartphone application for use of this index.MethodsThe original and modified CaCo index were evaluated using a data set of 234 hemispherical photographs taken in 78 plots in coniferous, mixed and broad‐leaved deciduous forests. The results of the CaCo analysis of these photographs were compared with expert field visual estimation of canopy cover of these plots. For each hemispherical photograph, several CaCo values were calculated based on the photograph being restricted to different degrees by artificial horizon masking. The CaCo index was also tested with respect to precision of canopy cover estimation and sensitivity to different photographic equipment, using a different set of 93 canopy photographs taken in mixed and coniferous forests. Calculation of the CaCo index was done with the newly developed GLAMA – Gap Light Analysis Mobile Application software.ResultsLinear regression showed a close relationship between the CaCo index and visually observed canopy cover data. A proposed calculation modification improved the stability of the CaCo index in cases in which no horizon masking was applied. The best fit, zero‐intercept and a regression slope close to1 were found in cases in which an artificial horizon mask that extended higher than 45° restricted the bottom part of the sky hemisphere. Low sensitivity of the CaCo index to type of photographic equipment used was shown.ConclusionsThe CaCo index is robust and can be used for precise canopy cover estimation, comparable to visual canopy cover estimation and unaffected by observer bias. Not only can it be used on already‐captured photographs, but the index can also be employed on smartphones to rapidly capture hemispherical photographs and immediately calculate their index values. This application is freely available on the Internet and can serve as a powerful research and educational tool that can not only calculate CaCo values, but also standardize forest canopy visual estimates.

SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is one of the most complete and frequently used global-scale DEM products in various applications. However, previous studies have shown that the SRTM DEM is systematically higher than the actual land surface in vegetated mountain areas. The objective of this study is to propose a procedure to calibrate the SRTM DEM over large vegetated mountain areas. Firstly, we developed methods to estimate canopy cover from aerial imagery and tree height from multi-source datasets (i.e., field observations, airborne light detection and ranging (LiDAR) data, Geoscience Laser Altimeter System (GLAS) data, Landsat TM imagery, climate surfaces, and topographic data). Then, the airborne LiDAR derived DEM, covering ~5% of the study area, was used to evaluate the accuracy of the SRTM DEM. Finally, a regression model of the SRTM DEM error depending on tree height, canopy cover, and terrain slope was developed to calibrate the SRTM DEM. Our results show that the proposed procedure can significantly improve the accuracy of the SRTM DEM over vegetated mountain areas. The mean difference between the SRTM DEM and the LiDAR DEM decreased from 12.15 m to −0.82 m, and the standard deviation dropped by 2 m.

Estimating pinyon and juniper cover across Utah using NAIP imagery

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush ( Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature Analyst TM for ArcMap 10.1 ® , ENVI Feature Extraction ® , and Trimble eCognition Developer 8.2 ® ) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept) on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst TM , and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature Analyst TM , and 0.83 for eCognition). All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel’s oak), which had similar spectral values as P-J.

Airborne multispectral detection of regrowth cotton fields

Effective methods are needed for timely areawide detection of regrowth cotton plants because boll weevils (a quarantine pest) can feed and reproduce on these plants beyond the cotton production season. Airborne multispectral images of regrowth cotton plots were acquired on several dates after three shredding (i.e., stalk destruction) dates. Linear spectral unmixing (LSU) classification was applied to high-resolution airborne multispectral images of regrowth cotton plots to estimate the minimum detectable size and subsequent growth of plants. We found that regrowth cotton fields can be identified when the mean plant width is ∼0.2 m for an image resolution of 0.1 m. LSU estimates of canopy cover of regrowth cotton plots correlated well (r2=0.81) with the ratio of mean plant width to row spacing, a surrogate measure of plant canopy cover. The height and width of regrowth plants were both well correlated (r2=0.94) with accumulated degree-days after shredding. The results will help boll weevil eradication program managers use airborne multispectral images to detect and monitor the regrowth of cotton plants after stalk destruction, and identify fields that may require further inspection and mitigation of boll weevil infestations.

Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2=0.88, RMSE=2.39m and bias=−0.16 for canopy height; R2=0.72, RMSE=0.068% and bias=−0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm.

Sample Grain Influences the Functional Relationship Between Canopy Cover and Gopher Tortoise (Gopherus polyphemus) Burrow Abandonment

Abstract Change in vegetation structure alters habitat suitability for the threatened gopher tortoise (Gopherus polyphemus). An understanding of this dynamic is crucial to inform habitat and tortoise management strategies. However, it is not known how the choice of the sample grain (i.e., cell size) at which vegetation structure is measured impacts estimates of tortoise–habitat relationships. We used lidar remote sensing to estimate canopy cover around 1573 gopher tortoise burrows at incrementally larger sample grains (1–707 m2) in 450 ha of longleaf pine (Pinus palustris) savanna. Using an information theoretic approach, we demonstrate that the choice of grain size profoundly influences modeled relationships between canopy cover and burrow abandonment. At the most supported grain size (314 m2), the probability of burrow abandonment increased by 1.7% with each percent increase in canopy cover. Ultimately, detecting the appropriate sample grain can lead to more effective development of functional relations...

Validating FAO AquaCrop using Landsat images and regional crop information

Defining crop response to water is a crucial part of decision-making for agricultural water management. This study is proposing an efficient and a low-cost approach to validate FAO's AquaCrop model using remote sensing (RS) estimates instead of crop ground measurements. A radiance use efficiency (RUE) based RS model for estimating aboveground biomass (AGB) is proposed with Landsat images and regional crop information. The RS estimates are used to validate AquaCrop's built-in crops and calibrate it under salinity stress. As a result, RS estimates of canopy cover (CC) and AGB were produced from an existing CC model and the proposed AGB model, respectively. These estimates became good replacements of the ground measurements for validation and calibration. Built-in maize of AquaCrop showed good agreement between simulations and RS estimates under non-stress conditions whereas built-in barley underestimated AGB compared to the RS estimates. By comparing the RS estimates in salinity-affected farms to AquaCrop simulations without considering salinity stress, AGB reduction due to salinity stress and corresponding CC reduction were quantified for calibration of AquaCrop under salinity stress. The results of calibration predicted initial soil salinity of saline-stressed farms and the values are within the possible ranges. The proposed methodology shows that the readily available Landsat images and regional crop information could extend the validation of built-in crops of AquaCrop to regions without ground measurements.

Canopy cover estimation across semi-Mediterranean woodlands: application of high-resolution earth observation data

The semi-Mediterranean Zagros forests in western Iran are a crucial source of environmental services, but are severely threatened by climatic and anthropological constraints. Thus, an adequate inventory of existing tree cover is essential for conservation purposes. We combined ground samples and Quickbird imagery for mapping the canopy cover in a portion of unmanaged Quercus brantii stands. Orthorectified Quickbird imagery was preprocessed to derive a set of features to enhance the vegetation signal by minimizing solar irradiance effects. A recursive feature elimination was conducted to screen the predictor feature space. The random forest (RF) and support vector machines (SVMs) were applied for modeling. The input datasets were composed of four sets of predictors including the full set of predictors, the four original Quickbird bands, selected vegetation indices, and the soil line-based vegetation indices. The highest r2 and lowest relative root mean square error (RMSE) were observed in modeling with total indices and the full data set in both modeling methods. Regardless of the input dataset used, the RF models outperformed the SVM by returning higher r2 and lower relative RMSEs. It can be concluded that applying these methods and vegetation indices can provide useful information for the retrieval of canopy cover in mountainous, semiarid stands which is crucial for conservation practices in such areas.

Utilizing National Agriculture Imagery Program Data to Estimate Tree Cover and Biomass of Piñon and Juniper Woodlands

With the encroachment of piñon (Pinus ssp.) and juniper (Juniperus ssp.) woodlands onto sagebrush steppe rangelands, there is an increasing interest in rapid, accurate, and inexpensive quantification methods to estimate tree canopy cover and aboveground biomass. The objectives of this study were 1) to evaluate the relationship and agreement of piñon and juniper (P-J) canopy cover estimates, using object-based image analysis (OBIA) techniques and National Agriculture Imagery Program (NAIP, 1-m pixel resolution) imagery with ground measurements, and 2) to investigate the relationship between remotely-sensed P-J canopy cover and ground-measured aboveground biomass. For the OBIA, we used eCognition® Developer 8.8 software to extract tree canopy cover from NAIP imagery across 12 P-J woodlands within the Sagebrush Steppe Treatment Evaluation Project (SageSTEP) network. The P-J woodlands were categorized based on the dominant tree species found at the individual sites for the analysis (western juniper, Utah juniper, and mixed P-J community). Following tree canopy cover extractions, relationships were assessed between remotely-sensed canopy cover and ground-measured aboveground biomass. Our OBIA estimates for P-J canopy cover were highly correlated with ground-measured tree canopy cover (averaged across all regions r = 0.92). However, differences between methods occurred for western and Utah juniper sites (P < 0.05), and were more prominent where tree canopy cover was > 40%. There were high degrees of correlation between predicted aboveground biomass estimates with the use of remotely-sensed tree canopy cover and ground-measured aboveground biomass (averaged across all regions r = 0.89). Our results suggest that OBIA methods combined with NAIP imagery can provide land managers with quantitative data that can be used to evaluate P-J woodland cover and aboveground biomass rapidly, on broad scales. Although some accuracy and precision may be lost when utilizing aerial imagery to identify P-J canopy cover and aboveground biomass, it is a reasonable alternative to ground monitoring and inventory practices.

Ground‐Based Digital Imaging as a Tool to Assess Soybean Growth and Yield

ABSTRACTLeaf and canopy characteristics influence light interception and photosynthesis, which ultimately contribute to total biomass production and crop yield. Inexpensive hand‐held digital cameras may provide an alternative to time‐consuming destructive sampling methods used to assess these traits. The objective of this study was to test the use of common digital cameras as a potential phenotyping tool to predict yield and other crop traits in two soybean (Glycine max L. Merr.) cultivars subjected to differences in soil moisture availability. Weekly digital images were acquired from each plot and used to estimate canopy cover, total aboveground biomass, leaf biomass, photosynthesis, and grain yield. The method proved effective in identifying the dynamics of canopy cover and changes in biomass and grain yield as influenced by soil water availability. Further, it was possible to differentiate between cultivars and accurately determine the timing of maximum crop biomass and canopy cover. The image analysis also indicated the onset of leaf senescence which led to a decrease in total biomass around 90 to 105 d after planting. Principal components analysis showed that canopy coverage extracted from images collected spanning a period from beginning pod to seed filling was positively correlated with grain yield. Models developed using multiple regression analysis exhibited strong relationships between the ground cover data obtained using image analysis and grain yield (R2 = 0.74), crop growth rate (R2 = 0.69), and photosynthesis (R2 = 0.80). Based on these results, digital imaging has potential as a tool to rapidly assess soybean canopy development and estimate biomass accumulation and grain yield.

Arctic-Alpine Plants Decline over Two Decades in Glacier National Park, Montana, U.S.A.

Climate-based models predict global warming will cause mountain plants to migrate upward with local extirpation of species currently restricted to the highest elevations. Alpine monitoring studies have generally documented changes in alpine plant distributions consistent with these predictions; however, relatively few such studies have been reported from North America. I estimated canopy cover in 71 permanent microplots at two alpine moist-turf sites in Glacier National Park, Montana, U.S.A., three times between 1988 and 2011. Mean annual and summer temperatures were approximately 0.6 and 0.7 °C higher than the previous four decades, respectively. Plants more restricted to high elevations declined more than species with a broader elevational amplitude during this time. Dicots were more likely to have declined than monocots. These data support predictions and provide information that may help refine climate-envelope models.

Characteristics of Barred Owl (&lt;em&gt;Strix varia&lt;/em&gt;) nest sites in Manitoba, Canada

During 2009 and 2010, nine Barred Owl (Strix varia) nest sites were located in Manitoba, Canada, and data on nest trees, nest structure, and nest site habitat were collected. Nests were located in a variety of tree species, including Balsam Poplar (Populus balsamifera), Paper Birch (Betula papyrifera), Trembling Aspen (Populus tremuloides) and Burr Oak (Quercus macrocarpa). All nests were in tree cavities, and the majority of nests were in dead trees (67%) and had lateral openings (67%). Habitat surrounding nest trees and estimated canopy cover were highly variable. Diameter at breast height of nest trees, cavity width, and cavity depth were consistent and were determined to be the most reliable indicators of nest suitability for breeding Barred Owls. We conclude that the distribution of nesting Barred Owls is influenced more by availability of suitable nest sites than by nest tree species or nest site habitat.

Puittaimestiku kaardistamine aerolidari andmete põhjal metsana lisanduvatel aladel/ Estimation of fractional forest cover from airborne laser scanning data in abandoned agricultural land

Abstract The abandonment of agricultural land is an actual problem in Estonia due to significant impact on landscape ecology and structure. Abandoned agricultural fields are usually converting into forest. Mapping of agricultural land use is a strategic interest of each country. Airborne laser scanning (ALS) is used in many countries for topographical mapping and the laser pulse return positions are promising datasets for mapping the abandonment of agricultural land. We used ALS data based woody plant canopy cover estimates made at certain reference height unachievable for field crops to map abandoned agricultural land in nine test sites in Tartumaa, Estonia. The maximum height of trees in test sites ranged from 6.5 m to 13.4 m. The lidar pulse returns based canopy cover estimate was assessed 1) by using ortophoto based digitized maps of tree canopy, 2) repeated measurements made with plant canopy analyzer LAI-2000 and 3) by using allometric crown radius models and repeated tree measurements from sample plots. The interpretation of canopy boundaries and separation of small spaces between tree crowns from ortophotos is a challenging task for an operator. The relationship between ALS based canopy cover and ortophoto based canopy cover was linear in all test sites except when ALS data from beginning of June were used. It the beginning of June foliage is not fully developed on trees. An increase in the woody canopy cover was detected from repeated LAI-2000 measurements and also from repeated tree measurements-based simulated crowns. The impact of reference height change from 2.0 m to 1.3 m on canopy cover estimations was not significant and much smaller compared to the tree growth induced increase in canopy cover, indicating that similar errors originating from e.g. digital elevation model are not problematic for the proposed method in practical applications.

Estimation of the fractional canopy cover of pecan orchards using Landsat 5 satellite data, aerial imagery, and orchard floor photographs

Pecan orchards are the largest agricultural water consumer in the lower part of the Mesilla Valley, NM, USA. Knowledge of fractional canopy (FC) cover allows better crop water use assessment and orchard management. FC can be estimated from vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), the simple ratio (SR), and the triangular vegetation index (TVI), using satellite imagery. The main objective of this research is to develop an approach to determine the FC from a simultaneous imagery campaign consisting of aerial imagery, orchard floor photographs, and satellite images. All the required data were collected based on satellite overpass times at three different times during the initial part of the growing season to enhance the quality of data and reduce errors. The data were processed using the software package Environment for Visualizing Images (ENVI® 4.6.1; ITT Research Systems Inc.). The orchard floor digital photographs were used as a ground truth data set that gave a good correlation to the aerial photography. The aerial images were then used to determine the relationship between the FC and the VIs using these ‘corrected FCs’. The results showed significant correlation between NDVI and FC (R 2 = 0.80; p < 0.0001). Likewise, the calculated SR not only showed good correlation to the FCs but also verified the calculated NDVI. The results indicated that the methodology of this research can be applied to other tree crops as an aid in estimating the FC.

The Influence of Multi-season Imagery on Models of Canopy Cover

Quantifying tree canopy cover in a spatially explicit fashion is important for broad-scale monitoring of ecosystems and for management of natural resources. Researchers have developed empirical models of tree canopy cover to produce geospatial products. For subpixel models, percent tree canopy cover estimates (derived from fi ne-scale imagery) serve as the response variable. The explanatory variables are developed from reflectance values and derivatives, elevation and derivatives, and other ancillary data. However, there is a lack of guidance in the literature regarding the use of leaf-on only imagery versus multi-season imagery for the explanatory variables. We compared models developed from leaf-on only Landsat imagery with models developed from multi-season imagery for a study area in Georgia. There was no statistical difference among models. We suggest that leaf-on imagery is adequate for the development of empirical models of percent tree canopy cover in the Piedmont of the Southeastern United States.