Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc Mountains of Tanzania

Estimating tropical biomass is critical for establishment of conservation inventories and landscape monitoring. However, monitoring biomass in a complex and dynamic environment using traditional methods is challenging. Recently, biomass estimates based on remotely sensed data and ecological variables have shown great potential. The present study explored the utility of remotely sensed data and topo-edaphic factors to improve biomass estimation in the Eastern Arc Mountains of Tanzania. Twenty-nine vegetation indices were calculated from RapidEye data, while topo-edaphic factors were taken from field measurements. Results showed that using topo-edaphic variables or vegetation indices, biomass could be predicted with an R² of 0.4. A combination of topo-edaphic variables and vegetation indices improved the prediction accuracy to an R² of 0.6. Results further showed a decrease in biomass estimates from 1162 ton ha^?1 in 1980 to 285.38 ton ha^?1 in 2012. This study demonstrates the value of combining remotely sensed data with topo-edaphic variables in biomass estimation.     

Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.     

Detecting forest damage after a low-severity fire using remote sensing at multiple scales

Remote sensing technologies are an ideal platform to examine the extent and impact of fire on the landscape. In this study we assess that capacity of the RapidEye constellation and Landsat (Thematic Mapper and Operational Land Imager to map fine-scale burn attributes for a small, low severity prescribed fire in a dry Western Canadian forest. Estimates of burn severity from field data were collated into a simple burn index and correlated with a selected suite of common spectral vegetation indices. Burn severity classes were then derived to map fire impacts and estimate consumed woody surface fuels (diameter ≥2.6 cm). All correlations between the simple burn index and vegetation indices produced significant results (p < 0.01), but varied substantially in their overall accuracy. Although the Landsat Soil Adjusted Vegetation Index provided the best regression fit (R² = 0.56), results suggested that RapidEye provided much more spatially detailed estimates of tree damage (Soil Adjusted Vegetation Index, R² = 0.51). Consumption estimates of woody surface fuels ranged from 3.38 ± 1.03 Mg ha⁻¹ to 11.73 ± 1.84 Mg ha⁻¹, across four derived severity classes with uncertainties likely a result of changing foliage moisture between the before and after fire images. While not containing spectral information in the short wave infrared, the spatial variability provided by the RapidEye imagery has potential for mapping and monitoring fine scale forest attributes, as well as the potential to resolve fire damage at the individual tree level.     

现代遥感技术在土地执法监察工作中的应用

描述了现代遥感技术在国土资源系统执法监察工作中的应用,对使用的工艺流程、技术路线进行了详细描述和分析。     

Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope

Airborne Lidar bathymetry (ALB) is an effective and a rapidly advancing technology for mapping and characterizing shallow coastal water zones as well as inland fresh-water basins such as rivers and lakes. The ability of light beams to detect and traverse shallow water columns has provided valuable information about unmapped and often poorly understood coastal and inland water bodies of the world.Estimating ALB survey results at varying water clarity and depth conditions is essential for realizing project expectations and preparing budgets accordingly. In remote locations of the world where in situ water clarity measurements are not feasible or possible, using multiband satellite imagery can be an effective tool for estimating and addressing such considerations.For this purpose, we studied and classified reflected electromagnetic energy from selected water bodies acquired by RapidEye sensor and then correlated findings with ALB survey results. This study was focused not on accurately measuring depth from optical bathymetry but rather on using multiband satellite imagery to quickly predict ALB survey results and identify potentially turbid water bodies with limited depth penetration. For this study, we constructed an in-house algorithm to confirm ALB survey findings using bathymetric waveform information.The study findings are expected to contribute to the ongoing understanding of forecasting ALB survey expectations in unknown and varying water conditions, especially in remote and inaccessible parts of the world.     

Integrating volunteered smartphone data with multispectral remote sensing to estimate forest fuels

Volunteered data sources are readily available due to advances in electronic communications technology. For example, smartphones provide tools to collect ground-based observations over broad areas from a diverse set of data collectors, including people with, and without, extensive training. In this study, volunteers used a smartphone application to collect ground-based observations. Forest structural components were then estimated over a broader area using high spatial resolution RapidEye remote sensing imagery (5 spectral bands 440–850 nm, 5 m spatial resolution) and a digital elevation model following a three nearest neighbor approach (K-NN). Participants with professional forestry experience on average chose high-priority fuel load locations near buildings, while nonprofessional participants chose a broader range of conditions over a larger extent. When used together, the professional and nonprofessional observations provided a more complete assessment of forest conditions. A generalized framework is presented that utilizes K-NN imputation tools for estimating the distribution of forest fuels using remote sensing and topography variables, ensuring spatial representation, checking attribute accuracy, and evaluating predictor variables. Frameworks to integrate volunteered data from smartphone platforms with remote sensing may contribute toward more complete Earth observation for Digital Earth.     

Analysis of channel transmission losses in a dryland river reach in north‐eastern Brazil using streamflow series,groundwater level series and multi‐temporal satellite data

Scarcity of hydrological data, especially streamflow discharge and groundwater level series, restricts the understanding of channel transmission losses (TL) in drylands. Furthermore, the lack of information on spatial river dynamics encompasses high uncertainty on TL analysis in large rivers. The objective of this study was to combine the information from streamflow and groundwater level series with multi‐temporal satellite data to derive a hydrological concept of TL for a reach of the Middle Jaguaribe River (MJR) in semi‐arid north‐eastern Brazil. Based on this analysis, we proposed strategies for its modelling and simulation. TL take place in an alluvium, where river and groundwater can be considered to be hydraulically connected. Most losses certainly infiltrated only through streambed and levees and not through the flood plains, as could be shown by satellite image analysis. TL events whose input river flows were smaller than a threshold did not reach the outlet of the MJR. TL events whose input flows were higher than this threshold reached the outlet losing on average 30% of their input. During the dry seasons (DS) and at the beginning of rainy seasons (DS/BRS), no river flow is expected for pre‐events, and events have vertical infiltration into the alluvium. At the middle and the end of the rainy seasons (MRS/ERS), river flow sustained by base flow occurs before/after events, and lateral infiltration into the alluvium plays a major role. Thus, the MJR shifts from being a losing river at DS/BRS to become a losing/gaining (mostly losing) river at MRS/ERS. A model of this system has to include the coupling of river and groundwater flow processes linked by a leakage approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m²/m². The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r² = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling.     

Using width‐based rating curves from spatially discontinuous satellite imagery to monitor river discharge

Remote estimation of river discharge from river width variations is an intriguing method for gauging rivers without conventional measurements. Entirely cloud‐free imagery of an entire river reach is often rare, but partial coverage is more frequent. Discharge is estimated from spatially discontinuous imagery via construction of multiple width–discharge rating curves within a 62‐km reach of the Tanana River, Alaska. The resulting discharge error is as low as 6.7% root mean squared error. Imagery covering <20% of the study reach can be used. Copyright © 2014 John Wiley & Sons, Ltd.     

Investigation of terrain illumination effects on vegetation indices and VI-derived phenological metrics in subtropical deciduous forests

We used RapidEye and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra data to study terrain illumination effects on 3 vegetation indices (VIs) and 11 phenological metrics over seasonal deciduous forests in southern Brazil. We applied TIMESAT for the analysis of the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) derived from the MOD13Q1 product to calculate phenological metrics. We related the VIs with the cosine of the incidence angle i (Cos i) and inspected percentage changes in VIs before and after topographic C-correction. The results showed that the EVI was more sensitive to seasonal changes in canopy biophysical attributes than the NDVI and Red-Edge NDVI, as indicated by analysis of non-topographically corrected RapidEye images from the summer and winter. On the other hand, the EVI was more sensitive to terrain illumination, presenting higher correlation coefficients with Cos i that decreased with reduction in the canopy background L factor. After C-correction, the RapidEye Red-Edge NDVI, NDVI, and EVI decreased 2%, 1%, and 13% over sunlit surfaces and increased up to 5%, 14%, and 89% over shaded surfaces, respectively. The EVI-related phenological metrics were also much more affected by topographic effects than the NDVI-derived metrics. From the set of 11 metrics, the 2 that described the period of lower photosynthetic activity and seasonal VI amplitude presented the largest correlation coefficients with Cos i. The results showed that terrain illumination is a factor of spectral variability in the seasonal analysis of phenological metrics, especially for VIs that are not spectrally normalized.