Historical effects of dissolved organic carbon export and land management decisions on the watershed-scale forest carbon budget of a coastal British Columbia Douglas-fir-dominated landscape

Background

To address how natural disturbance, forest harvest, and deforestation from reservoir creation affect landscape-level carbon (C) budgets, a retrospective C budget for the 8500 ha Sooke Lake Watershed (SLW) from 1911 to 2012 was developed using historical spatial inventory and disturbance data. To simulate forest C dynamics, data was input into a spatially-explicit version of the Carbon Budget Model-Canadian Forest Sector (CBM-CFS3). Transfers of terrestrial C to inland aquatic environments need to be considered to better capture the watershed scale C balance. Using dissolved organic C (DOC) and stream flow measurements from three SLW catchments, DOC load into the reservoir was derived for a 17-year period. C stocks and stock changes between a baseline and two alternative management scenarios were compared to understand the relative impact of successive reservoir expansions and sustained harvest activity over the 100-year period.

Results

Dissolved organic C flux for the three catchments ranged from 0.017 to 0.057 Mg C ha^?1 year^?1. Constraining CBM-CFS3 to observed DOC loads required parameterization of humified soil C losses of 2.5, 5.5, and 6.5%. Scaled to the watershed and assuming none of the exported terrestrial DOC was respired to CO₂, we hypothesize that over 100 years up to 30,657 Mg C may have been available for sequestration in sediment. By 2012, deforestation due to reservoir creation/expansion resulted in the watershed forest lands sequestering 14 Mg C ha^?1 less than without reservoir expansion. Sustained harvest activity had a substantially greater impact, reducing forest C stores by 93 Mg C ha^?1 by 2012. However approximately half of the C exported as merchantable wood during logging (~176,000 Mg C) may remain in harvested wood products, reducing the cumulative impact of forestry activity from 93 to 71 Mg C ha^?1.

Conclusions

Dissolved organic C flux from temperate forest ecosystems is a small but persistent C flux which may have long term implications for C storage in inland aquatic systems. This is a first step integrating fluvial transport of C into a forest carbon model by parameterizing DOC flux from soil C pools. While deforestation related to successive reservoir expansions did impact the watershed-scale C budget, over multi-decadal time periods, sustained harvest activity was more influential.

     

Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R²), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha^?1 (average = 55.8 Mg ha^?1); below-ground biomass ranged between 4.06 and 436.47 Mg ha^?1 (average = 81.47 Mg ha^?1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha^?1 (average = 64.52 Mg C ha^?1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas.     

From berries to blocks: carbon stock quantification of a California vineyard

Background

Quantifying terrestrial carbon (C) stocks in vineyards represents an important opportunity for estimating C sequestration in perennial cropping systems. Considering 7.2 M ha are dedicated to winegrape production globally, the potential for annual C capture and storage in this crop is of interest to mitigate greenhouse gas emissions. In this study, we used destructive sampling to measure C stocks in the woody biomass of 15-year-old Cabernet Sauvignon vines from a vineyard in California’s northern San Joaquin Valley. We characterize C stocks in terms of allometric variation between biomass fractions of roots, aboveground wood, canes, leaves and fruits, and then test correlations between easy-to-measure variables such as trunk diameter, pruning weights and harvest weight to vine biomass fractions. Carbon stocks at the vineyard block scale were validated from biomass mounds generated during vineyard removal.

Results

Total vine C was estimated at 12.3 Mg C ha^?1, of which 8.9 Mg C ha^?1 came from perennial vine biomass. Annual biomass was estimated at 1.7 Mg C ha^?1 from leaves and canes and 1.7 Mg C ha^?1 from fruit. Strong, positive correlations were found between the diameter of the trunk and overall woody C stocks (R² = 0.85), pruning weights and leaf and fruit C stocks (R² = 0.93), and between fruit weight and annual C stocks (R² = 0.96).

Conclusions

Vineyard C partitioning obtained in this study provides detailed C storage estimations in order to understand the spatial and temporal distribution of winegrape C. Allometric equations based on simple and practical biomass and biometric measurements could enable winegrape growers to more easily estimate existing and future C stocks by scaling up from berries and vines to vineyard blocks.

     

Geoinformatics as a tool for the assessment of the impact of ground water quality for irrigation on soil health

Secondary salinisation is the most harmful and extended phenomenon of the unfavourable effects of irrigation on the soil and environment. An attempt was made to study the impact of poor quality ground water on soils in terms of secondary salinisation and availability of soil nutrients in Faridkot district of Punjab of northern India. Based on physiographic analysis of IRS 1C LISS-III data and semi-detailed soil survey, the soil map was finalized on a 1:50,000 scale and digitized using Arc Info GIS. Georeferenced surface soil samples (0–0.15 m) from 231 sites were collected and analyzed for available phosphorus (P) and potassium (K). Interpolation by kriging produced digital spatial maps of available P and K. Ground water quality map was generated in GIS domain on the basis of EC (electrical conductivity) and RSC (residual sodium carbonate) of ground water samples collected from 374 georeferenced tube wells. Integration of soil and ground water quality maps enabled generating a map showing degree (high, moderate and low) and type (salinity, sodicity and both) of vulnerability to secondary salinization. Fine-textured soils have been found to be highly sensitive to secondary salinisation, whereas medium-textured soils as moderately sensitive to secondary salinisation. The resultant map was integrated with available P and K maps to show the combined influence of soil texture and ground water quality on available soil nutrients. The results show that available P and K in the soils of different physiographic units were found in the order of Ap1 < Ap2 < Ap3. The soils of all physiographic units had sizeable area having high content of P (>22.5 kg / ha) and medium available K (135–335 kg ha⁻¹) in most of the test sites when irrigated with saline, sodic or poor quality water.     

Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airborne laser scanner data and ultraCam-D images

Inventories of mixed broad-leaved forests of Iran mainly rely on terrestrial measurements. Due to rapid changes and disturbances and great complexity of the silvicultural systems of these multilayer forests, frequent repetition of conventional ground-based plot surveys is often cost prohibitive. Airborne laser scanning (ALS) and multispectral data offer an alternative or supplement to conventional inventories in the Hyrcanian forests of Iran. In this study, the capability of a combination of ALS and UltraCam-D data to model stand volume, tree density, and basal area using random forest (RF) algorithm was evaluated. Systematic sampling was applied to collect field plot data on a 150 m × 200 m sampling grid within a 1100 ha study area located at 36°38′- 36°42′N and 54°24′–54°25′E. A total of 308 circular plots (0.1 ha) were measured for calculation of stand volume, tree density, and basal area per hectare. For each plot, a set of variables was extracted from both ALS and multispectral data. The RF algorithm was used for modeling of the biophysical properties using ALS and UltraCam-D data separately and combined. The results showed that combining the ALS data and UltraCam-D images provided a slight increase in prediction accuracy compared to separate modeling. The RMSE as percentage of the mean, the mean difference between observed and predicted values, and standard deviation of the differences using a combination of ALS data and UltraCam-D images in an independent validation at 0.1-ha plot level were 31.7%, 1.1%, and 84 m³ ha⁻¹ for stand volume; 27.2%, 0.86%, and 6.5 m² ha⁻¹ for basal area, and 35.8%, −4.6%, and 77.9 n ha⁻¹ for tree density, respectively. Based on the results, we conclude that fusion of ALS and UltraCam-D data may be useful for modeling of stand volume, basal area, and tree density and thus gain insights into structural characteristics in the complex Hyrcanian forests.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Background

Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.

Results

Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m^?2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R² ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha^?1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha^?1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R² ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha^?1 [between 0.69–0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha^?1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R² was between 0.58–0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha^?1 for the echo-based model, whereas for the CHM R² was between 0.37–0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha^?1.

Conclusions

Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m^?2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m^?2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m^?2.

     

Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage

Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha^?1 (15 %) for grain yield and 1609 kg ha^?1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat.     

FAO 56 Model and Remote Sensing for the Estimation of Crop-Water Requirement in Main Branch Canal of the Bhadra Command area, Karnataka State

Remote sensing and FAO 56 crop water model are used for estimating crop water requirement for paddy crop located in the main branch canal of Bhadra Command Area in Karnataka, India. The estimation of crop-water requirement depends on the meteorological factors, soil type and crop coefficients. The result obtained showed that water requirements of rabi crops higher than those of the kariff crops. The total irrigated area estimated from the IRS image is 29,353 ha. It is found that the total paddy crop acreage is 18,257 ha covering 62 % in the total irrigated area of the command area, Arecanut 20 %, coconut 15 % and sugarcane with other crops 3 %. The water requirement for paddy is 1180.4 mm for its entire growth period. The total water requirement for irrigation supply for crops in the entire command area is 5,790 at a demand of 0.10501 cusecs per ha.     

The solani-ganga interfluve; an application of remote sensing technique on soil resource mapping for agricultural planning

The area of the Solani-Ganga interfluve, which lies between 29°16′N to 30°15′N latitude and 77°45′E to 78°15′E longitude was undertaken for the present study using LANDSAT imagery of band 5 and 7 and the false colour composite on the scale of 1:250,000 in combination with aerial photographs (1:25,000). Major geomorphic units, e.g., Siwalik Hills. Solani-upper alluvial plain, Solani lower alluvial plain, ‘Tarai’ and Ganga alluvial plain were delineated on LANDSAT and colour composite. Sample areas selected from LANDSAT were studied on aerial photographs in details and soil physiography relationship was developed. The soils on Siwalik hills are classified as Orthents. The soils of the pledmont plain and the recent terraces of Solani river and its tributaries were Psamments, Orthents, Fluvents, Orchrepts and Aquepts. The soils of upper alluvial tract of the Ganga plain is mostly Ustalfs with inclusion of Aqualfs, while the strong hydromorphic Tarai tract consists of partly Aquepts, Ochrepts (cultivated) and partly of Aquolls, Ustolls and Ustalfs (under forest). The present study aims to pin point the nature of soil relief relationship with the help of LANDSAT imagery and aerial photographs and diagnose the intensity of the depletion of soil resources (by prevailing factors like swift run off of biykderfed torrents, fast-flow of ground water, soil creep, mass wasting) through field studies and then treat them with ecological dose of soil conservation. For agronomic development of the region, it is worked out that the present crop-combination and crop-rotation systems should be slightly modified according to its ecosystem to prevent the depletion of soil nutrients.     

Characterization of Landforms and Soils in Complex Geological Formations—A Remote Sensing and GIS Approach

The present investigation has been designed to analyze the landform and soil relationship in a geologically complex terrain of Tirora tahsil of Gondia district, Maharashtra using remotely sensed data and GIS technique. The geomorphologic units of the study area were delineated through visual interpretation of IRS–ID LISS-III data based on the spatial variation of the image characteristics. Thirteen landform units have been identified in the tahsil. The slope varied from level to nearly level with an area of about 63.76% of the tahsil. Rest of the area ranged from very gentle to moderately steep slopes. During soil survey, soil profiles were studied for morphological features. Horizon-wise soil samples were collected from the representative soil profiles on each landform unit. The depth of soil varied from 25 to 160 cm and colour from dark brown to very dark grayish brown. The texture ranged from clay loam to clayey in accordance with higher and lower topographic positions respectively. Higher available water holding capacity (AWC 285 mm) is found in low-lying area and low to medium AWC (140 mm) is noticed in the soils developed at higher elevation. The soils reaction (pH) is strongly acidic in nature (pH 5.2) on dissected hills, linear ridge and moderately weathered pediments, whereas, the soils are moderately to slightly acidic in nature (pH 5.5 to 6.5) on hills, shallow weathered pediments, moderately weathered pediments, deeply weathered pediments, narrow valleys, and broad valley floors. Slightly alkaline condition (pH 7.6) was observed on foot slopes and aggraded valley fills. The electrical conductivity of the soils is found almost same in all landforms. The cation exchange capacity of the area varies from 10.5 to 51.5 cmol(p⁺)kg^?1. The base saturation increases with decreasing elevation and slope. The four major soil orders viz, Entisols, Alfisols, Inceptisols and Vertisols are found in the study areas which are further classified into suborder and great group levels. The landform and soil relationship was analyzed to appraise the land resources in the tahsil. The study shows that the application of remotely sensed data and GIS are immensely helpful in land resources appraisal for their management on sustainable basis.     

Carbon budgets of potential tropical perennial grass cropping scenarios for bioenergy feedstock production

Background

The environmental costs of fossil fuel consumption are globally recognized, opening many pathways for the development of regional portfolio solutions for sustainable replacement fuel and energy options. The purpose of this study was to create a baseline carbon (C) budget of a conventionally managed sugarcane (Saccharum officinarum) production system on Maui, Hawaii, and compare it to three different future energy cropping scenarios: (1) conventional sugarcane with a 50% deficit irrigation (sugarcane 50%), (2) ratoon harvested napiergrass (Pennisetum purpureum Schumach.) with 100% irrigation (napier 100%), and (3) ratoon harvested napiergrass with a 50% deficit irrigation (napier 50%).

Results

The differences among cropping scenarios for the fossil fuel-based emissions associated with agricultural inputs and field operations were small compared to the differences associated with pre-harvest burn emissions and soil C stock under ratoon harvest and zero-tillage management. Burn emissions were nearly 2000 kg C_eq ha^?1 year^?1 in the conventional sugarcane; whereas soil C gains were approximately 4500 kg C_eq ha^?1 year^?1 in the surface layer of the soil profile for napiergrass. Further, gains in deep soil profile C were nearly three times greater than in the surface layer. Therefore, net global warming potential was greatest for conventional sugarcane and least for napier 50% when deep profile soil C was included. Per unit of biomass yield, the most greenhouse gas (GHG) intensive scenario was sugarcane 50% with a GHG Index (GHGI, positive values imply a climate impact, so a more negative value is preferable for climate change mitigation) of 0.11 and the least intensive was napiergrass 50% when a deep soil profile was included (GHGI?=???0.77).

Conclusion

Future scenarios for energy or fuel production on former sugarcane land across the Pacific Basin or other volcanic islands should concentrate on ratoon-harvested crops that maintain yields under zero-tillage management for long intervals between kill harvest and reduce costs of field operations and agricultural input requirements. For napiergrass on Maui and elsewhere, deficit irrigation maximized climate change mitigation of the system and reduced water use should be part of planning a sustainable, diversified agricultural landscape.

     

Hierarchical Approach for Developing Baseline Soil Organic Carbon Database and Its Distribution in India

This study involves generation and logical integration of non-spatial and spatial data in a geographical information system framework to address the gap in national level soil organic carbon estimates. Remote sensing derived inputs and other spatial layers are corrected and integrated using same geographical standards. A relational data base of soil organic carbon density of Indian forest was prepared with attribute information. Hierarchical approach was followed to stratify and verify each sample from the data base using the corrected input layers in GIS and the resulting spatially distributed data is called Indian forest soil organic carbon database. The estimated mean soil organic carbon density for Indian forest is 70 t ha^?1 and varied from 35.4 t ha^?1 in Tropical thorn forest to 104.2 t ha^?1 in Himalayan moist temperate forest in the upper 30 cm of soil depth. Due to large variations in the surface layers the estimated standard error ranged from ±1.5 to 15 % for the upper 30 cm layer which is generally higher than the bottom soil layers. The level of detail in the data base helps to establish base line information for global, national and regional level studies.     

Investigating the vegetation and agricultural responses to El Nino/Southern Oscillation using AVHRR data

This study explores the possible linkages of El Nino/Southern Oscillation (ENSO) with vegetation and rainfall patterns, vegetation activity and food grain yields, in arid and semi-arid regions of western India. A sequence of 20-year (1981–2000) monthly maximum Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) and monthly rainfall from 160 stations were examined to study the seasonal patterns and their relation to ENSO activity. In addition, a direct (ENSO-crop yield) linkage and an intermediate (ENSO-NDVI) linkage of agricultural responses to ENSO were also investigated. The results indicate below-normal seasonal NDVI and rainfall associated with El Nino (warm) events, except during 1997, while positive anomalies occur during La Nina (cold) events. Sea surface temperature (SST) anomalies from NINO 3 region (5°N–5°S; 150°W–90°W), as an indicator of ENSO were significantly correlated with NDVI anomalies, rainfall anomalies and yield anomalies but the Southern Oscillation Index (SOI) was significantly related to NDVI anomalies only. NDVI anomaly patterns correspond to rainfall variability including that associated with ENSO activity. The observed strong intermediate linkage between yield anomalies and NDVI anomaly signal (r = 0.609) indicates that NDVI is an ideal index for understanding and analysing agricultural response to ENSO climate teleconnections.     

Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models

This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km², respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km², respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km². Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha^?1 year^?1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha^?1 year^?1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future.     

Prediction of soil loss differences and sediment accumulation at the Nilufer creek watershed,Turkey, using multiyear satellite data in a GIS

This study integrates the RUSLE, remote sensing and GIS to assess soil loss and identify sensitive areas to soil erosion in the Nilufer creek watershed in Bursa province, Turkey. The annual average soil loss was generated separately for years 1984 and 2011, in order to expose possible soil loss differences occurred in 27 years. In addition, sediment accumulation and sediment yield of the studied watershed was also predicted and discussed. The results indicated that very severe erosion risk areas in 1984 was 13.4% of the area, but it was increased to 15.3% by the year 2011, which needs immediate attention from soil conservation point of view. Furthermore, the estimated annual sediment yield of the Nilufer creek watershed was increased from 903 to 979 Mg km^?2 y^?1 in 27 years period. The study also provides useful information for decision-makers and planners to take appropriate land management practices in the area.     

An integrated methodology for soil moisture analysis using multispectral data in Mongolia

Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.     

Soil organic carbon mapping using remote sensing techniques and multivariate regression model

Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R² = 0.54; p < 0.0075). A significant statistical relationship (r = ?0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management.     

Soil Loss Mapping for Sustainable Development and Management of Land Resources in Warora Tehsil of Chandrapur District of Maharashtra: An Integrated Approach Using Remote Sensing and GIS

The study area is characterized by low and fluctuating rainfall pattern, thin soil cover, predominantly rain-fed farming with low productivity coupled with intensive mining activities, urbanization, deforestation, wastelands and unwise utilization of natural resources causing human induced environmental degradation and ecological imbalances, that warrant sustainable development and optimum management of land resources. Spatial information related to existing geology, land use/land cover, physiography, slope and soils has been derived through remote sensing, collateral data and field survey and used as inputs in a widely used erosion model (Universal Soil Loss Equation) in India to compute soil loss (t/ha/yr) in GIS. The study area has been delineated into very slight (<5 t/ha/yr), slight (5–10 t/ha/yr), moderate (10–15 t/ha/yr), moderately severe (15–20 t/ha/yr), severe (20–40 t/ha/yr) and very severe (>40 t/ha/yr) soil erosion classes. The study indicate that 45.4 thousand ha. (13.7% of TGA) is under moderate, moderately severe, severe and very severe soil erosion categories. The physiographic unit wise analysis of soil loss in different landscapes have indicated the sensitive areas, that has helped to prioritize development and management plans for soil and water conservation measures and suitable interventions like afforestation, agro-forestry, agri-horticulture, silvipasture systems which will result in the improvement of productivity of these lands, protect the environment from further degradation and for the ecological sustenance.