Publications

Semiarid grasslands contribute significantly to net terrestrial carbon flux as plant productivity and heterotrophic respiration in these moisture-limited systems are correlated with metrics related to water availability (e.g., precipitation, Actual EvapoTranspiration or AET). These variables are also correlated with remotely sensed metrics such as the Normalized Difference Vegetation Index (NDVI). We used measurements of growing season net ecosystem exchange of carbon (NEE), NDVI from eMODIS and AVHRR, precipitation, and volumetric soil water content (VSWC) from grazed pastures in the semiarid, shortgrass steppe to quantify the correlation of NEE with these driving variables. eMODIS NDVI explained 60 and 40% of the variability in daytime and nighttime NEE, respectively, on non-rain days; these correlations were reduced to 41 and 15%, respectively, on rain days. Daytime NEE was almost always negative (sink) on non-rain days but positive on most rain days. In contrast, nighttime NEE was always positive (source), across rain and non-rain days. A model based on eMODIS NDVI, VSWC, daytime vs. nighttime, and rain vs. non-rain days explained 48% of observed variability in NEE at a daily scale; this increased to 62% and 77%, respectively, at the weekly and monthly scales. eMODIS NDVI explained 50–52% of the variability in AET regardless of rain or non-rain days. A model based on eMODIS NDVI, VSWC, Potential EvapoTranspiration (or PET), and rain vs. non-rain days explained 70% of the observed variability in AET at a daily scale; this increased to 90 and 96%, respectively, at weekly and monthly scales. Models based on AVHRR NDVI showed similar patterns as those using eMODIS, but correlations with observations were lower. We conclude that remotely-sensed NDVI is a robust tool, when combined with VSWC and knowledge of rain events, for predicting NEE and AET across multiple temporal scales (day to season) in semiarid grasslands.

The goal of NASA's land long term data record (LTDR) project is to produce a consistent long term data set from the AVHRR and MODIS instruments for land climate studies. The project will create daily surface reflectance and normalized difference vegetation index (NDVI) products at a resolution of 0.05deg, which is identical to the climate modeling grid (CMG) used for MODIS products from EOS Terra and Aqua. Higher order products such as burned area, land surface temperature, albedo, bidirectional reflectance distribution function (BRDF) correction, leaf area index (LAI), and fraction of photosynthetically active radiation absorbed by vegetation (fPAR), will be created. The LTDR project will reprocess global area coverage (GAC) data from AVHRR sensors onboard NOAA satellites by applying the preprocessing improvements identified in the AVHRR Pathfinder II project and atmospheric and BRDF corrections used in MODIS processing. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and near infrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view (IFOV). Atmospheric corrections for Rayleigh scattering, ozone, and water vapor are undertaken, with aerosol correction being implemented. The LTDR also produces a surface reflectance product for channel 3 (3.75 mum). Quality assessment (QA) is an integral part of the LTDR production system, which is monitoring temporal trends in the AVHRR products using time-series approaches developed for MODIS land product quality assessment. The land surface reflectance products have been evaluated at AERONET sites. The AVHRR data record from LTDR is also being compared to products from the PAL (pathfinder AVHRR land) and GIMMS (global inventory modeling and mapping studies) systems to assess the relative merits of this reprocessing vis-a-vis these existing data products. The LTDR products and associated information can be found at http://ltdr.nascom.nasa.gov/ltdr/ ltdr.html.

No abstract is provided for this article.

Fire is a major disturbance in the boreal forest, and has been shown to release significant amounts of carbon (C) to the atmosphere through combustion. However, less is known about the effects on ecosystems following fire, which include reduced productivity and changes in decomposition in the decade immediately following the disturbance. In this study, we assessed the impact of fire on net primary productivity (NPP) in the North American boreal forest using a 17‐year record of satellite NDVI observations at 8‐ km spatial resolution together with a light‐use efficiency model. We identified 61 fire scars in the satellite observations using digitized fire burn perimeters from a database of large fires. We studied the postfire response of NPP by analyzing the most impacted pixel within each burned area. NPP decreased in the year following the fire by 60–260 g C m −2 yr −1 (30–80%). By comparing pre‐ and postfire observations, we estimated a mean NPP recovery period for boreal forests of about 9 years, with substantial variability among fires. We incorporated this behavior into a carbon cycle model simulation to demonstrate these effects on net ecosystem production. The disturbance resulted in a release of C to the atmosphere during the first 8 years, followed by a small, but long‐lived, sink lasting 150 years. Postfire net emissions were three times as large as from a model run without changing NPP. However, only small differences in the C cycle occurred between runs after 8 years due to the rapid recovery of NPP. We conclude by discussing the effects of fire on the long‐term continental trends in satellite NDVI observed across boreal North America during the 1980s and 1990s.

Comprehensive assessments of Paraguay's forest cover (FC) change from the 1970s to the 2000s using Landsat observations were conducted, including a wall-to-wall mapping of changes across the whole country between the 1990s and 2000s, and an assessment of forest area in the Atlantic Forest ecoregion in the 1970s using a systematic sampling approach. The derived wall-to-wall FC change map was evaluated using available high resolution satellite images and aerial photos. The overall accuracy values were 92% or higher in the areas covered by those high resolution data sets. The results revealed that the Atlantic Forest ecoregion experienced the most forest loss, with the 73.4% forest cover in the 1970s decreasing precipitously down to 40.7% by the 1990s and further down to 24.9% by the 2000s. The rapid loss of Atlantic forests was driven by complex social economic forces, including widespread land disputes arising from long time inequalities and profits from exporting agricultural products. Forest changes in the Humid Chaco and the Chaco ecoregions were relatively moderate. However, extensive forests were converted to non-forest land use near a major population center. The results also revealed that so far the established protected areas were effective in protecting forest within their border. However, most of the forests surrounding the protected areas were lost by the 2000s. Loss of Atlantic forest is a major threat to the rich biodiversity found in this region. The alarming deforestation rates over the last three decades and the low percentage of Atlantic forest left by the 2000s call for immediate actions to halt the trends of forest loss.

The consistent monitoring of trees both inside and outside of forests is key to mitigating climate change. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we make use of the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 demonstrates that a precise assessment of all tree-based systems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover, such as in croplands and grassland. Such accurate mapping of tree cover at metric resolution down to the level of individual trees and consistent among countries has the potential to redefine land use impacts, move beyond the need for forest definitions, build the basis for natural climate solutions, and provide a new scientific basis for tree related studies.

Coarse spatial resolution, high temporal frequency satellite data from the NOAA/AVHRR system are presented to demonstrate their utility for monitoring vegetation seasonal dynamics. The techniques for processing and analysing the data are outlined and examples are given for selected applications at a range of scales. Normalized difference vegetation index images are presented for the entire globe and for the continents of Africa, South America and south-east Asia, with descriptions of the seasonal dynamics of major vegetation formations as portrayed by the transformed AVHRR data. Monitoring of forest clearance in Brazil, the productivity of African grasslands, Indian tropical forest and Chinese agriculture are selected for discussion. The paper concludes that coarse-resolution satellite data provide a valuable tool for vegetation mapping and monitoring at regional and global scales.