MODIS NDVI Multi-Temporal Analysis Confirms Farmer Perceptions on Seasonality Variations Affecting Apple Orchards in Kinnaur, Himachal Pradesh

Apple cultivation in the Kinnaur district of the northern Indian State of Himachal Pradesh faces challenges from climatic changes and developmental activities. Farmers in the neighboring districts have already faced a major loss of livelihood due to seasonal changes. Therefore, it is important to study the extent of seasonal variations in the apple growing locations of this region. This study makes that attempt by assessing seasonality variations during a 15-year period from 2004 to 2018 when maximum construction activities occurred in this region. The study uses geospatial and statistical techniques in addition to farmer perceptions obtained during a field visit in November 2019. A temporal pattern using a normalized difference vegetation index (NDVI) based on Moderate Resolution Imaging Spectroradiometer (MODIS) was studied for seven apple-growing locations in the district. The results show high seasonal variations and reduced snowfall at lower elevations, resulting in less chilling hours, which are necessary for the healthy growth of apples. The normalized difference snow index (NDSI) and rainfall show a high correlation with apple growth. Local farmers are unprepared for future seasonal disturbances, as they lack early warning systems, insurance for apple crops, and alternative livelihood options.

Non-Binary Snow Index for Multi-Component Surfaces

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

ESTIMATION OF SNOW DEPLETION CURVE FOR GANGOTRI BASIN USING MULTI-SOURCE REMOTE SENSING DATA

Snow Depletion Curve derived from satellite images is a key parameter in Snowmelt Runoff Model. The fixed temporal resolution of a satellite and presence of cloud cover in Himalayas restricts accuracy of generated SDC. This study presents an effective approach of reducing temporal interval between two consecutive dates by integrating normalized Snow Cover Area estimated from multiple sources of satellite data. SCA is extracted by using Normalized Difference Snow Index for six snowmelt seasons from 2013 to 2018 for Gangotri basin situated in Indian Himalayas. This work also explores potential of recently launched Sentinel-3A for estimating SCA. Normalized SCA is utilized to eliminate the effect of difference in spatial resolution of various satellites. The result develops an important linear relation between SDC and time with a decrease in snow cover of 0.005/day that may be further refined by increasing the number of snowmelt seasons. This relationship may help scientific community in understanding hydrological response of glaciers to climate change.

A Revised Snow Cover Algorithm to Improve Discrimination between Snow and Clouds: A Case Study in Gran Paradiso National Park

Snow cover plays an important role in biotic and abiotic environmental processes, as well as human activities, on both regional and global scales. Due to the difficulty of in situ data collection in vast and inaccessible areas, the use of optical satellite imagery represents a useful support for snow cover mapping. At present, several operational snow cover algorithms and products are available. Even though most of them offer an up-to-daily time scale, they do not provide sufficient spatial resolution for studies requiring high spatial detail. By contrast, the Let-It-Snow (LIS) algorithm can produce high-resolution snow cover maps, based on the use of both the normalized-difference snow index (NDSI) and a digital elevation model. The latter is introduced to define a threshold value on the altitude, below which the presence of snow is excluded. In this study, we revised the LIS algorithm by introducing a new parameter, based on a threshold in the shortwave infrared (SWIR) band, and by modifying the overall algorithm workflow, such that the cloud mask selection can be used as an input. The revised algorithm has been applied to a case study in Gran Paradiso National Park. Unlike previous studies, we also compared the performance of both the original and the modified algorithms in the presence of cloud cover, in order to evaluate their effectiveness in discriminating between snow and clouds. Ground data collected by meteorological stations equipped with both snow gauges and solarimeters were used for validation purposes. The changes introduced in the revised algorithm can improve upon the overall classification accuracy obtained by the original LIS algorithm (i.e., up to 89.17 from 80.88%). The producer’s and user’s accuracy values obtained by the modified algorithm (89.12 and 95.03%, respectively) were larger than those obtained by the original algorithm (76.68 and 93.67%, respectively), thus providing a more accurate snow cover map.

Glacier Retreat in Iceland Mapped from Space: Time Series Analysis of Geodata from 1941 to 2018

In recent decades, glaciers outside Greenland and Antarctica have shown increasingly rapid rates of mass loss and retreat of the ice front, which is associated with climatic and oceanic warming. Due to their maritime location, Icelandic glaciers are sensitive to short-term climate fluctuations and have shown rapid rates of retreat and mass loss over the last decade. In this study, historical maps (1941–1949) of the US Army Map Service (AMS series C762) and optical satellite imagery (Landsat 1, Landsat 5, Landsat 7, Landsat 8, and Sentinel-2) are used to study the Langjökull, Hofsjökull and Vatnajökull ice caps. By the help of the Normalized Difference Snow Index (NDSI), the glacier terminus fluctuations of the ice caps from 1973 to 2018 and the Equilibrium Line Altitude (ELA) from 1973 to 2018 are analyzed. The results are compared with climate data, especially with mean summer temperatures and winter precipitation. Due to the negative temperature gradient with increasing altitude, bivariate histograms are generated, showing the glaciated area per altitude zone and time, and providing a prediction of the future development until 2050 and beyond. The results indicate that Langjökull, Hofsjökull and Vatnajökull are retreating and advancing over the study period in correlation with the mean summer temperature, with a steady decrease over time being the clearest and most significant trend. The lower parts of the glaciers, thus, will probably disappear during the next decades. This behaviour is also evident by an exceptional increase of the ELA observed on all three glaciers, which leads to a reduction of the accumulation zone.

Multi-sensor remote sensing to map glacier debris cover in the Greater Caucasus, Georgia

Global warming is causing glaciers in the Caucasus Mountains and around the world to lose mass at an accelerated pace. As a result of this rapid retreat, significant parts of the glacierized surface area can be covered with debris deposits, often making them indistinguishable from the surrounding land surface by optical remote-sensing systems. Here, we present the DebCovG-carto toolbox to delineate debris-covered and debris-free glacier surfaces from non-glacierized regions. The algorithm uses synthetic aperture radar-derived coherence images and the normalized difference snow index applied to optical satellite data. Validating the remotely-sensed boundaries of Ushba and Chalaati glaciers using field GPS data demonstrates that the use of pairs of Sentinel-1 images (2019) from identical ascending and descending orbits can substantially improve debris-covered glacier surface detection. The DebCovG-carto toolbox leverages multiple orbits to automate the mapping of debris-covered glacier surfaces. This new automatic method offers the possibility of quickly correcting glacier mapping errors caused by the presence of debris and makes automatic mapping of glacierized surfaces considerably faster than the use of other subjective methods.

SPATIO-TEMPORAL CHANGE IN SNOW COVER AREA USING RS & GIS IN THE GORI GANGA WATERSHED, KUMAUN HIGHER HIMALAYA

Present research paper is an attempt to examine the dynamics of snow cover by using Normalized Difference Snow Index (NDSI) in Gori Ganga watershed, Kumaun Himalaya, Uttarakhand (India). For the study of snow cover of Landsat satellite imageries of three different time periods like Landsat TM of 1990, Landsat TM of 1999 and Landsat TM 2016 were used. Geographical distribution of snow cover reveals that in 1990 about 30.97% (678.87 km2), in 1999 about 25.77% (564.92 km2) area of the Gori Ganga watershed was under snow cover while in 2016 the snow cover was found only 15.08% (330.44 km2). These data suggest that due to global warming about 348.43 km2 snow cover of Gori Ganga watershed has been converted into non-snow cover area at an average rate 13.40 km2/year during the last 26 years.

Mapping Fluctuations of Hispar Glacier, Karakoram, using Normalized Difference Snow Index (NDSI) and Normalized Difference Principal Component Snow Index (NDSPCSI)

Investigation of the fluctuations in the snow-covered area of the major glaciers of the Karakoram range is essential for proper water resource management in Pakistan, since its glaciers are responding differently to the rising temperatures. The objective of this paper is to map snow covered area of Hispar glacier in Hunza river basin for the years 1990, 2010 and 2018. Two techniques, (NDPCSI) Normalized Difference Principal Component Snow Index and (NDSI) Normalized Difference Snow Index were used. Hispar glacier of the Hunza basin has lost 114 km2 of its ice cover area, during the last 28 years, with an alarming annual retreat rate of 1.67 km2 of glacier ice from 1990 to 2018. Hunza basin experienced a +1°C rise in both mean minimum and mean maximum temperature during 2007 to 2018.as a result, Karakorum ice reserves have been affected by rising temperature of the region. Due to temperature rise, retreat of snowcovered area of Hispar, Karakoram mountain range shows a shift in the cryospheric hazard zone.

Advanced Dual-Satellite Method for Detection of Low Stratus and Fog near Japan at Dawn from FY-4A and Himawari-8

The detection of low stratus and fog (LSF) at dawn remains limited because of their optical features and weak solar radiation. LSF could be better identified by simultaneous observations of two geostationary satellites from different viewing angles. The present study developed an advanced dual-satellite method (DSM) using FY-4A and Himawari-8 for LSF detection at dawn in terms of probability indices. Optimal thresholds for identifying the LSF from the spectral tests in DSM were determined by the comparison with ground observations of fog and clear sky in/around Japan between April to November of 2018. Then the validation of these thresholds was carried out for the same months of 2019. The DSM essentially used two traditional single-satellite tests for daytime such as the 0.65-μm reflectance (R0.65), and the brightness temperature difference between 3.7 μm and 11 μm (BTD3.7-11); in addition to four more tests such as Himawari-8 R0.65 and BTD13.5-8.5, the dual-satellite stereoscopic difference in BTD3.7-11 (ΔBTD3.7-11), and that in the Normalized Difference Snow Index (ΔNDSI). The four were found to show very high skill scores (POD: 0.82 ± 0.04; FAR, 0.10 ± 0.04). The radiative transfer simulation supported optical characteristics of LSF in observations. The LSF probability indices (average POD: 0.83, FAR: 0.10) were constructed by a statistical combination of the four to derive the five-class probability values of LSF occurrence in a grid. The indices provided more details and useful results in LSF spatial distribution, compared to the single satellite observations (i.e., R0.65 and/or BTD3.7-11) of either LSF or no LSF. The present DSM could apply for remote sensing of environmental phenomena if the stereoscopic viewing angle between two satellites is appropriate.