Kyle Pittman

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.

Quantifying changes in the rates of forest clearing in Indonesia from 1990 to 2005 using remotely sensed data sets

Timely and accurate data on forest change within Indonesia is required to provide government, private and civil society interests with the information needed to improve forest management. The forest clearing rate in Indonesia is among the highest reported by the United Nations Food and Agriculture Organization (FAO), behind only Brazil in terms of forest area lost. While the rate of forest loss reported by FAO was constant from 1990 through 2005 (1.87 Mha yr −1 ), the political, economic, social and environmental drivers of forest clearing changed at the close of the last century. We employed a consistent methodology and data source to quantify forest clearing from 1990 to 2000 and from 2000 to 2005. Results show a dramatic reduction in clearing from a 1990s average of 1.78 Mha yr −1 to an average of 0.71 Mha yr −1 from 2000 to 2005. However, annual forest cover loss indicator maps reveal a near-monotonic increase in clearing from a low in 2000 to a high in 2005. Results illustrate a dramatic downturn in forest clearing at the turn of the century followed by a steady resurgence thereafter to levels estimated to exceed 1 Mha yr −1 by 2005. The lowlands of Sumatra and Kalimantan were the site of more than 70% of total forest clearing within Indonesia for both epochs; over 40% of the lowland forests of these island groups were cleared from 1990 to 2005. The method employed enables the derivation of internally consistent, national-scale changes in the rates of forest clearing, results that can inform carbon accounting programs such as the Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) initiative.

Estimating global cropland extent with multi-year MODIS data.

This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting to map croplands in aggregate.

Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA’s FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts’ ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a ‘convergence of evidence’ approach with meteorological data, field reports, crop models, attache reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring.

The global Landsat imagery database for the FAO FRA remote sensing survey

Abstract To collect and provide periodically updated information on global forest resources, their management and use, the United Nations Food and Agriculture Organization (FAO) has been coordinating global forest resources assessments (FRA) every 5–10 years since 1946. To complement the FRA national-based statistics and to provide an independent assessment of forest cover and change, a global remote sensing survey (RSS) has been organized as part of FAO FRA 2010. In support of the FAO RSS, an image data set appropriate for global analysis of forest extent and change has been produced. Landsat data from the Global Land Survey 1990–2005 were systematically sampled at each longitude and latitude intersection for all points on land. To provide a consistent data source, an operational algorithm for Landsat data pre-processing, normalization, and cloud detection was created and implemented. In this paper, we present an overview of the data processing, characteristics, and validation of the FRA RSS Landsat dataset. The FRA RSS Landsat dataset was evaluated to assess overall quality and quantify potential limitations.

Gross forest cover loss in temperate forests: biome-wide monitoring results using MODIS and Landsat data

The temperate forest is a complex biome due to the diversity of forest types, forest cover change dynamics and forest use management practices. While temperate forests play an important role in the global carbon cycle, their net carbon exchange is uncertain. Quantifying forest cover change is an important step in documenting disturbance regimes and carbon exchange estimates. Biome-wide gross forest cover loss was estimated using a probability-based sampling approach that integrated moderate and high spatial resolution satellite data sets. Area of gross forest cover loss from 2000 to 2005 within the temperate forest biome is estimated to be 1.03% of the total biome area, or 18.41 Mha. Estimated forest cover loss represented a 3.5% reduction in year 2000 forest area. About 68% of the total forest cover loss occurred in Eastern North America and in Europe. The mid-latitude forests of the United States exhibited the highest forest cover loss rates within the biome. Biome-wide rate of gross forest cover loss gradually increased from 2001 to 2005. The lowest change was detected in 2004, followed by the year of the highest change over the 5-year period. The regional forest cover change dynamics were confirmed by official forest fire and timber production statistics. The validation of the MODIS-based product demonstrated its efficiency in forest cover mapping and monitoring. Forest cover change monitoring using the approach presented should bring greater understanding on forest cover dynamics in temperate forests and enable improved carbon accounting.