Anupam Anand

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Abstract We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas. Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs (RMSE =8.6% in 2000 and 11.9% in 2005), but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE=16.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover.org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multi-temporal depiction of Earths tree cover available to the Earth science community.

Homestays at Korzok: Supplementing Rural Livelihoods and Supporting Green Tourism in the Indian Himalayas

Abstract Ladakh presents some unique challenges for development and nature conservation initiatives. Since opening to tourism in 1974, its unique landscapes, biodiversity, and culture have attracted adventure-seeking tourists from all over the world. Tourism, especially in the past few decades, has developed very rapidly, bringing many economic and social changes with adverse environmental impacts. By recognizing the threats posed by unplanned tourism, a number of organizations and institutions working in the area felt the urgency to initiate sustainable community-based tourism projects that would protect the local wildlife and natural resources and offer alternative livelihood opportunities to the local population. The homestay model in Ladakh is designed on the principle of equitable access and hence is available for adoption by households across economic classes. This article uses the particular case of Korzok homestays, implemented by the World Wide Fund For Nature (WWF-India) near the Ramsar wetland Tsomoriri, to illustrate how community development needs can be integrated with conservation goals. It elaborates on the evolution of the initiative, some early impacts, and how the initiative might evolve in the future. A general list of recommendations is also presented.

Disaster Mitigation and Management and Importance of Earth Observation

Natural events such as earthquakes, floods, hurricanes, tsunamis have occurred throughout Earth’s history. These are caused by naturally occurring processes and are considered natural hazards and disasters because they negatively affect humans and destroy livelihoods. On the other hand, technological disasters like oil and chemical spills, pollution, industrial accidents, and massive automobile, train, or airplane accidents, are direct results of human activity.

Isolating type-specific phenologies through spectral unmixing of satellite time series

ABSTRACT Vegetation phenology is commonly studied using time series of multi-spectral vegetation indices derived from satellite imagery. Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing, and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution. We present an alternative method to mitigate this ‘mixed-pixel problem’ and extract the phenological behavior of individual land-cover types inferentially, by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping. Parameterized using genetic algorithms, the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red, near infrared, and short-wave infrared wavelengths, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index. In simulation, the unmixing procedure reproduced the reflectances and phenological signals of grass, crop, and deciduous forests with high fidelity (RMSE < 0.007 NDVI); and in empirical tests, the algorithm extracted the phenological characteristics of evergreen trees and seasonal grasses in a semi-arid savannah. The approach shows potential for a wide range of ecological applications, including detection of differential responses to climate, soil, or other factors among vegetation types.