Global rain-fed,irrigated, and paddy croplands: A new high resolution map derived from remote sensing,crop inventories and climate data

Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.     

Temperature and emissivity separation and mineral mapping based on airborne TASI hyperspectral thermal infrared data

Thermal infrared remote sensing (8–12 μm) (TIR) has great potential for geologic remote sensing studies. TIR has been successfully used for terrestrial and planetary geologic studies to map surface materials. However, the complexity of the physics and the lack of hyperspectral data make the studies under-investigated. A new generation of commercial hyperspectral infrared sensors, known as Thermal Airborne Spectrographic Imager (TASI), was used for image analysis and mineral mapping in this study. In this paper, a combined method integrating normalized emissivity method (NEM), ratio algorithm (RATIO) and maximum–minimum apparent emissivity difference (MMD), being applied in multispectral data, has been modified and used to determine whether this method is suitable for retrieving emissivity from TASI hyperspectral data. MODTRAN 4 has been used for the atmospheric correction. The retrieved emissivity spectra matched well with the field measured spectra except for bands 1, 2, and 32. Quartz, calcite, diopside/hedenbergite, hornblende and microcline have been mapped by the emissivity image. Mineral mapping results agree with the dominant minerals identified by laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Both of the results indicated that the atmospheric correction method and the combined temperature–emissivitiy method are suitable for TASI image. Carbonate skarnization was first found in the study area by the spatial extent of diopside. Chemical analyses of the skarn samples determined that the Au content was 0.32–1.74 g/t, with an average Au content of 0.73 g/t. This information provides an important resource for prospecting for skarn type gold deposits. It is also suggested that TASI is suitable for prospect and deposit scale exploration.     

Very high spatial resolution optical and radar imagery in tracking water level fluctuations of a small inland reservoir

Tracking water level fluctuations in small lakes and reservoirs is important in order to better understand and manage these ecosystems. A geographic object-based image analysis (GEOBIA) method using very high spatial and temporal resolution optical (Pléiades) and radar (COSMO-SkyMed and TerraSAR-X) remote sensing imagery is presented here which (1) tracks water level fluctuations via variations in water surface area and (2) avoids common difficulties found in using single-band radar images for water-land image classification. Results are robust, with over 98% of image surface area correctly classified into land or water, R² = 0.963 and RMSE = 0.42 m for a total water level fluctuation range of 5.94 m. Multispectral optical imagery is found to be more straightforward in producing results than single-band radar imagery, but the latter crucially increase temporal resolution to the point where fluctuations can be satisfactorily tracked in time. Moreover, an analysis suggests that high and medium spatial resolution imagery is sufficient, in at least some cases, in tracking the water level fluctuations of small inland reservoirs. Finally, limitations of the methodology presented here are briefly discussed along with potential solutions to overcome them.     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.     

基于遥感技术的长白山火山区地热预测研究

在长白山火山区地热调查研究中,基于ASTER遥感数据,运用比辐射率归一化方法,定量反演了区域地表温度,并在此基础上,通过对地质构造的遥感解译,结合对水热活动及深源气体释放特征等资料的关联分析,预测长白山火山区地热资源存在的有利区.研究发现,六道沟-长白山天池-甑峰山北东向断裂的天池段和长白山火山环形断裂的长白聚龙段是地热勘探的有利区,也是火山监测的最佳场所.     

Thermal infrared imaging of the temporal variability in stomatal conductance for fruit trees

Repeated measurements using thermal infrared remote sensing were used to characterize the change in canopy temperature over time and factors that influenced this change on ‘Conference’ pear trees (Pyrus communis L.). Three different types of sensors were used, a leaf porometer to measure leaf stomatal conductance, a thermal infrared camera to measure the canopy temperature and a meteorological sensor to measure weather variables. Stomatal conductance of water stressed pear was significantly lower than in the control group 9 days after stress began. This decrease in stomatal conductance reduced transpiration, reducing evaporative cooling that increased canopy temperature. Using thermal infrared imaging with wavelengths between 7.5 and13 μm, the first significant difference was measured 18 days after stress began. A second order derivative described the average rate of change of the difference between the stress treatment and control group. The average rate of change for stomatal conductance was 0.06 (mmol m⁻² s⁻¹) and for canopy temperature was −0.04 (°C) with respect to days. Thermal infrared remote sensing and data analysis presented in this study demonstrated that the differences in canopy temperatures between the water stress and control treatment due to stomata regulation can be validated.     

Estimating dissolved organic carbon concentration in turbid coastal waters using optical remote sensing observations

Dissolved Organic Carbon (DOC) is an important component in the global carbon cycle. It also plays an important role in influencing the coastal ocean biogeochemical (BGC) cycles and light environment. Studies focussing on DOC dynamics in coastal waters are data constrained due to the high costs associated with in situ water sampling campaigns. Satellite optical remote sensing has the potential to provide continuous, cost-effective DOC estimates. In this study we used a bio-optics dataset collected in turbid coastal waters of Moreton Bay (MB), Australia, during 2011 to develop a remote sensing algorithm to estimate DOC. This dataset includes data from flood and non-flood conditions. In MB, DOC concentration varied over a wide range (20–520 μM C) and had a good correlation (R² = 0.78) with absorption due to coloured dissolved organic matter (CDOM) and remote sensing reflectance. Using this data set we developed an empirical algorithm to derive DOC concentrations from the ratio of R_rs(412)/R_rs(488) and tested it with independent datasets. In this study, we demonstrate the ability to estimate DOC using remotely sensed optical observations in turbid coastal waters.     

Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass

The land surface temperature (LST) is an important parameter when studying the interface between the atmosphere and the Earth's surface. Compared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing is better able to overcome atmospheric influences and to estimate the LST, especially in cloudy regions. However, methods for estimating PMW LSTs at the country and continental scales are still rare. The necessity of training such methods from a temporally dynamic perspective also needs further investigations. Here, a temporally land cover based look-up table (TL-LUT) method is proposed to estimate the LSTs from AMSR-E data over the Chinese landmass. In this method, the synergies between observations from MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), which are onboard the same Aqua satellite, are explored. Validation with the synchronous MODIS LSTs demonstrates that the TL-LUT method has better performances in retrieving LSTs with AMSR-E data than the method that uses a single brightness temperature in 36.5 GHz vertical polarization channel. The accuracy of the TL-LUT method is better than 2.7 K for forest and 3.2 K for cropland. Its accuracy varies according to land cover type, time of day, and season. When compared with the in-situ measured LSTs at four sites without urban warming in the Tibet Plateau, the standard errors of estimation between the estimated AMSR-E LST and in-situ measured LST are from 5.1 K to 6.0 K in the daytime and 3.1 K to 4.5 K in the nighttime. Further comparison with the in-situ measured air temperatures at 24 meteorological stations confirms the good performance of the TL-LUT method. The feasibility of PMW remote sensing in estimating the LST for China can complement the TIR data and can, therefore, aid in the generation of daily LST maps for the entire country. Further study of the penetration of PMW radiation would benefit the LST estimations in barren and other sparsely vegetated environments.     

Development of a 3-D urbanization index using digital terrain models for surface urban heat island effects

This study assesses surface urban heat island (SUHI) effects during heat waves in subtropical areas. Two cities in northern Taiwan, Taipei metropolis and its adjacent medium-sized city, Yilan, were selected for this empirical study. Daytime and night time surface temperature and SUHI intensity of both cities in five heat wave cases were obtained from MODIS Land-Surface Temperature (LST) and compared. In order to assess SUHI in finer spatial scale, an innovated three-dimensional Urbanization Index (3DUI) with a 5-m spatial resolution was developed to quantify urbanization from a 3-D perspective using Digital Terrain Models (DTMs). The correlation between 3DUI and surface temperatures were also assessed. The results obtained showed that the highest SUHI intensity in daytime was 10.2 °C in Taipei and 7.5 °C in Yilan. The SUHI intensity was also higher than that in non-heat-wave days (about 5 °C) in Taipei. The difference in SUHI intensity of both cities could be as small as only 1.0 °C, suggesting that SUHI intensity was enhanced in both large and medium-sized cities during heat waves. Moreover, the surface temperatures of rural areas in Taipei and Yilan were elevated in the intense heat wave cases, suggesting that the SUHI may reach a plateau when the heat waves get stronger and last longer. In addition, the correlation coefficient between 3DUI and surface temperature was greater than 0.6. The innovative 3DUI can be employed to assess the spatial variation of temperatures and SUHI intensity in much finer spatial resolutions than measurements obtained from remote sensing and weather stations. In summary, the empirical results demonstrated intensified SUHI in large and medium-sized cities in subtropical areas during heat waves which could result in heat stress risks of residents. The innovative 3DUI can be employed to identify vulnerable areas in fine spatial resolutions for formulation of heat wave adaptation strategies.     

Brown and green LAI mapping through spectral indices

When crops senescence, leaves remain until they fall off or are harvested. Hence, leaf area index (LAI) stays high even when chlorophyll content degrades to zero. Current LAI approaches from remote sensing techniques are not optimized for estimating LAI of senescent vegetation. In this paper a two-step approach has been proposed to realize simultaneous LAI mapping over green and senescent croplands. The first step separates green from brown LAI by means of a newly proposed index, ‘Green Brown Vegetation Index (GBVI)’. This index exploits two shortwave infrared (SWIR) spectral bands centred at 2100 and 2000 nm, which fall right in the dry matter absorption regions, thereby providing positive values for senescent vegetation and negative for green vegetation. The second step involves applying linear regression functions based on optimized vegetation indices to estimate green and brown LAI estimation respectively. While the green LAI index uses a band in the red and a band in the red-edge, the brown LAI index uses bands located in the same spectral region as GBVI, i.e. an absorption band located in the region of maximum absorption of cellulose and lignin at 2154 nm, and a reference band at 1635 nm where the absorption of both water and dry matter is low. The two-step approach was applied to a HyMap image acquired over an agroecosystem at the agricultural site Barrax, Spain.     

Validation of the SEBS-derived sensible heat for FY3A/VIRR and TERRA/MODIS over an alpine grass region using LAS measurements

In this study, sensible heat (H) calculation using remote sensing data over an alpine grass landscape is conducted from May to September 2010, and the calculation is validated using LAS (large aperture scintillometers) measurements. Data from two remote sensing sensors (FY3A-VIRR and TERRA-MODIS) are analysed. Remote sensing data, combined with the ground meteorological observations (pressure, temperature, wind speed, humidity) are fed into the SEBS (Surface Energy Balance System) model. Then the VIRR-derived sensible heat (VIRR_SEBS_H) and MODIS-derived sensible heat (MODIS_SEBS_H) are compared with the LAS-estimated H, which are obtained at the respective satellite overpass time. Furthermore, the similarities and differences between the VIRR_SEBS_H and MODIS_SEBS_H values are investigated. The results indicate that VIRR data quality is as good as MODIS data for the purpose of H estimation. The root mean square errors (rmse) of the VIRR_SEBS_H and MODIS_SEBS_H values are 45.1098 W/m² (n = 64) and 58.4654 W/m² (n = 71), respectively. The monthly means of the MODIS_SEBS_H are marginally higher than those of VIRR_SEBS_H because the satellite overpass time of the TERRA satellite lags by 25 min to that of the FT3A satellite. Relative evaporation (EFr), which is more time-independent, shows a higher agreement between MODIS and VIRR. Many common features are shared by the VIRR_SEBS_H and the MODIS_SEBS_H, which can be attributed to the SEBS model performance. In May–June, H is over-estimated with more fluctuations and larger rmse, whereas in July–September, H is under-estimated with fewer fluctuations and smaller rmse. Sensitivity analysis shows that potential temperature gradient (delta_T) plays a dominant role in determining the magnitude and fluctuation of H. The largest rmse and over-estimation in H occur in June, which could most likely be attributed to high delta_T, high wind speed, and the complicated thermodynamic state during the transitional period when bare land transforms to dense vegetation cover.     

Uncertainty analysis of remote sensing of colored dissolved organic matter: Evaluations and comparisons for three rivers in North America

The uncertainties involved in remote sensing inversion of CDOM (Colored Dissolved Organic Matter) were analyzed in estuarine and coastal regions of three North American rivers: Mississippi, Hudson, and Neponset. Water optical and biogeochemical properties, including CDOM absorption and above-surface spectra, were collected in very high resolution. CDOM’s concentrations (a_g(440), absorption coefficient at 440 nm) were inverted from EO-1 Hyperion images, using a quasi-analytical algorithm for CDOM (QAA-CDOM). Uncertainties are classified to five levels, in which the underwater measurement uncertainty (level 1), image preprocessing uncertainty (level 4) and inverse model uncertainty (level 5) were evaluated. Results indicate that at level 1, in situ CDOM measurement is significant with 0.1 in the unit of QSU and 0.01 in the unit of a_g(440) (m⁻¹). At level 4, surface wave is a potential uncertainty source for high-resolution images in estuarine and coastal regions. The remote sensing reflectance of wavy water is about 10 times of the truth. At level 5, the overall uncertainty of QAA-CDOM inversion is 0.006 m⁻¹, with accuracy R² = 0.77, k = 1.1 and RMSE_log = 0.33 m⁻¹. The correlations between uncertainties and other water properties indicate that the large uncertainty in some rivers, such as the Neponset and Atchafalaya, might be caused by high-concentration chlorophyll or sediments. The relationships among the three level uncertainties show that the level 1 uncertainty generally does not propagate into level 4 and 5, but the large uncertainty at level 4 usually introduce large uncertainty at level 5.     

Testing the discrimination and detection limits of WorldView-2 imagery on a challenging invasive plant target

Invasive plants pose significant threats to biodiversity and ecosystem function globally, leading to costly monitoring and management effort. While remote sensing promises cost-effective, robust and repeatable monitoring tools to support intervention, it has been largely restricted to airborne platforms that have higher spatial and spectral resolutions, but which lack the coverage and versatility of satellite-based platforms. This study tests the ability of the WorldView-2 (WV2) eight-band satellite sensor for detecting the invasive shrub mesquite (Prosopis spp.) in the north-west Pilbara region of Australia. Detectability was challenged by the target taxa being largely defoliated by a leaf-tying biological control agent (Gelechiidae: Evippe sp. #1) and the presence of other shrubs and trees. Variable importance in the projection (VIP) scores identified bands offering greatest capacity for discrimination were those covering the near-infrared, red, and red-edge wavelengths. Wavelengths between 400 nm and 630 nm (coastal blue, blue, green, yellow) were not useful for species level discrimination in this case. Classification accuracy was tested on three band sets (simulated standard multispectral, all bands, and bands with VIP scores ≥1). Overall accuracies were comparable amongst all band-sets (Kappa = 0.71–0.77). However, mesquite omission rates were unacceptably high (21.3%) when using all eight bands relative to the simulated standard multispectral band-set (9.5%) and the band-set informed by VIP scores (11.9%). An incremental cover evaluation on the latter identified most omissions to be for objects <16 m². Mesquite omissions reduced to 2.6% and overall accuracy significantly improved (Kappa = 0.88) when these objects were left out of the confusion matrix calculations. Very high mapping accuracy of objects >16 m² allows application for mapping mesquite shrubs and coalesced stands, the former not previously possible, even with 3 m resolution hyperspectral imagery. WV2 imagery offers excellent portability potential for detecting other species where spectral/spatial resolution or coverage has been an impediment. New generation satellite sensors are removing barriers previously preventing widespread adoption of remote sensing technologies in natural resource management.     

Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis—A case study in Tengchong,China

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using TIR data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. Based on radiometric calibration, atmospheric correction and emissivity calculation, a simple but efficient single channel algorithm with acceptable precision is applied to retrieve the land surface temperature (LST) of study area. The LST anomalous areas with temperature about 4–10 K higher than background area are discovered. Four geothermal areas are identified with the discussion of geothermal mechanism and the further analysis of regional geologic structure. The research reveals that the distribution of geothermal areas is consistent with the fault development in study area. Magmatism contributes abundant thermal source to study area and the faults provide thermal channels for heat transfer from interior earth to land surface and facilitate the present of geothermal anomalies. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect LST anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.     

Satellite mapping of Baltic Sea Secchi depth with multiple regression models

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea’s optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea’s special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model’s mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003–2012 come with this paper as Supplementary materials.     

Modelling the standing timber volume of Baden-Württemberg—A large-scale approach using a fusion of Landsat,airborne LiDAR and National Forest Inventory data

Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r² = 0.56, NRMSE = 9.65%), the approach with balanced training data (r² = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r² = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy.     

Using remote sensing to monitor the influence of river discharge on watershed outlets and adjacent coral Reefs: Magdalena River and Rosario Islands,Colombia

Worldwide, coral reef ecosystems are being increasingly threatened by sediments loads from river discharges, which in turn are influenced by changing rainfall patterns due to climate change and by growing human activity in their watersheds. In this case study, we explored the applicability of using remote sensing (RS) technology to estimate and monitor the relationship between water quality at the coral reefs around the Rosario Islands, in the Caribbean Sea, and the rainfall patterns in the Magdalena River watershed. From the Moderate Resolution Imaging Spectroradiometer (MODIS), this study used the water surface reflectance product (MOD09GQ) to estimate water surface reflectance as a proxy for sediment concentration and the land cover product (MCD12Q1 V51) to characterize land cover of the watershed. Rainfall was estimated by using the 3B43 V7 product from the Tropical Rainforest Measuring Mission (TRMM). For the first trimester of each year, we investigated the inter-annual temporal variation in water surface reflectance at the Rosario Islands and at the three main mouths of the Magdalena River watershed. No increasing or decreasing trends of water surface reflectance were detected for any of the sites for the study period 2001–2014 (p > 0.05) but significant correlations were detected among the trends of each site at the watershed mouths (r = 0.57–0.90, p < 0.05) and between them and the inter-annual variation in rainfall on the watershed (r = 0.63–0.67, p < 0.05). Those trimesters with above-normal water surface reflectance at the mouths and above-normal rainfall at the watershed coincided with La Niña conditions while the opposite was the case during El Niño conditions. Although, a preliminary analysis of inter-annual land cover trends found only cropland cover in the watershed to be significantly correlated with water surface reflectance at two of the watershed mouths (r = 0.58 and 0.63, p < 0.05), the validation analysis draw only a 40.7% of accuracy in this land cover classification. This requires further analysis to confirm the impact of the cropland on the water quality at the watershed outlets. Spatial analysis with MOD09GQ imagery detected the overpass of river plumes from Barbacoas Bay over the Rosario Islands waters.     

An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso,Brazilian Amazon

Forest cover disturbances due to processes such as logging and forest fires are a widespread issue especially in the tropics, and have heavily affected forest biomass and functioning in the Brazilian Amazon in the past decades. Satellite remote sensing has played a key role for assessing logging activities in this region; however, there are still remaining challenges regarding the quantification and monitoring of these processes affecting forested lands. In this study, we propose a new method for monitoring areas affected by selective logging in one of the hotspots of Mato Grosso state in the Brazilian Amazon, based on a combination of object-based and pixel-based classification approaches applied on remote sensing data. Logging intensity and changes over time are assessed within grid cells of 300 m × 300 m spatial resolution. Our method encompassed three main steps: (1) mapping forest/non-forest areas through an object-based classification approach applied to a temporal series of Landsat images during the period 2000–2015, (2) mapping yearly logging activities from soil fraction images on the same Landsat data series, and (3) integrating information from previous steps within a regular grid-cell of 300 m × 300 m in order to monitor disturbance intensities over this 15-years period. The overall accuracy of the baseline forest/non-forest mask (year 2000) and of the undisturbed vs disturbed forest (for selected years) were 93% and 84% respectively. Our results indicate that annual forest disturbance rates, mainly due to logging activities, were higher than annual deforestation rates during the whole period of study. The deforested areas correspond to circa 25% of the areas affected by forest disturbances. Deforestation rates were highest from 2001 to 2005 and then decreased considerably after 2006. In contrast, the annual forest disturbance rates show high temporal variability with a slow decrease over the 15-year period, resulting in a significant increase of the ratio between disturbed and deforested areas. Although the majority of the areas, which have been affected by selective logging during the period 2000–2014, were not deforested by 2015, more than 70% of the deforested areas in 2015 had been at least once identified as disturbed forest during that period.     

Investigating the correlation between radar backscatter and in situ soil property measurements

Utilizing remote sensing techniques to extract soil properties can facilitate several engineering applications for large-scale monitoring and modeling purposes such as earthen levees monitoring, landslide mapping, and off-road mobility modeling. This study presents results of statistical analyses to investigate potential correlations between multiple polarization radar backscatter and various physical soil properties. The study was conducted on an approximately 3 km long section of earthen levees along the lower Mississippi river as part of the development of remote levee monitoring methods. Polarimetric synthetic aperture radar imagery from UAVSAR was used along with an extensive set of in situ soil properties. The following properties were analyzed from the top 30–50 cm of soil: texture (sand and clay fraction), penetration resistance (sleeve friction and cone tip resistance), saturated hydraulic conductivity, field capacity, permanent wilting point, and porosity. The results showed some correlation between the cross-polarized (HV) radar backscatter coefficients and most of these properties. A few soil properties, like clay fraction, showed similar but weaker correlations with the co-polarized channels (HH and VV). The correlations between the soil properties and radar backscatter were analyzed separately for the river side and land side of the levee. It was found that the magnitude and direction of the correlation for most of the soil properties noticeably differed between the river and the land sides. The findings of this study can be a good starting point for scattering modelers in a pursuit of better models for radar scattering at cross polarizations which would include more diverse set of soil parameters.