Excellent data on deforestation have been obtained in the tropics with the use of high-resolution optical sensors. Yet, several problems remain. Cloud cover creates data gaps that limit the possibility of complete and frequent assessments, and secondary growth is not well characterized. Active microwave sensors could complement these sensors because they operate independently of cloud cover and smoke and can detect differences in woody biomass and forest structure associated with various stages of forest clearing and regrowth. An example of comparison and synergy between the two techniques is discussed here. Polarimetric, C- (5.6 cm) and L-band (24 cm) frequency, radar data gathered in October 1994 by NASA's Spaceborne Imaging Radar C, on a test site southeast of the city of Porto Velho, in the state of Rondonia, Brazil, are analyzed in conjunction with one 1993 Landsat Thematic Mapper (TM) scene, a 9-year time series of Satellite pour l'observation de la Terre (SPOT) XS data, two Japan Earth Resources Satellite (JERS-1) radar images from 1994 and 1995, and a field visit conducted in 1995. The C-band radar data are found to be of limited utility for mapping deforestation. At L-band, multiple polarizations are required to obtain a reliable classification. The single polarization, L-band, single date, JERS-1 data underestimate the extent of deforestation, especially during the wet season. With multiple polarizations, six classes of land cover, including one level of regrowth, are mapped with 90% accuracy, but intermediate regrowth 5-8 years of age is not well separated from the forest. The Landsat TM data identify deforested areas better but provide less information on residual woody biomass levels. Combining the two classifications, seven classes of land cover including two levels of regrowth are mapped with 93% accuracy. The results show that the deforestation rate for 1994 was 1.7%. Large variations in residual woody biomass are detected among new clearings. Half of the total deforested land is in some stage of regrowth but most of it is less than 5 years old. Secondary growth is therefore a significant form or land use that is recleared quickly.

This place‐based case study in an agricultural county in California’s Central Valley focused on the period of 2010–2050, and dealt with biophysical and socioeconomic issues related to both mitigation of greenhouse gas (GHG) emissions and to adaptation to an uncertain climate. In the past 100 years, changes in crop acreage has been more related to crop price and availability of irrigation water than to growing degree days during summer, and in fact, summer temperatures have increased less than winter temperatures. Econometric analysis indicated that warmer winters, as projected by Geophysical Fluid Dynamics Laboratory‐Bias Corrected Constructed Analog during 2035–2050, could result in less wheat acreage, more alfalfa and tomato acreage, and slight effects on tree and vine crops. The Water Evaluation and Planning (WEAP) model showed that these econometric projections did not reduce irrigation demand under either the B1 or A2 scenarios, but a diverse, water‐efficient cropping pattern combined with improved irrigation technology reduced demand to 12 percent below the historic mean.   Collaboration during development of Yolo County’s Climate Action Plan showed that nitrous oxide (mainly from nitrogen fertilizers) was the main source (≅40 percent) of agricultural emissions. Emissions from cropland and rangeland were several orders of magnitude lower than urbanized land per unit area. A survey distributed to 570 farmers and ranchers achieved a 34 percent response rate. Farmers concerned about climate change were more likely to implement water conservation practices, and adopt voluntary GHG mitigation practices. Use of the urban growth model (UPlan) showed that channeling much or all future urban development into existing urban areas will increase ecosystem services by preserving agricultural land and open space, immensely reducing the Yolo County’s GHG emissions, and greatly enhancing agricultural sustainability