C. S. Jha

Coherence-based land cover classification in forested areas of Chattisgarh, Central India, using environmental satellite—advanced synthetic aperture radar data

In the present work, the potential of synthetic aperture radar (SAR) interferometric coherence in land cover classification is studied over forested areas of Bilaspur, Chattisgarh, India using Environmental Satellite—Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data. Single look complex (SLC) interferometric pair ASAR data of 24th September 2006 (SLC-1) and 29th October 2006 (SLC-2) covering the study area were acquired and processed to generate backscatter and interferometric coherence images. A false colored composite of coherence, backscatter difference, and mean backscatter was generated and subjected to maximum likelihood classification to delineate major land cover classes of the study area viz., water, barren, agriculture, moist deciduous forest, and sal mixed forests. Accuracy assessment of the classified map is carried out using kappa statistics. Results of the study suggested potential use of ENVISAT-ASAR C-band data in land cover classification of the study area with an overall classification accuracy of 82.5%, average producers accuracy of 83.69%, and average users accuracy of 81%. The present study gives a unique scope of SAR data application in land cover classification over the tropical deciduous forest systems of India, which is still waiting for its indigenous SAR system.

Eddy covariance based methane flux in Sundarbans mangroves, India

We report the initial results of the methane flux measured using eddy covariance method during summer months from the world’s largest mangrove ecosystem, Sundarbans of India. Mangrove ecosystems are known sources for methane (CH4) having very high global warming potential. In order to quantify the methane flux in mangroves, an eddy covariance flux tower was recently erected in the largest unpolluted and undisturbed mangrove ecosystem in Sundarbans (India). The tower is equipped with eddy covariance flux tower instruments to continuously measure methane fluxes besides the mass and energy fluxes. This paper presents the preliminary results of methane flux variations during summer months (i.e., April and May 2012) in Sundarbans mangrove ecosystem. The mean concentrations of CH4 emission over the study period was 1682 ± 956 ppb. The measured CH4 fluxes computed from eddy covariance technique showed that the study area acts as a net source for CH4 with daily mean flux of 150.22 ± 248.87 mg m−2 day−1. The methane emission as well as its flux showed very high variability diurnally. Though the environmental conditions controlling methane emission is not yet fully understood, an attempt has been made in the present study to analyse the relationships of methane efflux with tidal activity. This present study is part of Indian Space Research Organisation–Geosphere Biosphere Program (ISRO–GBP) initiative under ‘National Carbon Project’.

Predictive modelling of the spatial pattern of past and future forest cover changes in India

This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km2, which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880–2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multi-layer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km2 of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.

Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha−1. The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture–AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture–AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km2 in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.

Monitoring of fire incidences in vegetation types and Protected Areas of India: Implications on carbon emissions

Carbon emissions released from forest fires have been identified as an environmental issue in the context of global warming. This study provides data on spatial and temporal patterns of fire incidences, burnt area and carbon emissions covering natural vegetation types (forest, scrub and grassland) and Protected Areas of India. The total area affected by fire in the forest, scrub and grasslands have been estimated as 48765.45, 6540.97 and 1821.33 km 2, respectively, in 2014 using Resourcesat-2 AWiFS data. The total CO 2 emissions from fires of these vegetation types in India were estimated to be 98.11 Tg during 2014. The highest emissions were caused by dry deciduous forests, followed by moist deciduous forests. The fire season typically occurs in February, March, April and May in different parts of India. Monthly CO 2 emissions from fires for different vegetation types have been calculated for February, March, April and May and estimated as 2.26, 33.53, 32.15 and 30.17 Tg, respectively. Protected Areas represent 11.46% of the total natural vegetation cover of India. Analysis of fire occurrences over a 10-year period with two types of sensor data, i.e., AWiFS and MODIS, have found fires in 281 (out of 614) Protected Areas of India. About 16.78 Tg of CO 2 emissions were estimated in Protected Areas in 2014. The natural vegetation types of Protected Areas have contributed for burnt area of 17.3% and CO 2 emissions of 17.1% as compared to total natural vegetation burnt area and emissions in India in 2014. 9.4% of the total vegetation in the Protected Areas was burnt in 2014. Our results suggest that Protected Areas have to be considered for strict fire management as an effective strategy for mitigating climate change and biodiversity conservation.

Assessment and monitoring of long-term forest cover changes (1920–2013) in Western Ghats biodiversity hotspot

Western Ghats are considered as one of the global biodiversity hotspots. There is an information gap on conservation status of the biodiversity hotspots. This study has quantified estimates of deforestation in the Western Ghats over a period of past nine decades. The classified forest cover maps for 1920, 1975, 1985, 1995, 2005 and 2013 indicates 95,446 (73.1%), 63,123 (48.4%), 62,286 (47.7%), 61,551 (47.2%), 61,511 (47.1%) and 61,511 km2 (47.1%) of the forest area, respectively. The rates of deforestation have been analyzed in different time phases, i.e., 1920–1975, 1975–1985, 1985–1995, 1995–2005 and 2005–2013. The grid cells of 1 km2 have been generated for time series analysis and describing spatial changes in forests. The net rate of deforestation was found to be 0.75 during 1920–1975, 0.13 during 1975–1985, 0.12 during 1985–1995 and 0.01 during 1995–2005. Overall forest loss in Western Ghats was estimated as 33,579 km2 (35.3% of the total forest) from 1920’s to 2013. Land use change analysis indicates highest transformation of forest to plantations, followed by agriculture and degradation to scrub. The dominant forest type is tropical semi-evergreen which comprises 21,678 km2 (35.2%) of the total forest area of Western Ghats, followed by wet evergreen forest (30.6%), moist deciduous forest (24.8%) and dry deciduous forest (8.1%) in 2013. Even though it has the highest population density among the hotspots, there is no quantifiable net rate of deforestation from 2005 to 2013 which indicates increased measures of conservation.

Landscape level analysis of disturbance regimes in protected areas of Rajasthan, India

There is an urgent need to identify the human influence on landscape as disturbance regimes was realized for prioritization of the protected areas. The present study has attempted to describe the landscape level assessment of fragmentation and disturbance index in protected areas of Rajasthan using remote sensing and GIS techniques. Geospatial analysis of disturbance regimes indicates 61.75% of the total PAs are under moderate disturbance index followed by 28.64% and 9.61% under low and high respectively. Among the 28 protected areas- National Chambal WLS, Jaisamand WLS, Kumbhalgarh WLS, Sawai Man Singh WLS, Kailadevi WLS and Bandh Baratha WLS are representing high level of disturbance. The present study has emphasized the moderate to low disturbance regimes in protected areas, which infer low biotic pressure and conservation effectiveness of PA network in Rajasthan. The spatial information generated on PAs is of valuable use for forest management and developing conservation strategies.

Development of National Database on Long-term Deforestation in Sri Lanka

Sri Lanka is one of the biodiversity hotspots of the world. This study has utilized satellite remote sensing and GIS techniques to generate a nation-wide database on forests, forest types and land use/land cover of Sri Lanka. Spatial assessment of forest cover changes was carried out for the periods 1976–1985, 1985–1994, 1994–2005 and 2005–2014. The landscape fragmentation analysis has carried out to calculate the spatial and temporal patterns of forest. Land use/land cover map was prepared representing seven classes in 2014. The plantations occupy a large area (34.2%) followed by forests (33.4%) and agriculture (26.1%) in 2014. During the period of 1976–2014, the forest has been decreased by 5.5%. From 1976 to 1985 forest recorded a loss at an annual rate of 0.49%. This annual rate decreased to 0.01% during 2005–2014 indicates declining trend of deforestation and effective conservation measures. The study found deforestation hotspots in south east and northern most parts of the Sri Lanka. Total number of patches estimated has increased from 15193 in 1976 to 16136 in 2014. The study has found that main causes of deforestation in Sri Lanka were due to expansion of agriculture and plantations. The extent of change detected in the study through geospatial techniques has significance to the forest ecology and management of natural landscapes in Sri Lanka.

Satellite image based quantification of invasion and patch dynamics of mesquite (Prosopis juliflora) in Great Rann of Kachchh, Kachchh Biosphere Reserve, Gujarat, India

The invasion of alien species is a significant threat to global biodiversity and the top driver of climate change. The present study was conducted in the Great Rann of Kachchh, part of Kachchh Biosphere Reserve, Gujarat, India, which has been severely affected by invasion of Prosopis juliflora. The invasive weed infestation has been identified using multi-temporal remote sensing datasets of 1977, 1990, 1999, 2005 and 2011. Spatial analyses of the transition matrix, extent of invasive colonies, patchiness, coalescence and rate of spread were carried out. During the study period of three and half decades, almost 295 km2 of the natural land cover was converted into Prosopis cover. This study has shown an increment of 42.9% of area under Prosopis cover in the Great Rann of Kachchh, part of the Kachchh Biosphere Reserve during 1977 to 2011. Spatial analysis indicates high occupancy of Prosopis cover with most of the invasion (95.9%) occurring in the grasslands and only 4.1% in other land cover types. The process of Prosopis invasion shows high patch initiation, followed by coalescence, indicating aggressive colonization of species. The number of patches within an area of < 1 km2 increased from 1977 to 2011, indicating the formation of new Prosopis habitats by replacing the grasslands. The largest patch of Prosopis cover increased from 144 km2 in 1977 to 430 km2 in 2011. The estimated mean patch size was 7.8 km2 in 1977. The mean patch size was largest during 2011, i.e., 9 km2. The annual spread rate for Prosopis has been estimated as 2.1% during 2005–2011. The present work has investigated the long term changes in Prosopis cover in the Great Rann of Kachchh, part of Kachchh Biosphere Reserve. The spatial database generated will be useful in preparing strategies for the management of Prosopis juliflora.

Reconstruction of time series MODIS EVI data using de-noising algorithms

Abstract Long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data have inherent noise due to clouds and poor atmospheric conditions that limit its applicability for environmental applications. This study was carried out with an objective of noise removal and reconstruction of time series MODIS EVI data (16 day) for the period 2010–2014 using de-noising algorithms. Relative evaluation of de-noising algorithms for smoothing temporal data with ideal noise free data is not possible in actual scenario. Hence, synthetic signals were generated and introduced Gaussian noise at different variance levels for evaluation purpose. Spatial analysis was carried out by introducing noise at different variance levels into the noise free EVI images from the raw EVI stacked image. Spatio-temporal analyses of noise signals in the reconstructed EVI images were evaluated in terms of performance indicators, namely Peak Signal-to-Noise Ratio and Mean Square Error.