Kevin Price

Publications (23) View all

• Source

  Available from: Kevin Price

  Article: Identifying historical and recent land-cover changes in Kansas using post-classification change detection techniques

  Dana L. Peterson, Stephen L. Egbert, Kevin P. Price, Edward A. Martinko
  [show abstract] [hide abstract]
  ABSTRACT: Statewide land-cover change detection analysis provides a useful tool for conservation planning and environmental monitoring and addresses issues of habitat fragmentation and urban sprawl. Furthermore, land-cover data offer a historical and recent perspective on landscape dynamics. To this end, the first alliance level land-cover map of Kansas (Kansas Vegetation Map) recently completed by the KARS Program was compared to Küchler's Potential Natural Vegetation map and the 1993 Kansas Land Cover Patterns map. The post-classification change detection technique was used along with co-occurrence matrices to identify areas and directions of land-cover change. Comparisons showed that the land cover of Kansas has changed drastically since European settlement. Over 48% of the land is now cultivated and native vegetation types such as tallgrass and shortgrass prairie have been reduced dramatically in area. There are, however, millions of ha of these vegetation types remaining in Kansas. Comparisons between the two recent land-cover maps reveal that over 80% of the land in Kansas has remained unchanged in the five years between map development. Recent land-cover changes include conversion of grassland to cropland, cropland to grassland, and grassland to woodland. Many areas changing from cropland to grassland have been identified as land being enrolled in the Conservation Reserve Program (CRP). Post-classification change detection analysis also shows that forest and woodland types have increased over the five-year period and over 1 million ha of grassland have been converted to cropland. The magnitude of increases in woodland and forest is questionable, however, and may be due to registration errors and classification methodologies used to generate the land-cover maps.
  Transactions of the Kansas Academy of Science 01/2009;
• Source

  Available from: ku.edu

  Article: Soybean production and conversion of tropical forest in the Brazilian Amazon: the case of Vilhena, Rondônia.

  J Christopher Brown, Matthew Koeppe, Benjamin Coles, Kevin P Price
  [show abstract] [hide abstract]
  ABSTRACT: The recent rise in Brazilian soybean production has generated concern among the environmental community and some authors that natural tropical environments of the Amazon Basin are being converted to soybean fields. Proponents of soybean production counter that soybeans represent a viable agricultural commodity for the region; environmental concern is unwarranted, because new soybean fields are replacing already deforested or otherwise transformed lands. Both arguments have been made without comprehensive study and measure of land-use/land-cover (LULC) in areas undergoing expansion of soybean production. This case study, conducted in the municipality of Vilhena, Rondônia, Brazil, in the southwestern Amazon Basin, uses remote sensing to evaluate the LULC accompanying this municipality's large growth in soybean production from 1996 to 2001. Forests are being converted for soybean production, but most of the production increase appears attributable to slight expansion of already existing fields, conversion of already deforested land, and higher yields.
  AMBIO A Journal of the Human Environment 09/2005; 34(6):462-9. · 2.03 Impact Factor
• Source

  Available from: J. Christopher Brown

  Article: Expansion of Mechanized Agriculture and Land-Cover Change in Southern Rondônia, Brazil

  J Christopher Brown, Wendy Jepson, Kevin P Price
  [show abstract] [hide abstract]
  ABSTRACT: Ranchers, gold miners, loggers and smallholder colonists have been clearing forest in the Brazilian Amazon since the late 1960s. In the 1990s, new agents of environmental change arrived in the region: mechanized commercial farmers. Some cite statistical correlations between increases in region-wide soybean production and deforestation rates to suggest that soybean farmers are the next destroyers or "relentless foes" of the Amazon. It is argued that soybean cultivation will spread into frontier forests, resulting in deforestation and consolidation of landholdings. In turn, this consolidation is feared to create a rural exodus of peasants, leading to even further ad-vancement of forest destruction. Agro-industrialists, however, describe mechanized farming as a "win-win" scenario for development and conservation, arguing that it increases land-use intensification which reduces the need for clearing new land while allowing for economic development. No published empirical study has used spatially explicit data, human or physical, to measure or explain the mechanisms of forest change due to mechanized cultivation. Our current project engages this debate by seeking answers to three main questions: (1) To what extent are forests converted for mechanized annual cropping?; (2) What are the key human variables that explain land change patterns? (3) What is the relationship between land-use intensification and forest conversion? Our study area is a modern agricultural frontier in southern Rondônia, an area at the humid Amazon forest savanna transition that is poised to become another major soybean-producing region.
  Journal of Latin American Geography 01/2004; 3(1):96-103.
• Source

  Available from: Kevin Price

  Article: Biophysical and spectral characteristics of cool-and warm-season grasslands under three land management practices in eastern Kansas

  Xulin Guo, Kevin P Price, James M Stiles
  [show abstract] [hide abstract]
  ABSTRACT: Grasslands are the largest of the Earth's four major vegetation types and are among the most agriculturally productive lands. Grassland management practices alter biophysical fac-tors, such as plant species composition, soil structure and chemistry, and primary productivity. Large-scale changes in land management practices can have significant impacts on available natural resources, therefore impacting a regional socioeconomics. For these reasons, an im-proved understanding of the geographic distribution of changing land management practices and how these changes affect grassland biophysical characteristics of an area is critical to im-prove land use planning and practices. This study investigated the spectral characteristics, as measured using satellite remotely sensed data, of three land management practices [grazing, haying, and management under Conservation Reserve Program (CRP)] on areas dominated by cool-and warm-season grasslands. The satellite imagery was acquired in May, July, and September in 1997 over Douglas County, Kansas. Biomass, vegetation cover, and species rich-ness data were collected around the satellite overflight periods. Our results indicate that the biophysical characteristics and spectral reflectances are significantly different among the three management practices over cool-and warm-season grasslands. Interestingly, sites grazed by livestock had the highest species richness and the highest forb cover, although the total cover is almost the same as the other practices, and they have the highest near-infrared reflectance throughout the growing season.
  04/2001;
• Article: A Multivariate Analysis of Biophysical Parameters of Tallgrass Prairie Among Land Management Practices and Years

  Jerry A. Griffith, Kevin P. Price, Edward A. Martinko
  [show abstract] [hide abstract]
  ABSTRACT: Six treatments of eastern Kansas tallgrass prairie – native prairie, hayed, mowed, grazed, burned and untreated – were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference VegetationIndex (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine whichvariables were contributing to any significant difference. Results showed a significant difference (p < 0.0005)="" among="" treatments="" in="" the="" composite="" of="" parameters="" during="" each="" of="" the="" months="" sampled.="" in="" most="" treatment="" types,="" there="" was="" asignificant="" difference="" between="" years="" within="" each="" month.="" the="" univariate="" tests="" showed,="" however,="" that="" only="" some="" variables,="" primarily="" soil="" moisture,="" were="" contributing="" to="" this="" difference.="" we="" conclude="" that="" biomass="" and="" %="" plant="" cover="" show="" the="" best="" potential="" to="" serve="" as="" long-term="" indicators="" of="" grassland="" condition="" as="" they="" generally="" were="" sensitive="" to="" effects="" ofdifferent="" land="" management="" practices="" but="" not="" to="" yearlychange="" in="" weather="" conditions.="" ndvi="" was="" insensitive="" to="" precipitation="" differences="" between="" years="" in="" july="" for="" most="" treatments,="" but="" was="" not="" in="" the="" native="" prairie.="" choice="" of="" sampling="" time="" is="" important="" for="" these="" parameters="" to="" serve="" effectively="" as="">
  Environmental Monitoring and Assessment 01/2001; 68(3):249-271. · 1.40 Impact Factor