Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924–1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km² (52.5 %), 56,661.1 km² (36.4 %), 51,642.3 km² (33.2 %), 49,773 km² (32 %) and 48,669.4 km² (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 %?year^?1 during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km²) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.     

Assessment of regional climatic changes in the Eastern Himalayan region: a study using multi-satellite remote sensing data sets

In this study, an attempt has been made to capture the sensitivity of a mountainous region to elevation-dependent warming and the response of a glacier-laden surface to increasing greenhouse gases (GHGs) and aerosol concentration. Some of the changes Sikkim has undergone due to urban sprawl are as follows: an increase of ~0.7?±?0.46 °C temperature in the past 40 years at an altitude of 5.5 km; a 2.21 km²/year rate of loss of glacierised area in the past 33 years; an increase in absorbed longwave radiation (6?±?2.41 W/m²); an increase in heat fluxes (2?±?0.97 W/m²); a decrease in albedo during the last 30 years; an increase in the concentrations of carbon dioxide (4.42 %), methane (0.61 %), ozone (0.67 %) and black carbon column optical depth (7.19 %); a decrease in carbon monoxide (2.61 %) and an increase in aerosol optical depth (19.16 %) during the last decade; a decrease in precipitation, water yield, discharge and groundwater; and an increase in evapotranspiration during 1971–2005. Detection of three climate signals (1976, 1997 and 2005) in the entire analysis is the quantification of the fact that the climate of Sikkim is moving away from its inter-annual variability. An increase in temperature (0.23 °C/decade) at higher altitude (~5.5 km), suppression of precipitation, decreasing water availability and rapid loss of glacierised area are the evidences of the fact that air pollution is playing a significant role in bringing about regional climatic changes in Sikkim. In this study, change detection method has been used for the first time for the estimation of change in a glacierised area of the region.     

Mapping aboveground woody biomass using forest inventory,remote sensing and geostatistical techniques

Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m?×?31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10–40, 40–70 and >70 % of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m³ ha^?1. The total growing stock of the forest was found to be 2,024,652.88 m³. The AGWB ranged from 143 to 421 Mgha^?1. Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE)?=?42.25 Mgha^?1) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha^?1 respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha^?1. The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.     

Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches

Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution 1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid.     

Estimation of underwater visibility in coastal and inland waters using remote sensing data

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z _sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z _sd , namely, turbidity and length attenuation coefficient (1/(c + K _d ), c = beam attenuation coefficient; K _d  = diffuse attenuation coefficient at 531 nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R _rs (443) + R _rs (490))/(R _rs (555) + R _rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531 nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K _d ) at 531 nm, K _d (490) is estimated as a function of reflectance ratio (R _rs (670)/R _rs (490)), which provides the bio-optical link between chlorophyll concentration and K _d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z _sd and in situ Z _sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z _sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z _sd and in situ Z _sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z _sd and in situ Z _sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z _sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z _sd products. The modeled and satellite-derived Z _sd values were compared with measurement data which yielded RMSE = 0.079, MRE = ?0.016, and R ²  = 0.95 for the modeled Z _sd and RMSE = 0.075, MRE = 0.020, and R ²  = 0.95 for the satellite-derived Z _sd products.     

Decadal time-scale monitoring of forest fires in Similipal Biosphere Reserve,India using remote sensing and GIS

Analyzing the spatial extent and distribution of forest fires is essential for sustainable forest resource management. There is no comprehensive data existing on forest fires on a regular basis in Biosphere Reserves of India. The present work have been carried out to locate and estimate the spatial extent of forest burnt areas using Resourcesat-1 data and fire frequency covering decadal fire events (2004–2013) in Similipal Biosphere Reserve. The anomalous quantity of forest burnt area was recorded during 2009 as 1,014.7 km². There was inconsistency in the fire susceptibility across the different vegetation types. The spatial analysis of burnt area shows that an area of 34.2 % of dry deciduous forests, followed by tree savannah, shrub savannah, and grasslands affected by fires in 2013. The analysis based on decadal time scale satellite data reveals that an area of 2,175.9 km² (59.6 % of total vegetation cover) has been affected by varied rate of frequency of forest fires. Fire density pattern indicates low count of burnt area patches in 2013 estimated at 1,017 and high count at 1,916 in 2004. An estimate of fire risk area over a decade identifies 12.2 km² is experiencing an annual fire damage. Summing the fire frequency data across the grids (each 1 km²) indicates 1,211 (26 %) grids are having very high disturbance regimes due to repeated fires in all the 10 years, followed by 711 grids in 9 years and 418 in 8 years and 382 in 7 years. The spatial database offers excellent opportunities to understand the ecological impact of fires on biodiversity and is helpful in formulating conservation action plans.     

基于EOS/MODIS卫星遥感数据的渤海海冰变化趋势及特征应用研究

根据卫星遥感数据的分辨率、采集频次、获取方式等特点,将MODIS卫星遥感数据应用到渤海海冰遥感监测中。通过海冰在可见光和红外光波段的光谱存在特定差异的特性,对海冰发生面积进行提取。同时对渤海海冰进行了遥感监测,并完整地监测到海冰从发生到极盛再到消退的全过程。同时,在海冰发生最为严重的时段,对辽东湾海域进行了密集监测。研究结果表明,MODIS卫星遥感数据在海冰监测中有着及时、准确的优势。     

Assessment of chlorophyll-a variations in high- and low-flow seasons in Apalachicola Bay by MODIS 250-m remote sensing

Chlorophyll-a (chl-a) is considered as a primary indicator for water quality and foods for oyster growth in Apalachicola estuarine ecosystem. Assessment of chl-a concentration variation in response to river inflow is important for estuarine environmental research and management. In this study, remote sensing analysis has been conducted to evaluate the effects of river inflow on chlorophyll concentrations in Apalachicola Bay of Florida in the northeast Gulf of Mexico. A remote sensing model for chl-a was improved and applied to map spatial distributions of chl-a by using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution imageries in high-flow and low-flow seasons in 2001 and 2008. Chl-a values approximately ranged from the minimum 6 μg/l to the maximum 29 μg/l in the study period. Maximum chl-a concentration in high-flow season was almost twice above that in low-flow season. The averaged mean and minimum chl-a level in the high-flow season were approximately 42 and 28 % higher than those in low-flow season, respectively. The remote sensing mapping of chl-a was able to show spatial variations of chl-a in the entire bay under different flow conditions, which indicated its advantage over the traditional field data sampling for monitoring water quality over a large area of estuary. The MODIS 250-m remote sensing regression model presented from this study can be used to support monitoring and assessment of the spatial chl-a distribution in the bay for environmental research and management in Apalachicola Bay.     

Assessment of large-scale deforestation of Nawarangpur district, Orissa, India: a remote sensing based study

The Nawarangpur district, Orissa, a tropical region with Sal mixed moist deciduous and Sal mixed dry deciduous forests, has been affected by extensive deforestation. The district was surveyed using Landsat MSS (1973), Landsat TM (1990) and IRS P6 LISS III (2004) satellite imagery. From 1973 to 1990, more than 888.6 km(2) of dense forest (rate of deforestation = 3.62) and from 1990 to 2004, 429.7 km(2) (rate of deforestation = 3.97) were found to have been deforested. The analysis of results identified the reduction in area of dense forest and increase of agricultural land, degraded areas of abandoned agricultural land and unproductive scrub. There is an urgent need for rational management of the remaining forest for it to be able to survive beyond next decades. From this study it can be concluded that temporal changes and the factors affecting these changes should be determined for sustainable management of natural resources.     

遥感技术在大气环境监测中的应用综述

综合论述了近20多年来国内外对大气环境遥感监测的研究现状,介绍了应用于大气环境遥感监测的多种方法并着重阐述了被动式空基遥感和主动式地基遥感在大气环境遥感中的应用以及探测气溶胶的卫星传感器的发展历程和特点。最后,对我国大气环境遥感研究中存在问题和发展前景进行了讨论。     

Barriers to the use of remote sensing in providing environmental information

Remote sensing from aircraft and earth-observing satellites is an essential source of environmental information and, at regional and global scales, remote sensing from satellites is often the only way in which some information can be collected. Naturally there are technical limitations, such as low resolution and the inability of optical sensors to see through clouds that restrict the use of satellite data, but technology is moving rapidly and major advances can be expected during the current decade, especially from radar satellites.The main barriers to the use of environmental information provided by remote sensing are not technological, but include cost and a need for training and transfer of technology, and a requirement for users to depart from traditional methods where new technology offers distinct advantages. Perhaps the most important contributions that users of remote sensing data can make to breaking down the barriers to the use of environmental data is to provide very clear statements of their information requirements so that technology can develop to meet these requirements.     

Top-down and bottom-up inventory approach for above ground forest biomass and carbon monitoring in REDD framework using multi-resolution satellite data

This study deals with the future scope of REDD (Reduced Emissions from Deforestation and forest Degradation) and REDD+ regimes for measuring and monitoring the current state and dynamics of carbon stocks over time with integrated geospatial and field-based biomass inventory approach. Multi-temporal and multi-resolution geospatial synergic approach incorporating satellite sensors from moderate to high resolution with stratified random sampling design is used. The inventory process involves a continuous forest inventory to facilitate the quantification of possible CO₂ reductions over time using statistical up-scaling procedures on various levels. The combined approach was applied on a regional scale taking Himachal Pradesh (India), as a case study, with a hierarchy of forest strata representing the forest structure found in India. Biophysical modeling implemented revealed power regression model as the best fit (R ²?=?0.82) to model the relationship between Normalized Difference Vegetation Index and biomass which was further implemented to calculate multi-temporal above ground biomass and carbon sequestration. The calculated value of net carbon sequestered by the forests totaled to 11.52 million tons (Mt) over the period of 20 years at the rate of 0.58 Mt per year since 1990 while CO₂ equivalent reduced from the environment by the forests under study during 20 years comes to 42.26 Mt in the study area.     

Assessment of some remote sensing techniques used to detect land use/land cover changes in South-East Transilvania,Romania

This paper assesses the image differencing technique for the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the TM 4 band (TM 4), as well as the post-classification comparison (PCC) in order to analyze the land use/land cover changes in the South-East Transilvania, Romania. The analysis was performed using two frames from Landsat 5 TM satellite images acquired on August 5, 1993 and July 24, 2009. After applying the NDVI, PC2, and TM 4 image differencing techniques, the images obtained were transformed into change/no change maps. The thresholds identified to highlight the changes were set at 0.6 s for NDVI and 0.7 s for PC2 and TM 4. Before applying the PCC technique, the satellite images were classified through the supervised classification method. The overall accuracy obtained was 85.91 % and the kappa statistics 0.8249 for 1993, 88.18 % and 0.8497 for 2009, respectively. The assessment of the changes detection methods in the studied area shows that the first place is occupied by NDVI image differencing with an overall accuracy of 83.80 %, followed by PCC method with 83.20 %, PC2 difference with an overall accuracy of 81.60 %, and TM 4 difference with an overall accuracy of 79.40 %.     

Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples

The present study focuses on developing models to predict lichen species richness in a UNESCO Biosphere Reserve of the Swiss Pre-Alps following a gradient of land-use intensity combining remote sensing data and regression models. The predictive power of the models and the obtained r ranging from 0.5 for lichens on soil to 0.8 for lichens on trees can be regarded as satisfactory to good, respectively. The study revealed that a combination of airborne and spaceborne remote sensing data produced a variety of ecological meaningful variables.     

Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey)

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities.     

Review of monitoring and assessing ground vegetation biodiversity in national forest inventories

Ground vegetation (GV) is an important component from which many forest biodiversity indicators can be estimated. To formulate policies at European level, taking into account biodiversity, European National Forest Inventories (NFIs) are one of the most important sources of forest information. However, for monitoring GV, there are several definitions, data collection methods, and different possible indicators. Even though it must be considered that natural conditions in different countries form very different understory types, each one has its own cost-efficient monitoring design, and they can hardly be compared. Therefore, the development of general guidelines is a particularly complex issue. This paper is a review of data collection methods and consequently a selection of the best available methods for the set of indicators with an emphasis on GV sampling methodologies in NFIs. As a final result, recommendations on GV definitions and classifications, sampling methodologies, and indicators are formulated for NFIs. Different sampling areas are recommended for each life form (shrubs, herbs, etc.). Inventory cycles and sampling seasons (depending on the phonological stages) should be specially considered and evaluated in the results. The proposed indicators are based on composition at different levels of sampling intensity for each life form and on coverage measurements.     

Assessment of groundwater vulnerability using remote sensing,susceptibility index,and WetSpass model in an arid region (Biskra,SE Algeria)

Environmental Monitoring and Assessment - Downstream water pollution resulting from anthropogenic pressures on upstream water can cause conflicts, especially in transboundary rivers basins. This...     

遥感在生态与环境监测中的主要应用领域

遥感是一种以物理手段、数学方法和地学分析为基础的综合应用技术 ,具有宏观、综合、动态和快速的特点。在解决宏观尺度的环境问题时 ,卫星遥感可重复获取多种空间、不同时相和不同波谱分辨率的地球信息 ,是适宜于调查和研究这些主题的唯一的、最有效的工具。本文通过对遥感在环境与生态监测的主要应用领域进行概要阐述 ,旨在抛砖引玉 ,推动遥感在环境与生态监测中的广泛应用。     

Water quality monitoring of Al-Habbaniyah Lake using remote sensing and in situ measurements

Environmental Monitoring and Assessment - A good quality drinking water is essential for human survival. However, a large percentage of the populations in most developing countries (Nigeria...