Remote Sensing Technology for Rangeland Management Applications

Abstract

ada Rena Paul has had over 30 years as student and professor working in the general area of Range Ecology and Management. He has initiated numerous studies concerned with range ecology, vegetation-soil relationships, range condition and trend, big game habitat management, brush encroachment, ecology of the pinyonjuniper woodland, mine waste reclamation, and remote sensing with emphasis on arid lands. Other qualifications include: commercial instrument rated pilot with over 3,000 hours, currently Associate Editor, JoumolofRanae Management, Certified Range. _ Management Consultant. Remote sensing applications PAUL T. TUELLER technology for rangeland management Tbe future of rangeland resmwces development and management in dependent upon increased scientific capability. Remote sensing technology can contribute information for a variety of rangeland resource management applications. In future we can expect to see an increased number of profnsion~l range managers with expertise in remote sensing. This training will include, in addition to principles of aerial photo interpretation, digttnl image analysis technology, increased use of geographic information systems, airborne video remote sensing, and the use of newly develop ing high resolution systems. The data will be obtained from both aircraft and spacecraft. Appltcations will include inventory, evaluation, and monitoring of rangeland resources and the incorporntion of remote senstng data to support and improve the decision processes on the use, development, and management of nngelnnd lesOurce area*.

Effects of Changing Spatial Scale on the Results of Statistical Analysis with Landscape Data: A Case Study

“…While our results seem to support this claim, the choice of appropriate sample size will be critically important to achieve high accuracy. On the other hand, vegetation indices derived from several bands using different mathematical formulations may indicate quantitative and qualitative differences in the properties of vegetation because significant differences in reflectance and absorption of radiation exist between vegetation and other geographical characteristics of the landscape (Tueller, 1989). According to our analysis, normalized difference vegetation index (NDVI) appeared to be better than RVI and TNDVI for characterizing local-scale diversity and richness pattern in this particular desert landscape (Table 2).…”

“…According to our analysis, normalized difference vegetation index (NDVI) appeared to be better than RVI and TNDVI for characterizing local-scale diversity and richness pattern in this particular desert landscape (Table 2). Numerous studies have shown that NDVI is a sensitive indicator of green biomass (Tucker, 1979, Tueller, 1989). Out study suggested that, together with TM3 and TM7, NDVI was a good predictor of diversity and richness in the landscape of our study.…”

“…Landsat Thematic Mapper (TM) and NOAA satellite AVHRR data, in particular, have been widely adopted in landscape ecological studies. Based on the features of reflectance and absorption of vegetation to electromagnetic radiation, a number of vegetation indices have been developed from several TM bands (e.g., Tueller, 1989). Both the spectral values of the different TM bands and vegetation indices derived from them can be correlated with various characteristics of landscapes (e.g., Tueller, 1989, Rey-Benayas and Pope, 1995).…”

Mobility and Sustainability: A Computational Model of African Pastoralists

“…Perhaps, the most pressing extension is making the model's underlying ecological dynamics more realistic as well as more diverse. Here, the model can greatly benefit from a recent trend in rangeland ecology and pastoralism research: a growing body of research based on spatially as well as temporally fine-grained data on rangeland conditions, especially those derived from satellite imagery (Booth & Tueller, 2003;Egeru, 2014;Miller, 2015;Ruelland, Levavasseur, & Tribotté, 2010;Tueller, 1989). By fully employing these spatiotemporal data, the abstract and stylized aspects of the model can fruitfully be replaced with more data-driven dynamics.…”

Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

“…Vegetation indices use radiance, surface reflectance or apparent reflectance values in the red (R) and near infrared (NIR) spectral bands (Gitelson, 2004;Tucker et al 2005;Zoran & Stefan 2006;Brown et al 2006;Angerer 2009). These indices are correlated with various vegetation parameters such as green biomass (Mutanga et al 2012;Ullah et al 2012), chlorophyll concentration (Clevers & Kooistra 2012), leaf area index (Mountrakis et al 2011;Yang et al 2012) and photosynthetic activity (Tueller 1989;Huete et al 2002;Li et al 2010). …”