C. Jeganathan

Amazon vegetation greenness as measured by satellite sensors over the last decade

[1] During the last decade two major drought events, one in 2005 and another in 2010, occurred in the Amazon basin. Several studies have claimed the ability to detect the effect of these droughts on Amazon vegetation response, measured through satellite sensor vegetation indices (VIs). Such monitoring capability is important as it potentially links climate changes (increasing frequency and severity of drought), vegetation response as observed through vegetation greenness, and land-atmosphere carbon fluxes which directly feedback into global climate change. However, we show conclusively that it is not possible to detect the response of vegetation to drought from space using VIs. We analysed 11 years of dry season (July–September) Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and normalised difference vegetation index (NDVI) images. The VI standardised anomaly was analysed alongside the absolute value of EVI and NDVI, and the VI values for drought years were compared with those for non-drought years. Through a series of analyses, the standardised anomalies and VI values for drought years were shown to be of similar magnitude to those for non-drought years. Thus, while Amazon vegetation may respond to drought, this is not detectable through satellite-observed changes in vegetation greenness. A significant long-term decadal decline in VI values is reported, which is independent of the occurrence of drought. This trend may be caused by environmental or noise-related factors which require further investigation.

A review of radar remote sensing for biomass estimation

Forest plays a vital role in regulating climate through carbon sequestration in its biomass. Biomass reflects the health and environmental conditions of a forest ecosystem. In context to the climate change mitigation mechanisms like REDD (reducing emissions from deforestation and forest degradation), an extensive forest monitoring campaign is especially important. Remote sensing of forest structure and biomass with synthetic aperture radar (SAR) bears significant potential for mapping and understanding forest ecological processes. Limitations of the conventional forest inventory procedures, like the extensive cost, labor and time, can be overcome through integrated geospatial techniques. Optical sensor or SAR data are suitable for extracting information about simple and homogeneous forest stand sites. However, optical sensors face serious limitations, specifically in tropical regions, like the cloud cover that SAR can overcome along with targeting saturation and penetration aspects. Simultaneous use of spectral information and image texture parameters improves the biomass assessment over undulating terrain and in radical conditions. Also, synergic use of multi-sensor optical and SAR has better potential than single sensor. Interferometric (InSAR) and polarimetric (PolSAR) SAR or a combination of the both (PolInSAR) serves as effective alternatives. These techniques could serve as valuable methods for biomass assessment of heterogeneous complex biophysical environments. However, SAR data have its own limitations and complexities. Identifying, understanding and solving major uncertainties in different stages of the biomass estimation procedure are critical. In this regard, the current study provides a review of radar remote sensing-based studies in forest biomass estimation.

Mapping the phenology of natural vegetation in India using a remote sensing-derived chlorophyll index

Time series of MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) level-3 data product, with a spatial resolution of ∼4.6 km composited at 8-day intervals for the years 2003 to 2007, were used to map the phenology of natural vegetation in India. Initial dropouts and noise in the MTCI data were corrected using a temporal moving window filter, Fourier-based smoothing using the first four harmonics was applied and then the phenological variables were extracted through a temporal iterative search of peaks and valleys in the time series for each pixel. The approach was fine-tuned to extract reliable phenological variables from the complex and multiple phenology cycles. A global land cover map (GLC2000) was used as a reference to extract the spatial locations of the vegetation types to infer their phenology. The median of each phenological variable was derived and a spatial majority filter was applied to the 1° × 1° grids (representing 1:250 000 Survey of India toposheet) covering the whole of India. This study presents the results derived for the evergreen, semi-evergreen, moist deciduous and dry deciduous vegetation types of India. A general trend of earlier onset of greenness at lower latitudes than at higher latitudes was observed for the natural vegetation in India.

Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R2 = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R2 = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R2 = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.

Terrestrial vegetation phenology from MODIS and MERIS

Phenological information can be provided globally using remote sensing based time-series vegetation indices. Basic differences in the data and methods used can yield different results. This study analysed such differences in the phenological information, mainly onset of greenness (OG), estimated using the Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Terrestrial Chlorophyll Index (MTCI) from Medium Resolution Imaging Spectrometer (MERIS) data. The two datasets were processed independently using different techniques to provide weekly estimates. Differences in the OG results were analysed for two years (2003 & 2006) and at four levels: a) full study area, b) within land cover classes, c) within core zones of each class and d) at the edge zones of each class. It was found that the trend of OG estimated from MODIS and MERIS were spatially similar, although not the same. From 15 Biome classes found in the study area the classes with the greatest differences were evergreen needle leaf, mixed forest and cropland. The differences were mainly due to the characteristic nature of the indices and also, to some extent, due to false internal flags in the algorithms.

Mapping Agriculture Dynamics and Associated Flood Impacts in Bihar using Time-series Satellite Data

Agriculture is the prime requirement for sustaining human life on earth, and agriculture sustainability depends on soil health and suitable climatic variations. Human have adopted many local-weather-dependent crop types and its cultivation patterns based on knowledge about long term climatic and environmental conditions. Any anomaly in these factors would result in unforeseen reduction in the food production and associated socio-economic chaos at local/regional to global scale. Due to anthropogenic activities like expansion of urban area, industrialization, deforestation etc. have increased the greenhouse gases (GHGs) level and hence the mean earth surface temperature has increased by 0.74 °C during 1900 to 2000 AD and it is anticipated to rise by 1.4–5.8 °C during 2000 to 2100 AD with notable local differences which would result in increase in the frequency of drought, flood, sea level rise etc. and will drastically affect the crop production. Bihar is one of the fertile regions in India, gifted with numerous water resources like Ganga, Gandak and Kosi and many more rivers. But these rivers are both boon and bane to Bihar because most of the rivers flood during monsoon season. Hence it would be interesting to know the Agriculture cropping pattern over a decade, its changing scenario and the impact of flood on agriculture area in Bihar. In this regard, current study attempted to use time-series remote sensing data from 2001 to 2012 in deriving spatio-temporal, seasonal and annual cropping pattern, and as well as flood scenario purely based on space based observation.

Mountain agriculture extraction from time-series MODIS NDVI using dynamic time warping technique

ABSTRACT The study attempts to extract Mountain Agriculture using an optimized Dynamic Time Warping (DTW) algorithm having endpoint constraints. The DTW was applied over a time-series annual stack of Normalized Differential Vegetation Index (NDVI) using a set of reference time series profiles for three agriculture classes (i.e. double cropping, single cropping, and horticulture) and the pixel-wise similarity is examined to identify the agriculture classes. In addition, Euclidean Distance (ED) was used to compare DTW-based result. The detection accuracy of each class was assessed using Google Earth-based agriculture sample, and the spatial agreement of resultant map was assessed with high-resolution reference data using Pareto boundary technique. The sample based accuracy evaluation reveals that DTW algorithm performed better for double and single cropping agriculture detection in compared to the horticulture. Overall, DTW-based agriculture map (0.81 ± 0.01) yielded higher overall accuracy in comparison with ED-based agriculture map (0.75 ± 0.01). The Pareto boundary-based spatial agreement analysis using high-resolution reference data also shows the dominant performance of DTW based agriculture map than an ED-based map. DTW performed better than ED, in terms of optimal distance (OD), in ten out of eleven districts. However, reliable spatial matching (OD less than 0.23) between DTW-based map and reference agriculture map was observed in lower elevation region, especially in Hamirpur (OD = 0.06), Bilaspur (OD = 0.09), Shimla (OD = 0.19) and Una (OD = 0.20) district.