A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5 Mg C ha⁻¹ had the highest tendency for overestimation, while SVR model with an ME of −4.4 Mg C ha⁻¹ had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R² values (14.9 Mg C ha⁻¹ and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R² values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts.     

Long-term tillage effects on soil organic carbon and dissolved organic carbon in a purple paddy soil of Southwest China

The impact of conservation tillage practices on soil carbon has been of great interest in recent years. Conservation tillage might have the potential to enhance soil carbon accumulation and alter the depth distribution of soil carbon compared to conventional tillage based systems. Changes in the soil organic carbon (SOC) as influenced by tillage, are more noticeable under long-term rather than short-term tillage practices. The objective of this study was to determine the impacts of long-term tillage on SOC and dissolved organic carbon (DOC) status after 19 years of four tillage treatments in a Hydragric Anthrosol. In this experiment four tillage systems included conventional tillage with rotation of rice and winter fallow system (CTF), conventional tillage with rotation of rice and rape system (CTR), no-till and ridge culture with rotation of rice and rape system (NT) and tillage and ridge culture with rotation of rice and rape system (TR). Soils were sampled in the spring of 2009 and sectioned into 0–10, 10–20, 20–30, 30–40, 40–50 and 50–60 cm depth, respectively.Tillage effect on SOC was observed, and SOC concentrations were much larger under NT than the other three tillage methods in all soil depths from 0 to 60 cm. The mean SOC concentration at 0–60 cm soil depth followed the sequence: NT (22.74 g kg^?1) > CTF (14.57 g kg^?1) > TR (13.10 g kg^?1) > CTR (11.92 g kg^?1). SOC concentrations under NT were significantly higher than TR and CTR (P < 0.01), and higher than CTF treatment (P < 0.05). The SOC storage was calculated on equivalent soil mass basis. Results showed that the highest SOC storage at 0–60 cm depth presented in NT, which was 158.52 Mg C ha^?1, followed by CTF (106.74 Mg C ha^?1), TR (93.11 Mg C ha^?1) and CTR (88.60 Mg C ha^?1). Compared with conventional tillage (CTF), the total SOC storage in NT increased by 48.51%, but decreased by 16.99% and 12.77% under CTR and TR treatments, respectively. The effect of tillage on DOC was significant at 0–10 cm soil layer, and DOC concentration was much higher under CTF than the other three treatments (P < 0.01). Throughout 0–60 cm soil depth, DOC concentrations were 32.92, 32.63, 26.79 and 22.10 mg kg^?1 under NT, CTF, CTR and TR, and the differences among the four treatments were not significant (P > 0.05). In conclusion, NT increased SOC concentration and storage compared to conventional tillage operation but not for DOC.     

Variability of soil carbon sequestration capability and microbial activity of different types of salt marsh soils at Chongming Dongtan

Variations in the soil carbon sequestration capability of different types of salt marsh soils at Chongming Dongtan and its influencing factors were studied by analyzing the soil organic carbon (SOC) content, organic matter input and microbial activities. The results indicated that the total SOC content at Area A (southeast of Dongtan, sandy soil with Phragmites communis) was only 46.11% of that of Area B (northeast of Dongtan, clay soil with mixed P. communis and Spartina alterniflora) (P = 0.000 < 0.05), but their organic matter input per year was almost identical. These findings implied that Area B had a lower output of SOC. The microbial biomass at Area A was 3.83 times greater than that at Area B (P = 0.049 < 0.05); the soil catalase and invertase activities at Area A, which were related to carbon metabolism, were 60.31% (P = 0.006 < 0.05) and 34.33% (P = 0.021 < 0.05) higher than at Area B, respectively; and the soil respiration at Area A was also higher than at Area B. These findings implied that the microbial activities at Area A were greater than those at Area B, and therefore the carbon metabolism was rapid, resulting in increased SOC output at Area A. Increased water content and salinity in the clay soil at Area B may inhibit the microbial activities, thereby reducing the decomposition of the organic matter and enhancing carbon sequestration. In addition, some artificial measures for controlling spread of S. alterniflora at Area B (mowing/digging and tillage (M + D); mowing/digging and tillage/waterlogging (M + D + W)) were found to generally improve the microbial activity of soil, thereby increasing SOC output. However, when the two different physical controlling modes were compared, the SOC and microbial activities of the soil subjected to the M + D + W treatment were relatively high and low, respectively, due to waterlogging restraining the microbial metabolism. These findings indicated that the difference in microbial activities was the important factor leading to variability in the SOC sequestration capability between Areas A and B. Additionally, with the exception of soil texture and vegetation types, environmental conditions and artificial turbulence also influenced microbial activities of soil, and hence SOC output and organic carbon sequestration capability.     

Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques

Forests are living dynamic systems and these unique ecosystems are essential for life on earth. Forest fires are one of the major environmental concerns, economic, and social in the worldwide. The aim of current research is to identify general indicators influencing on forest fire and compare forest fire susceptibility maps based on the boosted regression tree (BRT), generalized additive model (GAM), and random forest (RF) data mining models in the Minudasht Township, Golestan Province, Iran. According to expert opinion and literature review, fifteen condition factors on forest fire have been selected in the study area. These are slope degree, slope aspect, elevation, topographic wetness index (TWI), topographic position index (TPI), plan curvature, wind effect, annual temperature and rainfall, soil texture, distance to roads, rivers, and villages, normalized difference vegetation index (NDVI), and land use. Forest fire locations were identified using MODIS images, historical records, and extensive field checking. 106 (≈70%) locations, out of 151 forest fires identified, were used for models building/training, while the remaining 45 (≈30%) cases were used for the models validation.BRT, GAM, and RF data mining models were used to distinguish between presence and absence of forest fires and its mapping. These algorithms were used to perform feature selection in order to reveal the variables that contribute more to forest fire occurrence. Finally, for validation of models, the area under the curve (AUC) for forest fire susceptibility maps was calculated. The validation of results showed that AUC for three mentioned models varies from 0.7279 to 0.8770 (AUC_BRT = 80.84%, AUC_GAM = 87.70%, and AUC_RF = 72.79%,). Results indicated that the main drivers of forest fire occurrence were annual rainfall, distance to roads, and land use factors. The results can be applied to primary warning, fire suppression resource planning, and allocation work.     

Methane emissions in Danish riparian wetlands: Ecosystem comparison and pursuit of vegetation indexes as predictive tools

The present study was conducted to (i) investigate parameters influencing the fluxes of the greenhouse gas methane (CH₄) in Danish riparian wetlands with contrasting vegetation characteristics and (ii) develop models relating CH₄ emissions to soil and/or vegetation parameters integrating the spatial and temporal variability in the fluxes. Fluxes of CH₄ were monitored in 12 wetland plots over a year using static chambers, yielding a dataset with more than 800 measured fluxes of CH₄. Yearly emissions of CH₄ ranged from −0.2 to 38.3 g CH₄-C m⁻² year⁻¹, and significant effects of groundwater level, soil temperature (10 cm depth), peat depth, sulfate, nitrate, and soil carbon content were found. Two methods based on easily available environmental parameters to estimate yearly CH₄ emissions from riparian wetlands are presented. The first uses a generalized linear model (GLM) to predict yearly CH₄ emissions based on the humidity preference of vegetation (Ellenberg-F), peat depth and degree of humification of the peat (von Post index). The second method relies solely on plant species composition and uses weighted-average regression and calibration to link the vegetation assemblage to yearly CH₄ emission. Both models gave reliable predictions of the yearly CH₄ fluxes in riparian wetlands (modeling efficiency > 0.35). Our findings support the use of vegetation, possibly in combination with some soil parameters such as peat depth, as indicator of CH₄ emission in wetlands.     

Towards the application of soil organic matter as an indicator of forest ecosystem productivity: Deriving thresholds,developing monitoring systems,and evaluating practices

Soil organic matter (SOM), typically measured as soil organic carbon (SOC), has been widely recognized as a critical linkage between forest management and long-term site productivity. However, its use as an indicator of sustainable forest management practices has been limited both by difficulties in detecting changes in soil carbon due to inherent high variability and by challenges associated with determining appropriate thresholds for loss. In this study we evaluate a methodology for using field measures of total SOC (forest floor to 60 cm depth in mineral soil) in conjunction with a mechanistic forest growth model to derive threshold values for total SOC with respect to the maintenance of ecosystem productivity for a lodgepole pine (Pinus contorta) forest in the central interior region of British Columbia. We also examine the practicality of implementing a sustainable forest management (SFM) monitoring program around this measure and the potential long-term impact of alternative management scenarios on the indicator.Total SOC contents for the different site types sampled in the Quesnel region ranged from 35 to 57 t ha^?1. Long-term simulations of biomass extraction over several rotations showed a near 1:1 ratio in the relative decline of ecosystem productivity associated with relative declines in total SOC. A power analysis revealed that a mean sampling intensity of n = 12–25 and n = 8–17 would be required to detect 20% and 30% losses of total SOC, respectively, depending on the level of statistical power desired. The sampling intensity required for an effective monitoring program was significantly reduced by summing SOC for all soil layers to limit sampling error related to determination of layer boundaries. A modelling analysis of the effect of rotation length on SOC for the Quesnel forest types, suggests that rotation lengths shorter than 75 years should be avoided to prevent declines in ecosystem productivity. Our results confirm that the combination of modelling and statistical techniques can be successfully used to develop cost-efficient monitoring plans of sustainability of forest management, with SOC as a valid indicator of ecosystem productivity.     

The carbon flux of global rivers: A re-evaluation of amount and spatial patterns

Global rivers connect three large carbon reservoirs in the world: soil, atmosphere, and ocean. The amount and spatial pattern of riverine carbon flux are essential for the global carbon budget but are still not well understood. Therefore, three linear regression models for riverine DOC (dissolved organic carbon), POC (particulate organic carbon), and DIC (dissolved inorganic carbon) fluxes were established with related generating and transfer factors based on an updated global database. The three models then were applied to simulate the spatial distribution of riverine DOC, POC, and DIC fluxes and to estimate the total global riverine carbon flux. The major conclusions of this study are as follows: the correlation analysis showed that riverine DOC flux is significantly related to discharge (r² = 0.93, n = 109) and soil organic carbon amount (r² = 0.60), POC flux increases with discharge (r² = 0.55, n = 98) and amount of soil erosion (r² = 0.48), and DIC flux is strongly linked to CO₂ consumption by rock weathering (r² = 0.66, n = 111) and discharge (r² = 0.63). In addition, Asia exports more DOC and POC than other continents and North America exports more DIC. The Atlantic Ocean accepts the major portion of riverine DOC, POC, and DIC fluxes of all the oceans. The highest riverine DOC flux occurs in the 0–30°S zone, and the highest riverine POC and DIC fluxes appear in the 30–60°N zone. Furthermore, re-estimation revealed that global rivers export approximately 1.06 Pg C to oceans every year, including 0.24 Pg DOC, 0.24 Pg POC, 0.41 Pg DIC, and 0.17 Pg PIC.     

Carbon stock and allocation of five restoration ecosystems in subalpine coniferous forest zone in Western Sichuan Province, Southwest China

As the largest carbon pool of the terrestrial ecosystem, forest plays a key role in sequestrating and reserving greenhouse gases. With the method of replacing space with time, the typical restoration ecosystems of herb (dominated by Deyeuxia scabrescens, P1), shrub (dominated by Salix paraqplesia, P2), broadleaf (dominated by Betula platyphylla, P3), mixed forest (dominated by Betula spp. and Abies faxoniana, P4), and climax (dominated by Abies faxoniana, P5) were selected to quantify the carbon stock and allocation in the subalpine coniferous forest in Western Sichuan (SCFS). The results indicated that the soil organism carbon (SOC) stock decreased with the depth of soil layer, and the SOC per layer and the total SOC increased largely with the vegetation restoration. The contribution of SOC to the carbon stock of ecosystems decreased with the vegetation restoration from 89.45% to 27.06%, while the quantity was from 94.00 to 223.00 t C hm^?2. The carbon stock in ground cover increased with the vegetation restoration, and its contribution to the carbon stock of ecosystems was similar (3–4% of the total). Following the vegetation restoration, the plant carbon stock multiplied and reached to 430.86 ± 49.49 t C hm^?2 at the climax phase. During the restoration, the carbon stock of different layers increased, and the contribution of belowground to the carbon stock of ecosystems decreased sharply. The carbon stock on ecosystem scale of the climax phase was 5.89 times that of the herb phase. Our results highlighted that the vegetation restoration in SCFS was a large carbon sink.     

Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil–landscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28 km × 28 km and Jiangyan County, 38 km × 50 km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3 km in Jiangyan but 16 km in Peixian, and accuracy of best models differed by a factor of two (3.3 g/kg and 6.3 g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.     

Tillage effects on carbon footprint and ecosystem services of climate regulation in a winter wheat–summer maize cropping system of the North China Plain

Inappropriate farm practices can increase greenhouse gases (GHGs) emissions and reduce soil organic carbon (SOC) sequestration, thereby increasing carbon footprints (CFs), jeopardizing ecosystem services, and affecting climate change. Therefore, the objectives of this study were to assess the effects of different tillage systems on CFs, GHGs emissions, and ecosystem service (ES) values of climate regulation and to identify climate-resilient tillage practices for a winter wheat (Triticum aestivum L.)-summer maize (Zea mays L.) cropping system in the North China Plain (NCP). The experiment was established in 2008 involving no-till with residue retention (NT), rotary tillage with residue incorporation (RT), sub-soiling with residue incorporation (ST), and plow tillage with residue incorporation (PT). The results showed that GHGs emissions from agricultural inputs were 6432.3–6527.3 kg CO₂-eq ha⁻¹ yr⁻¹ during the entire growing season, respectively. The GHGs emission from chemical fertilizers and irrigation accounted for >80% of that from agricultural inputs during the entire growing season. The GHGs emission from agricultural inputs were >2.3 times larger in winter wheat than that in the summer maize season. The CFs at yield-scale during the entire growing season were 0.431, 0.425, 0.427, and 0.427 without and 0.286, 0.364, 0.360, and 0.334 kg CO₂-eq kg⁻¹ yr⁻¹ with SOC sequestration under NT, RT, ST, and PT, respectively. Regardless of SOC sequestration, the CFs of winter wheat was larger than that of summer maize. Agricultural inputs and SOC change contributed mainly to the component of CFs of winter wheat and summer maize. The ES value of climate regulation under NT was ¥159.2, 515.6, and 478.1 ha⁻¹ yr⁻¹ higher than that under RT, ST, and PT during the entire growing season. Therefore, NT could be a preferred “Climate-resilient” technology for lowering CFs and enhancing ecosystem services of climate regulation for the winter wheat–summer maize system in the NCP.     

Effect of different fertilization modes on soil organic carbon sequestration in paddy fields in South China: A meta-analysis

Evidences have shown that fertilizer application could affect soil organic carbon (SOC) content in cropland. South China is the main production region of rice, in which many different fertilization practices have been widely used during the last several decades, but the effect of different fertilization modes on SOC sequestration in paddy fields in this region was scarcely studied. Based on 274 samples taken from 44 paddy field experimental sites in South China, a meta-analysis was performed to quantify the relative annual change of SOC content (RAC) and SOC sequestration duration in paddy fields under five fertilization modes (inorganic nitrogen fertilization, N; inorganic nitrogen and phosphorus fertilization, NP; inorganic nitrogen, phosphorus, and potassium fertilization, NPK; organic fertilization, O; and inorganic combined with organic fertilization, OF). The results showed that the RAC under the five fertilization modes was 0–0.4 g kg⁻¹ yr⁻¹, with increments of 0.19 and 0.23 g kg⁻¹ yr⁻¹ in double cropping systems and triple cropping systems, respectively. The RAC under the O and OF treatments was higher than that under the N, NP and NPK treatments and was the highest (0.32 g kg⁻¹ yr⁻¹) under OF among the five treatments. The SOC accumulation rate decreased with time, and the SOC sequestration duration under N, NP, NPK, O and OF was approximately 23, 28, 46, 64 and 55 years, respectively. The accumulation enhancement rate of SOC over the whole SOC sequestration period under the N, NP, NPK, O and OF treatments was approximately 12.9%, 23.4%, 29.3%, 47.2% and 55.1%, respectively. The OF treatment performed the largest potential for SOC sequestration in paddy fields in South China under long-term conditions.     

Environmental indicators for estimating the potential soil respiration rate in alpine zone

Soil respiration is the main form of carbon flux from soil to atmosphere in the global carbon cycle. The effect of temperature on soil respiration rate is important in evaluating the potential feedback of soil organic carbon to global warming. We incubated soils from the alpine meadow zone and upper rocky zone along an altitudinal gradient (4400–5500 m a.s.l.) on the Tibetan Plateau under various temperature and soil moisture conditions. We evaluated the potential effects of temperature and soil moisture on soil respiration and its variation across altitudes. Soil respiration rates increased as the temperature increased. At 60% of soil water content, they averaged 0.21–5.33 μmol g soil⁻¹ day⁻¹ in the alpine meadow zone and 0.11–0.50 μmol g soil⁻¹ day⁻¹ in the rocky zone over the experimental temperature range. Soil respiration rates in the rocky zone did not increase between 25 and 35 °C, probably because of heat stress. Rates of decomposition of organic matter were high in the rocky zone, where the CN ratio was smaller than in the middle altitudes. Soil respiration rates also increased with increasing soil water content from 10% to 80% at 15 °C, averaging 0.04–2.00 μmol g soil⁻¹ day⁻¹ in the alpine meadow zone and 0.03–0.35 μmol g soil⁻¹ day⁻¹ in the rocky zone. Maximum respiration rates were obtained in the middle part of the alpine slope in any case of experimental temperature and soil moisture. The change patterns in soil respiration rate along altitude showed similar change pattern in soil carbon content. Although the altitude is a variable including various environmental factors, it might be used as a surrogate parameter of soil carbon content in alpine zone. Results suggest that temperature, soil moisture and altitude are used as appropriate environmental indicators for estimating the spatial distribution of potential soil respiration in alpine zone.     

Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system

Canopy height (H_canopy) and aboveground biomass (AGB) of crops are two basic agro-ecological indicators that can provide important indications on the growth, light use efficiency, and carbon stocks in agro-ecosystems. In this study, hundreds of stereo images with very high resolution were collected to estimate H_canopy and AGB of maize using a low-cost unmanned aerial vehicle (UAV) system. Millions of point clouds that are related to the structure from motion (SfM) were produced from the UAV stereo images through a photogrammetric workflow. Metrics that are commonly used in airborne laser scanning (ALS) were calculated from the SfM point clouds and were tested in the estimation of maize parameters for the first time. In addition, the commonly used spectral vegetation indices calculated from the UAV orthorectified image were also tested. Estimation models were established based on the UAV variables and field measurements with cross validation, during which the performance of the UAV variables was quantified. Finally, the following results were achieved: (1) the spatial patterns of maize H_canopy and AGB were predicted by a multiple stepwise linear (SWL) regression model (R² = 0.88, rRMSE = 6.40%) and a random forest regression (RF) model (R² = 0.78, rRMSE = 16.66%), respectively. (2) The UAV-estimated maize parameters were proved to be comparable to the field measurements with a mean error (ME) of 0.11 m for H_canopy, and 0.05 kg/m² for AGB. (3) The SfM point metrics, especially the mean point height (H_mean) greatly contributed to the estimation model of maize H_canopy and AGB, which can be promising indicators in the detection of maize biophysical parameters. To conclude, the variations in spectral and structural attributes for maize canopy should be simultaneously considered when only simple RGB images are available for estimating maize AGB. This study provides some suggestions on how to make full use of the low-cost and high-resolution UAV stereo images in precision agro-ecological applications and management.     

Sensitivity and resistance of soil fertility indicators to land-use changes: New concept and examples from conversion of Indonesian rainforest to plantations

Tropical forest conversion to agricultural land leads to a strong decrease of soil organic carbon (SOC) stocks. While the decrease of the soil C sequestration function is easy to measure, the impacts of SOC losses on soil fertility remain unclear. Especially the assessment of the sensitivity of other fertility indicators as related to ecosystem services suffers from a lack of clear methodology. We developed a new approach to assess the sensitivity of soil fertility indicators and tested it on biological and chemical soil properties affected by rainforest conversion to plantations. The approach is based on (non-)linear regressions between SOC losses and fertility indicators normalized to their level in a natural ecosystem. Biotic indicators (basal respiration, microbial biomass, acid phosphatase), labile SOC pools (dissolved organic carbon and light fraction) and nutrients (total N and available P) were measured in Ah horizons from rainforests, jungle rubber, rubber (Hevea brasiliensis) and oil palm (Elaeis guineensis) plantations located on Sumatra. The negative impact of land-use changes on all measured indicators increased in the following sequence: forest < jungle rubber < rubber < oil palm. The basal respiration, microbial biomass and nutrients were resistant to SOC losses, whereas the light fraction was lost stronger than SOC. Microbial C use efficiency was independent on land use. The resistance of C availability for microorganisms to SOC losses suggests that a decrease of SOC quality was partly compensated by litter input and a relative enrichment by nutrients. However, the relationship between the basal respiration and SOC was non-linear; i.e. negative impact on microbial activity strongly increased with SOC losses. Therefore, a small decrease of C content under oil palm compared to rubber plantations yielded a strong drop in microbial activity. Consequently, management practices mitigating SOC losses in oil palm plantations would strongly increase soil fertility and ecosystem stability. We conclude that the new approach enables quantitatively assessing the sensitivity and resistance of diverse soil functions to land-use changes and can thus be used to assess resilience of agroecosystems with various use intensities.     

Estimation of big sagebrush leaf area index with terrestrial laser scanning

Accurate monitoring and quantification of the structure and function of semiarid ecosystems is necessary to improve carbon and water flux models that help describe how these systems will respond in the future. The leaf area index (LAI, m² m⁻²) is an important indicator of energy, water, and carbon exchange between vegetation and the atmosphere. Remote sensing techniques are frequently used to estimate LAI, and can provide users with scalable measurements of vegetation structure and function. We tested terrestrial laser scanning (TLS) techniques to estimate LAI using structural variables such as height, canopy cover, and volume for 42 Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis Beetle & Young) shrubs across three study sites in the Snake River Plain, Idaho, USA. The TLS-derived variables were regressed against sagebrush LAI estimates calculated using specific leaf area measurements, and compared with point-intercept sampling, a field method of estimating LAI. Canopy cover estimated with the TLS data proved to be a good predictor of LAI (r² = 0.73). Similarly, a convex hull approach to estimate volume of the shrubs from the TLS data also strongly predicted LAI (r² = 0.76), and compared favorably to point-intercept sampling (r² = 0.78), a field-based method used in rangelands. These results, coupled with the relative ease-of-use of TLS, suggest that TLS is a promising tool for measuring LAI at the shrub-level. Further work should examine the structural measures in other similar shrublands that are relevant for upscaling LAI to the plot-level (i.e., hectare) using data from TLS and/or airborne laser scanning and to regional levels using satellite-based remote sensing.     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

Effects of rest grazing on organic carbon storage in Stipa Baicalensis steppe in Inner Mongolia

In order to evaluate the potential effects of rest grazing on organic carbon storage on the Stipa baicalensis steppe in Inner Mongolia, compared the S. baicalensis steppes after rest grazing for 3 years, 6 years, and 9 years, using potassium dichromate heating method, this study analyzed the organic carbon storage of plant and soil in the steppes among different periods of rest grazing. The results indicated that as the rest grazing years prolonged, the biomass included above-ground parts, litters and underground plant parts(roots) of the plant communities all increased, meanwhile the carbon content of the biomass increased with the rest grazing years prolonged. For the zero rest grazing (RG0) steppe and the steppes after a rest grazing of 3 years (RG3a), 6 years (RG6a), 9 years (RG9a), the carbon storage in above-ground parts of plant communities were 42.60 g C/m², 66.33 g C/m², 83.46 g C/m², 100.29 g C/m² respectively; the carbon storage of litters were 7.85 g C/m², 9.12 g C/m², 9.18 g C/m², 11.54 g C/m² separately; the carbon storage of underground plant parts (0–100 cm) were 281.40 g C/m², 576.38 g C/m², 745.33 g C/m², 1279.61 g C/m² respectively; and the carbon storage in 0–100 cm soil were 22991.14 g C/m², 24687.75 g C/m², 26564.86 g C/m²,33041.55 g C/m². The results suggested that as the rest grazing years prolonged, the organic carbon storage in plant communities and soil increased. The carbon storage of underground plant parts and soil organic carbon mainly concentrated in 0–40 cm soil. After rest grazing for 3 years, 6 years, and 9 years, the increased soil organic carbon were as the 81.14%, 85.84%, and 89.46% of the total increased carbon; From the perspective of carbon sequestration cost, the total cost of RG3a, RG6a and RG9a were 2903.40 RMB/hm², 5806.80 RMB/hm², and 8710.20 RMB/hm². The cost reduced with the extension of rest grazing years, 0.17 RMB/kg C, 0.16 RMB/kg C, 0.09 RMB/kg C for RG3a, RG6a and RG9a respectively. From the growth characteristics of grassland plants, the spring was one of the two avoid grazing periods, timely rest grazing could effectively restore and update grassland vegetation, and was beneficial to the sustainable use of grassland. From the available data, the organic carbon storage of RG9a was the highest, while the cost of carbon sequestration was the lowest. Therefore, spring rest grazing should be encouraged to continue for it was proved to be a very efficient grassland use measures.     

Plant species and communities assessment in interaction with edaphic and topographic factors; an ecological study of the mount Eelum District Swat,Pakistan

The current analyses of vegetation were aimed to study the different effects of environmental variables and plant species and communities interaction to these variables, identified threats to local vegetation and suggestion for remedial measures in the Mount Eelum, Swat, Pakistan. For assessment of environmental variability quantitative ecological techniques were used through quadrats having sizes of 2 × 2, 5 × 5 and 10 × 10 m² for herbs, shrubs and trees respectively. Result of the present study revealed 124 plant species in the study area. Canonical Correspondence Analysis (CCA) was used to analyze the ecological gradient of vegetation. The environmental data and species abundance were used in CANOCO software version 4.5. The presence absence data of plant species were elaborated with Cluster and Two Way Cluster Analysis techniques using PC-ORD version 5 to show different species composition that resulted in five plant communities. Findings indicate that elevation, aspect and soil texture are the strongest variables that have significant effect on species composition and distribution of various communities shown with P value 0.0500. It is recommended to protect and use sensibly whole of the Flora normally and rare species particularly in the region.     

Forest carbon storage along the north-south transect of eastern China: Spatial patterns,allocation, and influencing factors

Forests play an important role in sequestrating atmospheric CO₂; therefore, understanding the spatial variations and controlling mechanisms of forest carbon (C) storage is important. In this study, we collected data on forest C storage along a north-south transect of eastern China from literature published between 2004 and 2014. The collected data, which were from over 2000 plots, allowed us to explore the latitudinal patterns in forest C storage. The results showed that vegetation C storage decreased with increasing latitude, while soil C storage increased. This spatial pattern of vegetation C storage was more apparent for mature forests (forest age > 80 years). Furthermore, latitudinal patterns in forest C storage, both in vegetation and in soil, became stronger with increasing statistical scale, increasing from plot scale to latitudinal scale (2–5°). However, total forest C storage (vegetation + soil) had no apparent latitudinal pattern. Interestingly, the allocation ratios of forest C storage between vegetation and soil had a negative logarithmic relationship with latitude. These results suggest that in eastern China, climatic factors control latitudinal patterns in the forest C storage of vegetation and soil, albeit in different ways (positive for vegetation and negative for soil), and also control the allocation ratios of forest C storage between vegetation and soil. Furthermore, the latitudinal patterns of forest C storage were opposite for vegetation and soil, resulting from the different climatic controlling mechanism.     

Effect of fire on soil nutrients and under storey vegetation in Chir pine forest in Garhwal Himalaya,India

The study was carried out in the Pinus roxburghii Sargent (Chir pine) forest in the sub-tropical region of Garhwal Himalaya to assess the effect of fire on soil nutrient status at different altitudes (700 m, 800 m and 1000 m), soil depths (0–20 cm, 20–40 cm and 40–60 cm) and on under storey vegetation. The soil nutrients and under storey vegetation were assessed before fire (pre-fire) and after fire (post-fire). The results of the study indicate that fire plays an important role in soil nutrient status and under storey vegetation. The nutrients (soil organic carbon, nitrogen, phosphorus and potassium), decreased in post-fire assessment and with increasing altitudes, and soil depths, compared to pre-fire assessment. The under storey vegetation diminished after fire in all forest sites. The study concludes that in Chir pine forest, fire plays a role in reducing soil nutrients along the altitudinal gradient, soil depths and under storey vegetation. Thus, these nutrients can be saved through some management practices e.g. by early controlled burning and by educating local villagers about the negative impacts of severe wild fires on soil and vegetation.