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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Satellite Data Aid Monitoring of Nation's Forests

The USDA Forest Service’s Asheville, North Carolina-based Eastern Forest Environmental Threat Assessment Center and Prineville, Oregon-based Western Wildlands Environmental Threat Assessment Center partnered with Stennis Space Center and other agencies to create an early warning system to identify, characterize, and track disturbances from potential forest threats. The result was ForWarn, which is now being used by federal and state forest and natural resource managers.

Source record↗
NASA NTRS2014

MODIS NDVI Change Detection Techniques and Products Used in the Near Real Time ForWarn System for Detecting, Monitoring, and Analyzing Regional Forest Disturbances

This presentation discusses MODIS NDVI change detection methods and products used in the ForWarn Early Warning System (EWS) for near real time (NRT) recognition and tracking of regionally evident forest disturbances throughout the conterminous US (CONUS). The latter has provided NRT forest change products to the forest health protection community since 2010, using temporally processed MODIS Aqua and Terra NDVI time series data to currently compute and post 6 different forest change products for CONUS every 8 days. Multiple change products are required to improve detectability and to more fully assess the nature of apparent disturbances. Each type of forest change product reports per pixel percent change in NDVI for a given 24 day interval, comparing current versus a given historical baseline NDVI. EMODIS 7 day expedited MODIS MOD13 data are used to obtain current and historical NDVIs, respectively. Historical NDVI data is processed with Time Series Product Tool (TSPT); and 2) the Phenological Parameters Estimation Tool (PPET) software. While each change products employ maximum value compositing (MVC) of NDVI, the design of specific products primarily differs in terms of the historical baseline. The three main change products use either 1, 3, or all previous years of MVC NDVI as a baseline. Another product uses an Adaptive Length Compositing (ALC) version of MVC to derive an alternative current NDVI that is the freshest quality NDVI as opposed to merely the MVC NDVI across a 24 day time frame. The ALC approach can improve detection speed by 8 to 16 days. ForWarn also includes 2 change products that improve detectability of forest disturbances in lieu of climatic fluctuations, especially in the spring and fall. One compares current MVC NDVI to the zonal maximum under the curve NDVI per pheno-region cluster class, considering all previous years in the MODIS record. The other compares current maximum NDVI to the mean of maximum NDVI for all previous MODIS years.

Spruce, Joseph P.↗
NASA NTRS1990

Multiband radar characterization of forest biomes

The utility of airborne and orbital SAR in classification, assessment, and monitoring of forest biomes is investigated through analysis of orbital synthetic aperature radar (SAR) and multifrequency and multipolarized airborne SAR imagery relying on image tone and texture. Preliminary airborne SAR experiments and truck-mounted scatterometer observations demonstrated that the three dimensional structural complexity of a forest, and the various scales of temporal dynamics in the microwave dielectric properties of both trees and the underlying substrate would severely limit empirical or semi-empirical approaches. As a consequence, it became necessary to develop a more profound understanding of the electromagnetic properties of a forest scene and their temporal dynamics through controlled experimentation coupled with theoretical development and verification. The concatenation of various models into a physically-based composite model treating the entire forest scene became the major objective of the study as this is the key to development of a series of robust retrieval algorithms for forest biophysical properties. In order to verify the performance of the component elements of the composite model, a series of controlled laboratory and field experiments were undertaken to: (1) develop techniques to measure the microwave dielectric properties of vegetation; (2) relate the microwave dielectric properties of vegetation to more readily measured characteristics such as density and moisture content; (3) calculate the radar cross-section of leaves, and cylinders; (4) improve backscatter models for rough surfaces; and (5) relate attenuation and phase delays during propagation through canopies to canopy properties. These modeling efforts, as validated by the measurements, were incorporated within a larger model known as the Michigan Microwave Canopy Scattering (MIMICS) Model.

Dobson, M. Craig↗
DOE OSTI2015

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. In conclusion, improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Lastly, our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Seed classification with random forest models

Premise: To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators. Methods and Results: Seeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species-classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species. Conclusions: This protocol is an adaptable tool for efficient and consistent identification of seed species or potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large-scale monitoring projects in conservation biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Panama tropical forest digital camera imagery for vegetation phenology, Dec2016-May2019, PA-SLZ, PA-PNM, PA-BCI

Tropical forest phenology monitoring undertaken by use of Wingscapes timelapse cameras (phenocams) at three tropical evergreen forests in Panama: San Lorenzo, Parque Natural Metropolitano, and Barro Colorado Island. Following installation in December 2016, images were acquired every 10 minutes from 10 am to 2 pm daily, ending in May 2019. A total of 37 cameras were mounted on towers and tree branches, and view sunlit canopy or understory forest as follows: PA-SLZ, 11 canopy, 14 understory; PA-PNM, 6 canopy, 2 understory, PA-BCI, 4 canopy (trees also monitored for sap flow). This data package contains a full data description, metadata, species identification guides and summary files showing example fields of view from each camera location as csv and pdf files. The data description provides instructions for download of the full resolution image files, and other summary data products stored on external servers.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Early Lessons on Combining Lidar and Multi‑baseline SAR Measurements for Forest Structure Characterization

The estimation and monitoring of 3D forest structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D forest structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements pro-vided by the two configurations different and, when it comes to the sensitivity to individual 3D forest structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D forest structure estimation has been recognised from the very beginning. However, until today there is no established frame-work for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different forest types. Then, current perspectives on their combination for the generation of enhanced structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a structure-based framework based on the ability of lidar and SAR measurements to express physical forest structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.

Matteo Pardini↗
NASA NTRS1993

(abstract) Monitoring Seasonal Change in Taiga Forests Using ERS-1 SAR Data

Sensitivity of radar backscatter to the dielectric and geometric character of forested regions suggests significant changes in backscatter are expected with season due to freezing temperatures, snow, wind, leaf fall, and drought. The first European Remote Sensing Satellite, ERS-1, offers a unique opportunity to monitor a complete seasonal cycle for the Alaskan taiga forest ecosystem with synthetic aperture radar. During the 3-day repeat Commissioning Phase of ERS-1, from August 1991to December 1991, ERS-1 SAR data were collected in the region of Manley Hot Springs, Alaska, along the Tanana River, west of Fairbanks. In parallel with the SAR data collection, meteorological data from three weather stations positioned in three forest stands were collected continuously along with in situ measurements of the dielectric and moisture properties of the canopy and of ground cover which were collected during each overflight. The in situ data were collected in floodplain forest stands dominated by balsam poplar, white spruce, and black spruce. These results from the Commissioning Phase as well as preliminary results from the 35-day Repeat Phase will be presented.

characteristics geometric characteristics moisture↗
NASA NTRSDate not supplied

On the Advantages of Using Harmonized Landsat Sentinel-2 Data for Monitoring Environmental Change

NASA coordinates the Satellite Needs Working Group, dedicated to identifying, communicating, and addressing Earth observation needs of federal agencies. In 2016, the Harmonized Landsat Sentinel-2 (HLS) dataset was formulated and implemented to fulfill multiple needs. The combination of acquisitions from the Landsat and Sentinel-2 platforms results in a global dataset of surface reflectance with a temporal resolution of two days, while retaining the geometry and 30-meter spatial resolution of Landsat data. This harmonization allows for seamless integration with the 40-year archive of Landsat data. The HLS dataset is now available on the Google Earth Engine, enabling HLS utilization in various algorithms and frameworks essential for monitoring environmental change worldwide. During this presentation, we will demonstrate and discuss the advantages of using HLS data in comparison to using separate streams of Landsat and Sentinel-2 data in existing time series-based frameworks for change monitoring. Specifically, we will explore the application of HLS for continuous monitoring of deforestation using time series-based algorithms traditionally run with Landsat data. Additionally, we will showcase the benefits of HLS data for near real-time monitoring of forest disturbance in tropical regions. These examples underscore the value and utility of the HLS dataset for environmental monitoring and analysis.

Pontus Olofsson↗
DOE OSTI2016

Satellite Earth observation data to identify climate and anthropogenic pressures on Bucharest periurban forests

Satellite Earth observation data in the visible and near-infrared (VNIR) wavelengths represent a useful source of information for forest systems monitoring through derived biogeophysical parameters (vegetation index, leaf area index, canopy cover, fraction of absorbed photosynthetically active radiation, chlorophyll content, net primary production, canopy water stress, etc.). Use of satellite remote sensing data to assess forest spatio-temporal changes due to climatic or anthropogenic stressors is an excellent example of the value of multispectral and multitemporal observations. Fusion technique was applied to time-series multispectral and multitemporal satellite imagery (NOAA AVHRR, MODIS Terra/Aqua, Landsat ETM and IKONOS satellite data) for periurban forest areas Cernica-Branesti, placed in the neighboring of Bucharest town, Romania, over 2002-2014 period.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Evaluation of C-band SAR data from SAREX 1992: Tapajos study site

As part of the SAREX'92 (South American Radar Experiment), the Tapajos study site, located in Para State, Brazil was imaged by the Canada Center for Remote Sensing (CCRS) Convair 580 SAR system using a C-band frequency in HH and VV polarization and 3 different imaging modes (nadir, narrow, and wide swath). A preliminary analysis of this dataset is presented. The wide swath C-band HH polarized image was enlarged to 1:100,000 in a photographic form for manual interpretation. This was compared with a vegetation map produced primarily from Landsat Thematic Mapper (TM) data and with single-band and color composite images derived from a decomposition analysis of TM data. The Synthetic Aperture Radar (SAR) image shows well the topography and drainage network defining the different geomorphological units, and canopy texture differences which appear to be related to the size and maturity of the forest canopy. Areas of recent clearing of the primary forest can also be identified on the SAR image. The SAR system appears to be a source of information for monitoring tropical forest which is complementary to the Landsat Thematic Mapper.

Shimabukuro, Yosio Edemir↗
NASA NTRS2012

View Angle Effects on MODIS Snow Mapping in Forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.

MODIS↗
DOE OSTI2021

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827-2807 km2 for Senegal and 245-1271 km2 for The Gambia with one outlier for each country. We further report "Places of Agreement" (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.

Mondal, Pinki↗
DOE OSTI2015

SPRUCE Environmental Monitoring Data: 2010-2016

This data set reports selected ambient environmental monitoring data from the S1 bog for the period June 2010 through December 2016. Measurements of the environmental conditions at these stations serve as a pre-treatment baseline for experimental treatments and provide driver data for future modeling activities. There are/were three monitoring sites located in the bog: Stations 1 and 2 are co-located at the southern end of the bog and Station 3 is located north central and adjacent to an existing U.S. Forest Service monitoring well. The stations are equipped with standard sensors for measuring meteorological parameters, solar radiation, soil temperature and moisture, and groundwater temperature and elevation. All measurements were made at the 8.1-ha S1 bog forest site in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). See Table 1 for a list of measurements.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Collected Data of The Boreal Ecosystem and Atmosphere Study (BOREAS)

The Boreal Ecosystem-Atmosphere Study (BOREAS) was a large-scale international interdisciplinary climate-ecosystem interaction experiment in the northern boreal forests of Canada. Its goal was to improve our understanding of the boreal forests -- how they interact with the atmosphere, how much CO2 they can store, and how climate change will affect them. BOREAS wanted to learn to use satellite data to monitor the forests, and to improve computer simulation and weather models so scientists can anticipate the effects of global change. This BOREAS CD-ROM set is a set of 12 CD-ROMs containing the finalized point data sets and compressed image data from the BOREAS Project. All point data are stored in ASCII text files, and all image and GIS products are stored as binary images, compressed using GZip. Additional descriptions of the various data sets on this CD-ROM are available in other documents in the BOREAS series.

Newcomer, J.↗