Mark Carroll

Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm

Abstract The first results of the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous field algorithms global percent tree cover are presented. Percent tree cover per 500-m MODIS pixel is estimated using a supervised regression tree algorithm. Data derived from the MODIS visible bands contribute the most to discriminating tree cover. The results show that MODIS data yield greater spatial detail in the characterization of tree cover compared to past efforts using AVHRR data. This finer-scale depiction should allow for using successive tree cover maps in change detection studies at the global scale. Initial validation efforts show a reasonable relationship between the MODIS-estimated tree cover and tree cover from validation sites.

Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data

Forest cover is an important input variable for assessing changes to carbon stocks, climate and hydrological systems, biodiversity richness, and other sustainability science disciplines. Despite incremental improvements in our ability to quantify rates of forest clearing, there is still no definitive understanding on global trends. Without timely and accurate forest monitoring methods, policy responses will be uninformed concerning the most basic facts of forest cover change. Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics. A probability-based sampling approach that synergistically employs low and high spatial resolution satellite datasets was used to quantify humid tropical forest clearing from 2000 to 2005. Forest clearing is estimated to be 1.39% (SE 0.084%) of the total biome area. This translates to an estimated forest area cleared of 27.2 million hectares (SE 2.28 million hectares), and represents a 2.36% reduction in area of humid tropical forest. Fifty-five percent of total biome clearing occurs within only 6% of the biome area, emphasizing the presence of forest clearing “hotspots.” Forest loss in Brazil accounts for 47.8% of total biome clearing, nearly four times that of the next highest country, Indonesia, which accounts for 12.8%. Over three-fifths of clearing occurs in Latin America and over one-third in Asia. Africa contributes 5.4% to the estimated loss of humid tropical forest cover, reflecting the absence of current agro-industrial scale clearing in humid tropical Africa.

Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data

The continuous fields Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products are 500-m sub-pixel representations of basic vegetation characteristics including tree, herbaceous and bare ground cover. Our previous approach to deriving continuous fields used a linear mixture model based on spectral endmembers of forest, grassland and bare ground training. We present here a new approach for estimating percent tree cover employing continuous training data over the whole range of tree cover. The continuous training data set is derived by aggregating high-resolution tree cover to coarse scales and is used with multi-temporal metrics based on a full year of coarse resolution satellite data. A regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multitemporal metrics. The automated algorithm was tested globally using Advanced Very High Resolution Radiometer (AVHRR) data, as a full year of MODIS data has not yet been collected. A root mean square error (rmse) of 9.06% tree cover was found from the global training data set. Preliminary MODIS products are also presented, including a 250-m map of the lower 48 United States and 500-m maps of tree cover and leaf type for North America. Results show that the new approach used with MODIS data offers an improved characterization of land cover.

Detection of land cover changes using MODIS 250 m data

The Vegetative Cover Conversion (VCC) product is designed to serve as a global alarm for land cover change caused by anthropogenic activities and extreme natural events. MODIS 250 m surface reflectance data availability was limited both spatially and temporally in the first year after launch due to processing system constraints. To address this situation, the VCC algorithms were applied to available MODIS 250 m Level 1B radiance data to test the VCC change detection algorithms presented in this paper. Five data sets of MODIS Level 1B 250 m data were collected for the year 2000, representing: (1) Idaho–Montana wildfires; (2) the Cerro Grande prescribed fire in New Mexico; (3) flood in Cambodia; (4) Thailand–Laos flood retreat; and (5) deforestation in southern Brazil. Decision trees are developed for each of the VCC change detection methods for each of these six cases. These decision trees are to be used for updating the look-up tables required by the VCC production code. For these change detection cases, the VCC change detection methods worked reasonably well. In the Idaho–Montana wildfire case, a fire perimeter polygon data set compiled by the USDA Forest Service was used to validate the output of the VCC change detection methods. Although the VCC output identified only 32% of the burned pixels within the ground observed Idaho–Montana fire perimeter polygons, the detection accuracy of the VCC output did reach 99% when the VCC product is considered as an alarm system identifying the occurrence of the change in an area. For other cases, the detection accuracy in per-pixel terms of the VCC output ranges from 55% to 90% against reference change bitmaps that were created by image interpretation. Look-up tables created with AVHRR and Landsat Thematic Mapper data require modifications for the MODIS data due to differences in radiometric response between MODIS and the heritage instruments. The applications presented in this paper also evaluate the relative performance of each of the five change detection methods used as VCC algorithms. Conclusions reached in this paper will be used for future refinement of the VCC product.

Estimation of tree cover using MODIS data at global, continental and regional/local scales

Comparisons of MODIS inputs appropriate to mapping land cover at different scales are made using global training data and a SAFARI 2000 validation database from western Zambia. Multiple single‐date images, 40‐day composites and multitemporal annual metrics from the MODIS sensor are tested in mapping percent tree cover. While the metrics outperform the composites at the global scale and are comparable to composites at the continental scale, composites outperform the metrics at the local/regional scale of the Zambia test area. Multiple single‐date MODIS imagery are best at mapping the test area, and this points to their utility in mapping at the local/regional scale. However, the overall difference between inputs is less than 1% in terms of standard error. This implies that the metrics and composites, with appropriate training data, can come close to replicating the spatial detail present in single‐date images. Comparing the Zambia test area data with a subset of the global MODIS percent tree cover map in a validation exercise shows that the general tree cover distribution is well represented. However, the global signatures underestimate Kalahari woodlands on sands and do poorly in overestimating tree cover in some dambos and pans. Further work will aim at refining the global signal using multiple validation sites.

Rapid Assessment of Annual Deforestation in the Brazilian Amazon Using MODIS Data

The Brazilian government annually assesses the extent of de- forestation in the Legal Amazon for a variety of scientific and policy applica- tions. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less

Shrinking lakes of the Arctic: Spatial relationships and trajectory of change

Extensive interplanetary scintillation (IPS) observations at 327 MHz obtained between 1983 and 2009 clearly show a steady and significant drop in the turbulence levels in the entire inner heliosphere starting from around ~1995. We believe that this large-scale IPS signature, in the inner heliosphere, coupled with the fact that solar polar fields have also been declining since ~1995, provide a consistent result showing that the buildup to the deepest minimum in 100 years actually began more than a decade earlier.

RELEVANCE OF RANGELAND DEGRADATION IN SEMIARID NORTHEASTERN SOUTH AFRICA TO THE NONEQUILIBRIUM THEORY

According to the nonequilibrium theory, livestock grazing has a limited effect on long-term vegetation productivity of semiarid rangelands, which is largely determined by rainfall. The communal lands in northeastern South Africa contain extensive degraded areas which have been mapped by the National Land Cover (NLC) program. Much evidence suggests that long-term heavy grazing is the cause of this degradation. In order to test for the prevalence of nonequilibrium dynamics, we determined the relative effects of rainfall- and grazing-induced degradation on vegetation productivity. The vegetation production in the NLC degraded areas was estimated using growth-season sums of the Normalized Difference Vegetation Index (sigmaNDVI), calculated using data from both the Advanced Very High Resolution Radiometer (AVHRR) (1985-2003) and Moderate-resolution Imaging Spectroradiometer (MODIS) (2000-2005). On average, rainfall and degradation accounted for 38% and 20% of the AVHRR sigmaNDVI variance and 50% and 33% of the MODIS sigmaNDVI variance, respectively. Thus, degradation had a significant influence on long-term vegetation productivity, and therefore the rangelands did not behave according to the nonequilibrium model, in which grazing is predicted to have a negligible long-term impact.

Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change

Climate change and degradation of ecosystem services functioning may threaten the ability of current agricultural systems to keep up with demand for adequate and inexpensive food and for clean water, waste disposal and other broader ecosystem services. Human health is likely to be affected by changes occurring across multiple geographic and time scales. Impacts range from increasing transmissibility and the range of vectorborne diseases, such as malaria and yellow fever, to undermining nutrition through deleterious impacts on food production and concomitant increases in food prices. This paper uses case studies to describe methods that make use of satellite remote sensing and Demographic and Health Survey data to better understand individual-level human health and nutrition outcomes. By bringing these diverse datasets together, the connection between environmental change and human health outcomes can be described through new research and analysis.

Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture

AbstractThe Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture...