SourCeBook oN remoTe SeNSiNg aND BioDiverSiTy iNDiCaTorS

Recommendations

Info

Sourcebook on Remote Sensing and Biodiversity Indicators 4.2.2 Forest fragmentation The destruction and fragmentation of natural habitats (including forests) is widely reported as the most significant driver in the global decline in biodiversity. Riitters et al. ( 004) created digital forest fragmentation maps from GLCC land cover maps (AVHRR, circa 199 ) where each pixel value represents a forest fragmentation category for the surrounding 81 square kilometre landscape. Repetition of this mapping assessment would provide a general global overview and highlight coarse spatial trends, but it will be unsuitable for addressing most biodiversity concerns resulting from forest fragmentation: ecological impacts on species operate at finer scales. Mapping forest fragmentation has been demonstrated using moderate-resolution imagery and ancillary data for entire countries such as the United States (Heilman et al. 00 ; Riitters et al. 00 ), which could be applied to other nations regardless of their size. (More in-depth discussion, including issues of data, metrics, standards, and scale in mapping fragmentation can be found in chapter 10.) 4.2.3 Area and location of old-growth forests Because of the importance of old-growth forests to native biodiversity and its continuing decline, it is important in any global/national survey strategy to monitor where these forests exist and how they change over time. While still an area of active research for remote sensing, currently age (or area of old growth) in some forest community types can be mapped with moderate to high spatial and thematic accuracy. Most studies on mapping forest age (including old growth) have been conducted in temperate forest ecosystems, especially those dominated by conifers. For example, temperate rainforests have been successfully mapped based on moderate-resolution remote sensing data using a number of different analytical techniques. Cohen et al. (1995) used unsupervised classification to map discrete forest age classes in the U.S. Pacific Northwest and later mapped the same region modelling continuous age (Cohen et al. 001). Ohmann and Gregory ( 00 ) employed direct gradient analysis and nearest neighbor imputation to predict detailed site conditions. Jiang et al. ( 004) mapped forest age in the same region, using a technique called “optimal iterative unsupervised classification,” which resulted in accuracies between 80–90 percent for broadly defined age classes (figure 4.5). Old-growth forests also have been mapped for portions of the boreal forests of Russia (Aksenov et al. 1999), montane regions of tropical America (Costa Rica: Helmer et al. [ 000]; Honduras: Aguilar [ 005]), and oak-pine forests of western Mexico (Lammertink et al. 007). In almost every case, ancillary data (e.g., slope, elevation, roads, and ownership) were employed to help interpret remote sensing signatures. In tropical forests, multitemporal remote sensing data has been shown to be necessary (Kimes et al. 1998). Using moderate resolution imagery is the most cost-effective method for mapping forest age over relatively large areas, but the nextgeneration sensors are pushing the level of spatial and thematic detail even more. There is a growing body of knowledge about forest age, using high-resolution sensors such as IKONOS and airborne platforms (Franklin et al. 001; Clark et al. 003; Nelson et al. 003). Although the various techniques mentioned here have proven to be feasible and reliable for mapping forest age, the future of mapping this important forest characteristic will be based on either active sensors (e.g., radar or lidar) alone (Drake et al. 00 ) or through a combination of these sensors with moderate- (e.g., Landsat TM) or high-resolution (e.g., IKONOS) imagery (Lefsky et al. 00 ). There are always pros and cons to every forest survey decision. For some forest biomes, it is feasible in terms of quality of the results and cost today to map forest age with reasonable accuracy with moderate-resolution imagery at the national or subnational extent. In situations where fine resolution and very narrow age classes are required, more effort, additional data, and more cost are also required. From the global perspective, narrowing the scope of work to regions of particular interest or biological importance 48
FIguRe 4.5 Map of old (dark green) and mature (light green) conifer forests in the u.S. Pacific Northwest (Jiang et al. 2004). Chapter 4. Trends in Selected Biomes, Habitats, and Ecosystems: Forests over the near term is encouraged until technological advances and cost reductions are realized, allowing for more geographically broad application. 4.2.4 Area and location of plantations Plantations continue to expand throughout the world, increasing by .8 million hectares per year during 000– 005 (figure 4.6). According to the definitions used by the most recent global forest assessment by FAO (FRA 005), plantations are defined as a subset of planted forests consisting primarily of exotic species. Plantation forests are further subdivided into two classes: forests planted for wood and fiber production (or productive plantations) and forests planted for protected soil and water (or protective plantations). Of the approximately 140 million hectares of plantations in the world (3.8 percent of all forest cover), 78 percent have been identified as productive plantations and percent as protective plantations (FRA 005). Plantations are generally of lesser biodiversity value than natural forests, especially when comprised of exotic species (Hunter 1999). Their expansion warrants monitoring, especially when they replace high-quality native forests. Because of their commercial value, plantations have been the focus of many remote sensing studies, usually over small geographic extents and for purposes other than biodiversity. Remote sensing studies of plantations have focused predominantly on mapping plantation characteristics such as timber volume (Trotter et al. 1997), age (Jensen et al. 1999; Ratnayake 006), and productivity (Coops et al. 1998). Fewer studies have addressed distinguishing plantations from native forests directly; nevertheless, some insights can be gleaned from these studies. Evans et al. ( 00 ) mapped land cover change, with particular interest in mapping the conversion of native forests to plantations for a portion of the southeastern United States, using a combination of Landsat TM and aerial photographs. Donahey ( 006) used multispectral images to map forest plantations in the Atlantic Forest region of Costa Rica. SPOT 4 imagery and Landsat TM were used to map vegetation changes in Central Sumatra with reasonable success in mapping plantation cover types (Trichon et al. 1999). All of these studies confirm the importance of integrating field data and usually a mixing of data from different sensors, including high resolution imagery. The use of moderate resolution images ( 0–30 metres) alone usually proves inadequate, unless the plantations cover very large areas. The UNEP World Conservation Monitoring Centre ( 007) has divided forest plantations into four classes (temperate/boreal exotic species plantation, temperate/boreal native species plantation, tropical exotic plantation, and tropical native plantation) and mapped them at a coarse scale, using AVHRR-based satellite images. This data set is too coarse to help address most biodiversity monitoring questions. Data at this scale will only identify major changes in forest cover, such as forest clearance. 49
Page 1: Secretariat of the Convention on Bi
Page 4 and 5: Sourcebook on Remote Sensing and Bi
Page 100 and 101:
Sourcebook on Remote Sensing and Bi
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
show all

SourCeBook oN remoTe SeNSiNg aND BioDiverSiTy iNDiCaTorS

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?