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Remote Sensing Images of Ecoregions Remote Sensing Images of Ecoregions Remote Sensing Images of Ecoregions 131 132 Greater Mekong Subregion Atlas of the Environment Remote Sensing Images of Ecoregions he subregion’s ecoregions were introduced Interpretation in the Biodiversity section (see p. 69). Ecoregions represent the original distribution The remote sensing images use a range of colors Tof distinct groupings of plants and animals to offer visual presentations of the major land cover on earth. The WWF classification used here defines features. The images presented in the Atlas are 40 ecoregions in the subregion. Ecoregions are composed of band 5 (short-wave infrared), band 4 particularly important for understanding (infrared) and band 3 (red) in the red, green, and biodiversity. They also provide useful reference blue channel, respectively. This band combination points in establishing and planning the system of particularly enhances the vegetation and also protected areas in the subregion. Almost all of the discriminates between other land uses (e.g., ecoregions of the subregion fall within Indochinese agricultural area, urban area, and wetlands). In the subregion of the Indo-Malayan ecological realm. images, the various shades of green show vegetation Parts of the northernmost ecoregions, those located types, the pinkish tone depicts urban areas or barren in Yunnan Province, PRC and Myanmar, belong to land, and the blue color represents water bodies. The the Palearctic realm. darkest blue or black usually indicates seawater. Remote sensing images of the 40 ecoregions were To help interpret the features of these images, prepared at a scale of 1:100,000 to provide an each one has a legend with four or five land-cover overview of the subregion’s ground cover. The index types. These legends have been created using expert map opposite shows the locations of the images. judgment and no attempt has been made to ground- Each image has an inset index map that shows the truth the interpretations of the experts. location of the ecoregion and another inset index map that shows the location of the remote sensing Caution in interpretation is urged, as actual image within the ecoregion. conditions on the ground may be different from those implied by the legend. List of Remote Sensing Images of Ecoregions ECOREGION ECOREGION NAME REPRESENTATIVE REMOTE SENSING IMAGE PAGE CODE IM0101 Andaman Islands Rain Forests Koko Island, Myanmar 135 IM0106 Cardamom Mountains Rain Forests Kiri Saakor and Botum Saakor, Cambodia 136 IM0107 Chao Phraya Freshwater Swamp Forests North of Bangkok, Thailand 137 IM0108 Chao Phraya Lowland Moist Deciduous Forests Chonburi, Thailand 138 IM0109 Chin Hills-Arakan Yoma Montane Forests Haka, Myanmar 139 IM0116 Irrawaddy Freshwater Swamp Forests Ayeyarwady Delta near Yangon, Myanmar 140 IM0117 Irrawaddy Moist Deciduous Forests Sagaing, Myanmar 141 IM0119 Kayah-Karen Montane Rain Forests Chiang Mai, Thailand 142 IM0121 Luang Prabang Montane Rain Forests Louang Phrabang, Lao PDR 143 IM0131 Mizoram-Manipur-Kachin Rain Forests Sagaing, Myanmar 144 IM0132 Myanmar Coastal Rain Forests Rakhine Yoma, Myanmar 145 IM0136 Northern Annamites Rain Forests Nakai-NamTheun NBCA, Lao PDR and Vu Quang Nature 146 Reserve, Viet Nam IM0137 Northern Indochina Subtropical Forests Xishuangbanna, Yunnan Province, PRC 147 IM0138 Northern Khorat Plateau Moist Deciduous Forests Vientiane, Lao PDR and Nong Khai, Thailand 148 IM0139 Northern Thailand-Laos Moist Deciduous Forests Louang Phrabang, Lao PDR 149 IM0140 Northern Triangle Subtropical Forests Kachin State, Myanmar 150 IM0141 Northern Vietnam Lowland Rain Forests Thanh Hoa, Viet Nam 151 IM0144 Peninsular Malaysian Montane Rain Forests Yala, Thailand 152 IM0146 Peninsular Malaysian Rain Forests Yala, Thailand 153 IM0147 Red River Freshwater Swamp Forests Hanoi, Viet Nam 154 IM0149 South China-Vietnam Subtropical Evergreen Forests Quang Ninh, Viet Nam 155 IM0152 Southern Annamites Montane Rain Forests Da Lat, Viet Nam 156 IM0163 Tenasserim-South Thailand Semi-evergreen Rain Forests Phang Nga, Thailand 157 IM0164 Tonle Sap Freshwater Swamp Forests Battambang, Cambodia 158 IM0165 Tonle Sap-Mekong Peat Swamp Forests Prey Veng, Cambodia 159 IM0202 Central Indochina Dry Forests Nam Mun Basin, Thailand 160 IM0205 Irrawaddy Dry Forests Mandalay, Myanmar 161 IM0210 Southeastern Indochina Dry Evergreen Forests Veun Sai, Cambodia 162 IM0211 Southern Vietnam Lowland Dry Forests Nha Trang Area, Viet Nam 163 IM0402 Northern Triangle Temperate Forests Nanyun, Myanmar 164 IM1402 Indochina Mangroves Cau Mau, Viet Nam 165 IM1404 Myanmar Coast Mangroves Ayeyarwady Delta, Myanmar 166 PA0101 Gizhou Plateau Broadleaf and Mixed Forests Near Guizhou Provincial Border, 167 Yunnan Province, PRC PA0102 Yunnan Plateau Subtropical Evergreen Forests Kunming, Yunnan Province, PRC 168 PA0437 Sichuan Basin Evergreen Broadleaf Forests Northern Yunnan Province, PRC 169 PA0509 Hengduan Mountains Subalpine Conifer Forests Lijiang Prefecture, Yunnan Province, PRC 170 PA0516 Nujiang Langcang Gorge Alpine Conifer and Mixed Forests Chung Tien, Yunnan Province, PRC 171 PA0518 Qionglai-Minshan Conifer Forests Northern Yunnan Province, PRC 172 PA1003 Eastern Himalayan Alpine Shrub and Meadows Northernmost Portion of Kachin State, Myanmar 173 PA1017 Southeast Tibet Shrub and Meadows Northern Yunnan Province, PRC 174 Remote Sensing Images of Ecoregions 133 Technical Aspects involved, very different responses to the mechanisms of absorption, transmission, and reflection can occur The remote sensing images of ecoregions in this among the features and even for the same feature section were created from a data set of 127 scenes depending upon the time and status of the target. acquitted by the Landsat-7 Enhanced Thematic The figure above shows the different spectral Mapper plus (ETM+) during September 1999–April behavior of vegetation, soil, and water in different 2002. The original data were received in level-1 G wavelengths of the electromagnetic spectrum. (radiometrically and geometrically corrected) data product format. The level-1 G data stored in CD ROM in Hierarchical Data Format (HDF) and GeoTiff formats were downloaded using ERDAS software. Three bands were selected to create the false color composite (FCC) images. Band 5 (short wavelength infrared band) was presented in red, Band 4 (near infrared band) in green, and Band 3 (visible band) in blue. Because vegetation has a very high reflectance in Band 4 (0.775–0.900 m), it appears in green in the FCC. The remote sensing image preparation and analysis were done using ERDAS Imagine 8.4/8.5 and visual enhancement using ER Mapper 6.21; the cartographic work was carried out in ESRI ArcView 3.2a. Landsat-7 ETM+ Principles of Remote Sensing The Earth Resources Technology Satellite (ERTS) Program, part of the National Aeronautics Remote sensing is the science and art of obtaining and Space Administration’s (NASA) Earth information about an object, area, or phenomenon Resources Survey Program, launched the first of a through the analysis of data required by a device series of satellites (ERTS 1) in 1972. The ERTS that is not in contact with the object, area, or satellites were later known as Landsat to better phenomenon under investigation. In much of remote represent the civil satellite program’s prime sensing, the process involves an interaction between emphasis on remote sensing of land resources. incident radiation and the targets, e.g., earth features. Landsats 1, 2, and 3 carried the MultiSpectral Depending on the complexity of the target that is Scanner (MSS) sensor and experimental return beam being sensed and the wavelengths of radiation vidicon cameras. The Landsat 4 and 5 satellites carried the MSS and Thematic Mapper (TM) sensors. The launch of the sixth satellite in the Landsat series Characteristics of Landsat ETM+ was unsuccessful and did not achieve orbit. The Landsat 7 satellite, launched on 15 April 1999, was another step in the development and BAND SPECTRAL GROUND DATA LINES DATA LINE BITS PER MAJOR NUMBER RANGE (µ) RESOLUTION PER LENGTH SAMPLE APPLICATION application of remotely sensed satellite data for use (m) SCAN (bytes) in managing the earth’s land resources. The satellite carries the ETM+ sensor, an enhanced version of the 1 0.450–0.515 30 16 6,600 8 Soil/vegetation discrimination; bathymetry/coastal mapping; TM sensor aboard Landsats 4 and 5, which has the cultural/urban feature identifica- following orbital characteristics: tion • Altitude: 705 km, near polar, near circular • Inclination: sun-synchronous, 98.2° 2 0.525–0.605 30 16 6,600 8 Green vegetation mapping (measures reflectance peak); • Descending Node: 10:00 a.m. (+/- 15-minute cultural/urban feature identifica- equatorial crossing time) tion • Repeat cycle: 16 days, 233 orbits/cycle 3 0.630–0.690 30 16 6,600 8 Vegetated vs nonvegetated and • Swath: 183 km plant species discrimination • Period: 98.884 minutes (plant chlorophyll absorption); • Argument of Perigee: 90 degrees (+/- 40°) cultural/urban feature identifica- • Payload: the Enhanced Thematic Mapper tion Plus (ETM+), a single nadir-pointing 4 0.775–0.900 30 16 6,600 8 Identification of plant and instrument vegetation types, health and The ETM+ sensor is designed to collect, filter, biomass content, water body and detect radiation from the earth. Daytime data delineation, soil moisture are collected during the satellite’s descending mode, 5 1.550–1.750 30 16 6,600 8 Sensitive to moisture in soil and while nighttime data are collected during the vegetation; discriminating snow
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