Creating Cloud-Free Landsat 

ETM+ Data Sets in Tropical 

Landscapes: Cloud and 

Cloud-Shadow Removal

Sebastián Martinuzzi, William A. Gould, and Olga M. Ramos González

United States

Department of

Agriculture

Forest Service

International Institute

of Tropical Forestry

General Technical

Report

IITF-GTR-32

February 2007

0 5 10 20 30 40

Kilometers

PUERTO RICO

CULEBRA

VIEQUES

o 67°15'0"W

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66°0'0"W

65°45'0"W

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65°30'0"W

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65°15'0"W

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17°51'0"N

17°51'0"N

18°0'0"N

18°0'0"N

18°9'0"N

18°9'0"N

18°18'0"N

18°18'0"N

18°27'0"N

18°27'0"N

18°36'0"N

18°36'0"N
Authors

Sebastián Martinuzzi is an ecologist, Laboratorio de Investigación de Sistemas 

Ecológicos y Ambientales, Universidad Nacional de La Plata, Diagonal 113 s/n 

Escuela Superior de Bosques, CP 1900, Argentina, and visiting scholar at the 

International Institute of Tropical Forestry. William A. Gould is a research 

ecologist, and Olga M. Ramos González is a geographic information system 

analyst, U.S. Department of Agriculture, Forest Service, International Institute 

of Tropical Forestry, Jardín Botánico Sur, 1201 Calle Ceiba, Río Piedras, PR 

00926-1119.

The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the National Forests and National 

Grasslands, it strives—as directed by Congress—to provide increasingly greater service 

to a growing Nation.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and 

activities on the basis of race, color, national origin, age, disability, and where applicable, 

sex, marital status, familial status, parental status, religion, sexual orientation, genetic 

information, political beliefs, reprisal, or because all or part of an individual’s income 

is derived from any public assistance program. (Not all prohibited bases apply to all 

programs.) Persons with disabilities who require alternative means for communication of 

program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET 

Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write 

USDA, Director, Office of Civil Rights, 1400 Independence Avenue, SW, Washington, DC 

20250-9410 or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal 

opportunity provider and employer.
Abstract

Martinuzzi, Sebastián; Gould, William A.; Ramos González, Olga M. 2006. 

Creating cloud-free Landsat ETM+ data sets in tropical landscapes: cloud and 

cloud-shadow removal. Gen. Tech. Rep. IITF-32. Rio Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 

12 p.

Clouds and cloud shadows are common features of visible and infrared remotelysensed images collected from many parts of the world, particularly in humid and 

tropical regions. We have developed a simple and semiautomated method to mask 

clouds and shadows in Landsat ETM+ imagery, and have developed a recent 

cloud-free composite of multitemporal images for Puerto Rico and its adjacent 

islands that can be used for a variety of landscape analyses. Our assumption is that 

if clouds and shadows can be identified in a reference image, they can be replaced 

with data from other dates. We created cloud masks by using Landsat ETM+ band 

1 (blue) and thermal band 6.1. Additionally, Landsat ETM+ band 4 (near infrared) 

and parameters of sun angle, topography, and cloud-shadow projection were used 

for directing and masking shadows. This methodology was applied to a set of 18 

images from 1999 to 2003 to develop an island-wide image that is 96.5 percent 

cloud free. We considered the seasonality of the imagery when selecting reference 

images and building the mosaic in order to minimize variation in reflectance related 

to dry or wet season canopy characteristics. We developed a higher resolution data 

set by merging the 15-m resolution panchromatic band with the 30-m resolution 

Landsat ETM+ data. The methodology developed is simple and straightforward to 

use wherever obtaining cloud-free image data sets is desirable and can be integrated 

into other efforts that demand an accurate method for the identification of clouds 

and shadows.

Keywords: Remote sensing, clouds, shadows, mask, Landsat ETM+, 

Puerto Rico.

Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal

Introduction

Clouds are common features of visible and infrared remotely-sensed images collected from many tropical, humid, mountainous, and coastal regions of the world. 

Puerto Rico is a mountainous tropical island with pressing needs for readily applicable methods to develop Landsat ETM+ cloud-free scenes for analyses of current 

land cover and land-cover change. Landsat data has been used in Puerto Rico for 

high temporal resolution analyses of land-cover change related to urban expansion 

and regeneration of forests over abandoned agricultural land (Grau et al. 2003; Helmer 2004; Helmer and Ruefenacht 2005; Lopez et al. 2001; Thomlinson et al. 1996; 

Ramos-Gonzalez 2001), estimation of forested area, identification of forests types 

(Helmer et al. 2002), and current land-cover classification, a key component for 

mapping habitats and biodiversity as part of the Puerto Rico Gap Analysis Program 

(Gould 2005). Cloud-free data acquisition for a single date is difficult or impossible 

for the entire island, particularly for higher elevations where a high percentage of 

the protected forest resources are located. In the past, the continued presence of 

clouds in imagery has reduced the amount of island-wide data available, and as a 

result has restricted analyses to those years for which low cloud cover images exist 

(1985, 1991, and 1992). Cloud-free image data sets are useful both for the creation 

of composites and for comparison of temporal variation in spectral signals. In the 

creation of composite imagery, data can be radiometrically corrected, mosaicked, 

and analyzed as a single image for classification of general land cover types, or 

scenes can be classified individually and mosaicked postclassification.

The Puerto Rican landscape represents a challenge for clouds and shadow 

identification in Landsat ETM+ images owing to the complexity and heterogeneity of land cover (including different forest types and a variety of high-reflectance 

surfaces such as urban, barren, quarries, rocks, salt mining, sand, pastures and 

agricultural lands), and topography. In the visible and near-infrared spectral regions 

of Landsat ETM+ images, clouds are generally characterized by reflectance higher 

than the underlying (land or ocean) surface. However, under certain surface conditions, such as urban/built-up areas (as for ice and snow in temperate and cold 

regions), the reflectance difference between cloud and underlying surface can be 

insufficiently low or even reversed (Li et al. 1999, Schröder et al. 2002). Optical 

remote sensing images also have the problem of cloud shadows. The spectral irradiance reaching the ground in cloud-shadow areas is both reduced in intensity and 

changed in its spectral properties (Choi and Milton 1999), similar to that of topographic shadows. The presence of two additional thermal bands in Landsat imagery 
GENERAL TECHNICAL REPORT IITF-GTR-32

(bands 6.1 and 6.2) allows for the analysis of the surface in terms of ground or 

canopy surface temperatures (Melesse and Jordan 2002). In these bands, clouds, 

tree foliage, and damp ground appear in dark tones (cooler), whereas grassland and 

other low-lying vegetation, dry ground, concrete, and asphalt appear in brighter 

tones (warmer) (Jensen 2000). 

The development of cloud-free composites involves both the identification of 

cloud features as well as distinguishing cloud shadows from topographic shadows. 

Various techniques have been developed to minimize the effect of clouds and cloudshadows (Ackerman et al. 1998, Choi and Milton 1999, Helmer and Ruefenacht 

2005, Li et al. 1999, Liew et al. 1998, Milton and Choi 1999, Schröder et al. 2002, 

Song and Civco 2002). We used optical and thermal spectral characteristics as well 

as geographic position to identify cloud and cloud shadows. Analysis of spectral 

reflectance in different bands is essential to identifying features in the images. 

Song and Civco (2002) used the brightness value in Landsat TM bands 1 (blue) and 

4 (near infrared) to detect clouds and shadows in Madagascar. We added thermal 

information from Landsat ETM band 6.1 to spectrally separate clouds from similarly responding features, such as urban/built-up lands. Although cloud shadows 

and topographic shadows are not spectrally distinguishable in optical images, they 

can be identified by geographic position. The direction of the shadow occurrence 

relative to the cloud is the same for the entire scene as it depends on the sun illumination angle, a constant for any given scene. The location, form, and dimensions of 

any cloud shadow depend on the cloud shape and elevation, topography, and the sun 

illumination angle. 

The development of masks for identifying clouds and cloud shadows is our 

solution to develop a final cloud- and shadow-free Landsat ETM+ composite image. 

Our principle is to identify clouds and shadows in a reference image and replace 

them with cloud- and shadow-free parts from other dates. Masks facilitate operations such as subtraction and replacement of specific areas, as well as buffering, 

intersection, and union. 

We have developed simple semiautomated procedures to generate cloud and 

shadow masks in tropical regions for use in the creation of cloud-free composite 

Landsat ETM+ imagery, by using multiple recent images acquired at different times 

over the same region. Our goals in developing these methods were to make use of 

spectral and geographic information to facilitate the identification of cloud cover, 

separate cloud and topographic shadows, and minimize the needed masked areas. 
Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal

In addition to the masking method, we have incorporated additional processes 

for the creation of the final imagery data set. We sought to minimize differences 

in reflectance owing to atmospheric conditions by correcting for Rayleigh scatter 

and differences in seasonality of vegetation by using, whenever possible, imagery 

acquired during the same season. Aside from Rayleigh scatter correction, we have 

not incorporated any methods to normalize systematic scene-to-scene radiometric 

variation such as dark object subtraction (Chavez 1996). This would be advisable 

when classifying a composite image but unnecessary when classifying individual 

scenes before mosaicking. We also improved our ability to separate subpixel 

features (for urban or mangrove areas) by enhancing the spatial resolution incorporating the higher (15-m) resolution of the panchromatic band into the multispectral 

images. We have developed a useful image data set for the islands of Puerto Rico, 

Vieques, Culebra, and additional small islands within the Puerto Rican Archipelago. The methods to develop this data set will work equally well throughout the 

Caribbean basin and similar regions, and with Landsat ETM+ imagery acquired 

over a range of dates.

Data and Methods

Four Landsat ETM+ path-rows (4-47, 4-48, 5-47, and 5-48) are necessary to 

cover Puerto Rico and its adjacent islands of Vieques and Culebra. Our data set 

is composed of 18 images acquired between September 1999 and February 2003. 

During 2002, no images were incorporated owing to persistent dense cloud cover. 

The complete process leading to the resulting cloud- and cloud-shadow-free composite image can be accomplished in four major steps outlined in our methods, and 

subsequently applied to our 18 images. These include atmospheric correction, cloud 

and cloud-shadow masking, resampling and addition of the panchromatic band, and 

image selection and composition.

Atmospheric Correction

Atmospheric correction is necessary to put multitemporal data on the same 

radiometric scale, in order to create an image data set with compatible scenes that 

is useful for classification and monitoring terrestrial surfaces over time (Song et 

al. 2001). We used a model developed by the Institute for Astronomy and Space 

Physics IAFE-CONITET to convert radiance to reflectance corrected by Rayleigh 

scatter (Paolini et al. 2004). The input parameters required for running the model 

are included in the image header and include gain, bias, sun elevation angle, solar 

azimuth angle, and date. The model is described by the following formula: 
GENERAL TECHNICAL REPORT IITF-GTR-32

Surface reflectance = ð × (Lë - L Rayleigh) × r2/Eo (cos/ö) ,

Where

Lë = at satellite radiance = gainë × DN × biasë,

LRayleigh = radiance owing to Rayleigh scatter,

Eo = extraterrestrial irradiance,

r = Earth-Sun distance, and

ö = Sun elevation angle.

Each scene was processed using this formula before proceeding with the 

cloud and shadow masking. 

Cloud and Cloud-Shadow Masking

This involves two main procedures: (1) identifying clouds and developing a mask 

by using Landsat ETM+ brightness values (DN) in optical band 1 (blue) and in 

thermal band 6.1 and (2) developing a final cloud and cloud-shadow mask by using 

the cloud mask, the brightness values of shadowed areas in band 4 (near infrared), 

and the geometric relationship between shadow position and the clouds (i.e., 

parameters of cloud projection) (fig. 1).

Figure 1–Steps for masking clouds and cloud shadows. In step 1, the range of reflectance values for 

clouds are determined by visually inspecting the range of cloud types and digital numbers (DNs) in 

band 1 (blue) and in thermal band 6.1, and then selecting DNs in this range. In step 2, band 4 (near 

infrared), the geometric relationships of shadows to clouds, and the clouds mask, were used in the 

identification of cloud shadows.

step step

Landsat ETM+ image

Band 1 Band 6 Band 4

Parameters of

cloud projection

1 2

Cloud mask Cloud and cloud-shadow mask


Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal

Cloud Masking

The spatial resolution of the thermal band 6.1 was resampled from 60- to 30-m 

pixels, and subsequently bands 1 and 6 were analyzed by using the following 

procedures: 

• Brightness values for clouds were identified by visual analysis. For band 1, 

DN values between 120 and 255 include clouds as well as urban, barren, 

quarries, rocks, and sand. For band 6, DN values of 102 to 128 include both 

clouds and densely forested areas.

• A mask was created for band 1 and for band 6 by using the previous values. 

A three-pixel buffer was added to incorporate mixed pixels from cloud borders that could not be incorporated by brightness value analysis.

• The intersection of both masks results in a final clouds mask, which was 

also buffered by three pixels (fig. 2).

Cloud-Shadow Masking and Obtaining the Final Cloud 

and Cloud-Shadow Mask 

The first step in the creation of a cloud-shadow mask was to estimate the potential 

shadowed areas in the reference image by projecting the cloud mask on the land, 

and considering the effects of Sun illumination angle, topography, and cloud elevation (fig. 3). For this, an identifiable feature or point observable in both a cloud and 

its shadow was located in the image, ideally over low-elevation landscapes where 

clouds are at the greatest height above land (assuming a relatively constant elevation of clouds affecting the image). The difference in X and Y coordinates for these 

points is an estimation of the horizontal “shift” between cloud and shadow positions. A mask of potential cloud-shadow areas was created by displacement of the 

cloud mask based on the horizontal shift. To account for possible variations in the 

shadowed areas related to variation in cloud elevation and topography, a 10-pixel 

buffer was added. The resulting mask includes and overestimates all the areas with 

potential cloud shadows. Within these areas, the brightness values in band 4 were 

used to differentiate cloud-shadow areas from nonshadowed areas (fig. 2). In our 

imagery, the DN range 17 to 66 includes cloud shadows, but also some topographic 

shadows, certain urban areas, and land-water transition zones that are within 

potential cloud-shadow areas. Finally, cloud-shadow areas were added to the cloud 

mask to obtain a final cloud and cloud-shadow mask.


GENERAL TECHNICAL REPORT IITF-GTR-32

Figure 2—Flow chart illustrating cloud and cloud-shadow masking steps in an area of heterogeneous land cover and high cloud 

cover. A Landsat ETM+ subset image of the town of Ponce in southern Puerto Rico includes urban areas, cultivated land, pastures, 

quarries, barren areas, beaches, rivers, clouds, and shadows. The image is shown in band combination R (7) G (5) B (3). Three 

masks were developed from bands 1, 6, and 4, respectively. Band 1 mask (in yellow) includes clouds and some urban, barren, 

quarries, rocks, and sand. Band 6 mask (in yellow) includes clouds and some densely forested areas. Band 4 mask (in blue) includes 

cloud shadows, topographic shadows, certain urban areas, and land-water transition zones. Band 1 and band 6 masks are intersected 

for a mask of clouds only (yellow). This is joined with a cloud-shadow mask to develop the cloud-free reference image.

Parameters of

cloud-shadow

projection

Band 1 Band 6 Band 4

Band 1 mask (yellow) Band 6 mask (yellow) Band 4 mask (yellow)

Mask of clouds (yellow) and their

projection in the surface (orange)

Masks of clouds only (yellow) Reference Landsat image
Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal

Resolution Enhancement and Image Composition

One of our immediate uses of this composite image data set is to study land use 

and urban developments. Because much of the spatial variability in developed and 

undeveloped surfaces is related to roads and buildings that are subpixel features 

at 30-m resolution, we enhanced the spatial resolution of the images by merging 

the visible and IR bands (30- by 30-m pixel) with the panchromatic band (15- by 

15-m pixel) in all of our scenes by using Principal Component Analysis and Cubic 

Convolution Resampling in ERDAS Imagine 8.7. For the image composition the 

most cloud-free scene from each path/row was selected as the reference image, i.e., 

the top scene for each path/row in the mosaic. Cloud and cloud-shadow areas in 

reference images were replaced with data from other dates. 

Results

Our set of images ranged from 16.35 to 74.64 percent cloud and cloud-shadow 

coverage and contained acquisition dates in both the wet and dry seasons in Puerto 

Rico (table 1). Over half of the scenes had greater than 50 percent cloud cover. Two 

scenes were from 1999, eight from 2000, seven from 2001, none from 2002, and 

one from 2003. We qualitatively ranked the images into dry or wet season assisted 

by monthly rainfall data from different locations in the island, and visual analysis 

of the images. Seven scenes were from the dry season, nine from the wet season, 

Cloud-shadow X,Y Cloud X, Y

Ground surface

Cloud Buffer

Cloud

“Shift”

shadow Cloud

Figure 3—Representation of the geometric shift of cloud shadow plus buffer 

mask along X and Y axes that is determined by cloud elevation and Sun angle.
GENERAL TECHNICAL REPORT IITF-GTR-32

and two were transitional. The significance of identifying seasonality in the scenes 

is to maintain, when possible, phenological homogeny between images composing 

the mosaic. 

For the mosaic, one image from each path/row was selected as the reference 

image, i.e., the top scene for each path row in the mosaic. To determine which 

scene would be our reference, we chose scenes low in cloud cover and from the 

dry season. The primary reference image was path/row 5-47 from January 2003 

that covered the north, east, and center of the island and had low cloud cover (27.66 

percent). In addition, we used a Landsat 5 image from 1985, the only completely 

cloud-free image for eastern Puerto Rico at the time, to replace 5600 ha (0.65 

percent of the island) of medium- and high-elevation forested areas located within 

the Caribbean National Forest that had persistent cloud cover in all the recent 

images. We produced a 15-m Landsat ETM+ composite image data set that is 96.5 

percent cloud and cloud-shadow free. The final mosaic (fig. 4) is displayed by using 

the band combination R (7), G (5), B (3). Forested vegetation shows up as dark 

green, grassland and agricultural areas as pale green, urban and, dry agricultural 

areas as violet. Remaining cloud-covered areas (3.5 percent) are in black and will 

be replaced as cloud-free imagery becomes available. 

Table 1—Landsat ETM+ imagery data set description, including reference image number, path, row, region 

covered, acquisition date, season, and percentage of cloud cover

Reference image Path Row Region Acquisition date Season (wet or dry) Cloud cover

Percent

1 5 47 NW and Center PR January 22, 2003 d 27.66

2 5 47 NW and Center PR September 10, 2000 w 55.80

3 5 47 NW and Center PR July 27, 2001 w 47.01

4 5 47 NW and Center PR March 5, 2001 d 53.62

5 5 48 Southwest PR September 10, 2000 w 52.86

6 5 48 Southwest PR July 11, 2001 d 39.84

7 5 48 Southwest PR November 13, 2000 w 16.35

8 4 47 Northeast PR May 14, 2000 d-w 40.81

9 4 47 Northeast PR September 17, 1999 w 62.74

10 4 47 Northeast PR January 25, 2001 w 62.08

11 4 47 Northeast PR February 26, 2001 d-w 58.43

12 4 47 Northeast PR March 27, 2000 d 34.65

13 4 47 Northeast PR August 2, 2000 d 69.00

14 4 47 Northeast PR January 9, 2001 w 52.24

15 4 48 Southeast PR September 17, 1999 w 64.40

16 4 48 Southeast PR August 18, 2000 d 46.76

17 4 48 Southeast PR January 25, 2001 w 74.64

18 4 48 Southeast PR March 27, 2000 d 27.39

Final composite 3.50


Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal

Discussion

Remote-sensing tools and applications have become deeply integrated in a wide 

range of theoretical and applied ecological modeling. New problems and questions, 

new remote-sensing technologies, better computing facilities, and more sophisticated 

analyses are continually being developed. Advancements in optical remote sensing 

are particularly useful for characterizing landscapes in fast-changing environments 

where scientific expertise must keep pace with development and the needs of 

society. The ability to contribute to these advancements and use them for research 

and management issues depends on the ability to acquire and build data sets with 

complete historical and current coverage. It will always be difficult to obtain cloudfree data in cloud-prone environments, but applying simple methods of cloud and 

cloud-shadow detection, atmospheric correction, and image mosaicking can be 

beneficial in developing useful remote-sensing data sets. 

The major benefit of this methodology is its simplicity and straightforward 

approach, allowing cloud-free-image analyses by using the most comprehensive 

image data set for the study area and objectives. The distinctive point of our study is 

that we semiautomatically derive cloud and cloud-shadow masks from simple parameters. There are a number of instances in which this methodology can prove useful: 

Figure 4—Composite Landsat ETM+ image for Puerto Rico, Vieques, and Culebra, developed from a set of 18 images acquired 

between 1999 and 2003.

0 5 10 20 30 40

Kilometers

PUERTO RICO

CULEBRA

VIEQUES

o 67°15'0"W

67°15'0"W

67°0'0"W

67°0'0"W

66°45'0"W

66°45'0"W

66°30'0"W

66°30'0"W

66°15'0"W

66°15'0"W

66°0'0"W

66°0'0"W

65°45'0"W

65°45'0"W

65°30'0"W

65°30'0"W

65°15'0"W

65°15'0"W

17°51'0"N

17°51'0"N

18°0'0"N

18°0'0"N

18°9'0"N

18°9'0"N

18°18'0"N

18°18'0"N

18°27'0"N

18°27'0"N

18°36'0"N

18°36'0"N
10

GENERAL TECHNICAL REPORT IITF-GTR-32

• Our method for developing a cloud and cloud-shadow mask is useful for 

preparing the imagery for pixel-replacement procedures. Innovative strategies for the development of cloud-free imagery use regression trees to 

predict values underneath clouds and cloud shadows in reference scenes 

based scenes from other dates (Helmer and Ruefenacht 2005). The input 

images need to have clouds and cloud shadows already masked, which 

is originally solved by a combination of unsupervised classification and 

manual editing.

• Our method increases the functionality of Landsat TM and ETM+ data 

that have been acquired since 1982, and had been underutilized because 

of the presence of clouds, and adds to the set of images suitable for conducting modeling and land-cover change studies.

• The method highlights the importance of the thermal band in the Landsat 

TM and ETM+ sensors for the creation of cloud- and shadow-free images 

in tropical and other landscapes.

• The method can be incorporated into the research effort of the U.S. 

Geological Survey (USGS) Landsat 7 Project at the USGS EROS Data 

Center to increase the utility of the Landsat 7 ETM+ data for images 

acquired in tropical regions. 

The limitations of our methodology include, for the creation of the masks, 

the inability to discriminate between cloud shadow and topographic shadow 

within areas of potential shadow occurrence, and in the mosaicking process, 

issues common to any multiscene or multidate composite, i.e., atmospheric 

differences and temporal changes related to seasonality of vegetation or land use. 

We found that the atmospheric correction for Rayleigh scatter eliminated some of 

the variability between scenes related to atmospheric conditions. The best-case 

scenario is to identify and minimize likely temporal changes, or develop models 

to estimate expected spectral responses related to seasonality. In a region like 

Puerto Rico, with areas of the island having strong vegetation responses to dry 

and wet seasons, the best-case scenario would be to develop image composites 

for both the dry and wet seasons.

Acknowledgments 

This research was supported by the International Institute of Tropical Forestry 

(IITF) and the U.S. Geological Survey, Biological Resources Division, Gap 

Analysis Program (01-HQPG-003/01-IA-11120101-002). Thanks to Haydee 

Karszenbaum at the Institute for Astronomy and Space Physics IAFE-CONICET; 
11

Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal

Bonnie Ruefenacht, USDA Forest Service, Remote Sensing Applications Center; 

and Maya Quiñones and Eileen Helmer, USDA Forest Service, IITF. This research 

was supported by the IITF GIS and Remote Sensing Laboratory. All research at the 

IITF is done in collaboration with the University of Puerto Rico. 

English Equivalents

When you know: Multiply by: To get:

Meters (m) 3.28 Feet

Hectares (ha) 2.47 Acres

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