Relative suitability of indices derived from Landsat ETM+ and SPOT 5 for detecting fire severity in sagebrush steppe

Remote sensing indices of burn area and fire severity have been developed and tested for forest ecosystems, but not sparsely vegetated, desert shrub-steppe in which large wildfires are a common occurrence and a major issue for land management. We compared the performance of remote sensing indices for detecting burn area and fire severity with extensive ground-based cover assessments made before and after the prescribed burning of a 3 km² shrub-steppe area. The remote sensing indices were based on either Landsat 7 ETM+ or SPOT 5 data, using either single or multiple dates of imagery. The indices delineating burned versus unburned areas had better overall, User, and Producer's accuracies than indices delineating levels of fire severity. The Soil Adjusted Vegetation Index (SAVI) calculated from SPOT had the greatest overall accuracy (100%) in delineating burned versus unburned areas. The relative differenced Normalized Burn Ratio (RdNBR) using Landsat ETM+ provided the highest accuracies (73% overall accuracy) for delineating fire severity. Though SPOT's spatial resolution likely conferred advantages for determining burn boundaries, the higher spectral resolution (particularly band 7, 2.21 μm) of Landsat ETM+ may be necessary for detecting differences in fire severity in sparsely vegetated shrub-steppe.     

Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery

A forest fire started on August 8th, 2016 in several places on Madeira Island causing damage and casualties. As of August 10th the local media had reported the death of three people, over 200 people injured, over 950 habitants evacuated, and 50 houses damaged. This study presents the preliminary results of the assessment of several spectral indices to evaluate the burn severity of Madeira fires during August 2016. These spectral indices were calculated using the new European satellite Sentinel-2A launched in June 2015. The study confirmed the advantages of several spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVIreXn) using red-edge spectral bands to assess the post-fire conditions. Results showed high correlation between NDVI, GNDVI, NBR and NDVIre1n spectral indices and the analysis performed by Copernicus Emergency Management Service (EMSR175), considered as the reference truth. Regarding the red-edge spectral indices, the NDVIre1n (using band B5, 705 nm) presented better results compared with B6 (740 nm) and B7 (783 nm) bands. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for post-fire monitoring. In the future, the two twin Sentinel-2 satellites will offer global coverage of the Madeira Archipelago every five days, therefore allowing the simultaneous study of the evolution of the burnt area and reforestation information with high spatial (up to 10 m) and temporal resolution (5 days).     

Modeling of multi-strata forest fire severity using Landsat TM Data

Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and ecological responses at different forest layers. In this study, using field measured fire severity across five forest strata of dominant tree, intermediate-sized tree, shrub, herb, substrate layers, and the aggregated measure of CBI as response variables, we fit statistical models with predictors of Landsat TM bands, Landsat derived NBR or dNBR, image differencing, and image ratioing data. We model multi-strata forest fire in the historical recorded largest wildfire in California, the Big Sur Basin Complex fire. We explore the potential contributions of the post-fire Landsat bands, image differencing, image ratioing to fire severity modeling and compare with the widely used NBR and dNBR. Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing, and image ratioing. We predict and map multi-strata forest fire severity across the whole Big Sur fire areas, and find that the overall measure CBI is not optimal to represent multi-strata forest fire severity.     

运用遥感指数的崩滑检测方法

以汶川地震时出现的崩滑为研究对象,以该地区震前/后TM影像为研究数据,利用变化检测的方法,比较和分析了归一化植被指数(NDVI)、归一化建筑指数(NDBI)和归一化燃烧率(NBR)应用在崩滑检测中的可行性和优劣性。结果表明,3种遥感指数均可以用于崩滑检测,但效果不同:NDVI对崩滑检测最全面但可信度最低,NBR对崩滑检测最可信但遗漏最多,NDBI处于中间水平。     

联合多源遥感数据监测四川木里县森林火灾

森林火灾既严重影响森林生态系统的稳定,还威胁到人类生命财产安全。传统监测森林火灾方法,覆盖范围小,难以及时监测小面积火灾。遥感卫星能大范围精确监测火情,提高了监测方法的时效性,但使用单一卫星数据源很容易受到云雨等客观环境因素影响,降低监测的时效性。本文以四川木里藏族自治县"330森林火灾"区域为对象,开展多源卫星遥感数据对小范围火灾联合监测的研究。首先,充分挖掘高分四号高时空分辨率和中红外火烧敏感波段优势,联合烟幕、温度和植被指数时序变化确定火烧时间与位置;然后,使用Sentinel-2数据监测不同火烧区域光谱信息;接着,使用Sentinel-2数据提取dNBR(differenced Normalized Burn Ratio),提出了基于最大类间方差算法(OTSU)分步骤确定不同程度火烧迹地与面积的方法;最后,建立Sentinel-1A极化比值PR (Polarization Ratio)和NDVI之间关系,利用微波雷达突破云雨限制。结果表明:(1)高分四号联合IRS(InfraRed Scanner)和PMS(Panchromatic Multispectral Sensor)能够实时监测小范围火灾;(2)根据火点位置,确定火灾蔓延期间NDVI下降(由0.7降低至0.25),确定起火时间(3月30日);(3)火灾区域与未受灾区,以及不同类型火烧迹地之间的光谱在490—2200 nm范围存在差异;(4)基于OTSU算法自动确定阈值,确定林地损失面积41.56公顷(dNBR=0.35),精度达94.67%,提取林地过火未损失面积66.56公顷(dNBR=0.10),精度达90.94%,林地损失区域基本符合实际调查结果;(5)火灾前后极化比值由6.6 dB升高至10.8 dB,NDVI与PR经线性回归,R2=0.58,验证R2=0.50。联合多源卫星监测森林火灾,能提高森林火灾监测的时效性,避免了云雨等复杂环境的影响。研究成果能为小火点的及时识别和灾害评估提供参考,其应用可为林火应急响应提供技术支撑。     

SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity

Fires are a problematic and recurrent issue in Mediterranean ecosystems. Accurate discrimination between burn severity levels is essential for the rehabilitation planning of burned areas. Sentinel-2A MultiSpectral Instrument (MSI) record data in three red-edge wavelengths, spectral domain especially useful on agriculture and vegetation applications. Our objective is to find out whether Sentinel-2A MSI red-edge wavelengths are suitable for burn severity discrimination. As study area, we used the 2015 Sierra Gata wildfire (Spain) that burned approximately 80 km². A Copernicus Emergency Management Service (EMS)-grading map with four burn severity levels was considered as reference truth. Cox and Snell, Nagelkerke and McFadde pseudo-R² statistics obtained by Multinomial Logistic Regression showed the superiority of red-edge spectral indices (particularly, Modified Simple Ratio Red-edge, Chlorophyll Index Red-edge, Normalized Difference Vegetation Index Red-edge) over conventional spectral indices. Fisher's Least Significant Difference test confirmed that Sentinel-2A MSI red-edge spectral indices are adequate to discriminate four burn severity levels.     

An updated delineation of stand ages of deciduous rubber plantations during 1987-2018 using Landsat-derived bi-temporal thresholds method in an anti-chronological strategy

Timely and accurately monitoring stand ages of deciduous rubber plantations is of great importance for ecological studies and plantations management. The re-establishment of rubber plantations usually experiences a short period (several years) of land clearance and transplantation of rubber seedlings, along with a noticeable landscape change from well-grown forest to bare land and sparse vegetation in situ. With Landsat times series (LTS) data of four commonly-used vegetation indices (VIs), namely the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Normalized Burn Ratio (NBR), and three non-visible spectral bands, i.e. the near-infrared (NIR) and shortwave-infrared (SWIR1/2), in this study, an approach by combining the inter-annual defoliating and foliating features of rubber trees and the intra-annual landscape changes of rubber plantations was presented to detect and map stand ages of deciduous rubber plantations in an anti-chronological manner across Xishuangbanna between 1987 and 2018, one of the most intensive regions of deciduous rubber plantations within the tropics. The approach highlighted the repeated distribution of newly-cleared and replanted plot (NCRP) of rubber seedlings due to rubber management. It applied the bi-temporal VIs thresholds of zero of NBR and NDMI during the defoliation to foliation phases to delineate the stand ages of deciduous rubber plantations at an interval of five years, by combining a Landsat-based rubber map in 2018 and 32-year NCRPs as well as quadri-classified age-groups and seven sub-categories (i.e. ≤5 as infantile rubber plantations (IRP), 6–10 as young rubber plantations (YRP), 11–15 and 16–20 as mature rubber plantations (MRP), 21–25, 26–30, and ≥31 years as old rubber plantations (ORP)). The results showed that the areas of IRP, YRP, MRP, and ORP were 19.1 km², 817.1 km², 1681.7 km², and 573.7 km² in 2018, respectively. Spatially, the YRP are mainly around the outskirts of two county-level administrative centers (Jinghong and Mengla), while ORP primarily distributed along main roads. Nearly 53.9% of ORP, 51.8% of IRP, 47.3% of MRP and 46.3% of YRP were in Jinghong City, and Mengla County had 50.5% of YRP, 48.8% of MRP, 42.4% of IRP and 36.3% of ORP. This study demonstrates that the bi-temporal VIs thresholds method (i.e. NBR_defoliation <0, NDMI_defoliation <0, NBR_foliation <0, and NDMI_foliation <0) have great potential for detecting stand ages of deciduous rubber plantations.     

Evaluating uncertainty in Landsat-derived postfire recovery metrics due to terrain,soil, and shrub type variations in southern California

ABSTRACT

Temporal trajectories of apparent vegetation abundance based on the multi-decadal Landsat image series provide valuable information on the postfire recovery of chaparral shrublands, which tend to mature within one decade. Signals of change in fractional shrub cover (FSC) extracted from time-sequential Normalized Difference Vegetation Index (NDVI) data can be systematically biased due to spatial variation in shrub type, soil substrate, or illumination differences associated with topography. We evaluate the effects of these variables in Landsat-derived metrics of FSC and postfire recovery, based upon three chaparral sites in southern California which contain shrub community ecotones, complex terrain, and soil variations. Detailed validations of prefire and postfire FSC are based on high spatial resolution ortho-imagery; cross-stratified random sampling is used for variable control. We find that differences in the composition and structure of shrubs (inferred from ortho-imagery) can substantially influence FSC-NDVI relations and impact recovery metrics. Differences in soil type have a moderate effect on the FSC-NDVI relation in one of the study sites, while no substantial effects were observed due to variation of terrain illumination among the study sites. Arithmetic difference recovery metrics – based on NDVI values that were not normalized with unburned control plots – correlate in a moderate but significant manner with a change in FSC (R ² values range 0.47–0.59 at two sites). Similar regression coefficients resulted from using Landsat visible reflectance data alone. The lowest correlations to FSC resulted from Soil-Adjusted Vegetation Index (SAVI) and are attributed to the effects of the soil-adjustment factor in sparsely vegetated areas. The Normalized Burn Ratio and Normalized Burn Ratio 2 showed a moderate correlation to FSC. This study confirms the utility of Landsat NDVI data for postfire recovery evaluation and implies a need for stratified analysis of postfire recovery in some chaparral landscapes.     

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.     

Comparing Fire Severity Models from Post-Fire and Pre/Post-Fire Differenced Imagery

Wildland fires are common in rangelands worldwide. The potential for high-severity fires to affect long-term changes in rangelands is considerable, and for this reason assessing fire severity shortly after the fire is critical. Such assessments are typically carried out following Burned Area Emergency Response teams or similar protocols. These data can then be used by land managers to plan remediation and future land uses. To complement these procedures and explore fire severity modeling of sagebrush steppe rangelands, we compared models developed using (1) post-fire imagery only with (2) differenced imagery (pre-fire minus post-fire imagery). All models were developed from Classification Tree Analysis (CTA) techniques using Satellite Pour l'Observation de la Terre 5 (SPOT 5) imagery and Shuttle Radar Topography Mission (SRTM) elevation data. The results indicate that both techniques produced similar fire severity models (model agreement = 98.5%) and that little improvement in overall accuracy was gained by using differenced imagery (0.5%). We suggest the use of CTA models developed using only the post-fire imagery. The analyses and techniques described in this paper provide land managers with tools to better justify their recommendations and decisions following wildland fires in sagebrush steppe ecosystems and provide remote sensing scientists with information that will potentially improve future modeling efforts.     

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.     

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.     

Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated.The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST.     

Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery

Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km²). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r² = 0.64, r² = 0.71, respectively). Best results were achieved for LSU (r² = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r² = 0.39 for LSU, r² = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.     

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m², R² = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m², R² = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.     

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.     

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.     

Developing soil indices based on brightness,darkness, and greenness to improve land surface mapping accuracy

Soil, as one of the three basic biophysical components, has been understudied using remote sensing techniques compared to vegetation and impervious surface areas (ISA). This study characterized land surfaces based on the brightness–darkness–greenness model. These three dimensions, brightness, darkness, and greenness, were represented by the first Tasseled Cap Transformation (TC1), Normalize Difference Snow Index (NDSI), and Normalized Difference Vegetation Index (NDVI), respectively. The Ratio Index for Bright Soil (RIBS) was developed based on TC1 and NDSI, and the Product Index for Dark Soil (PIDS) was established by TC1 and NDVI. Their applications to the Landsat 8 Operational Land Imager images and 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in China revealed the efficiency. The two soil indices proficiently highlighted soil covers with consistently the smallest values, due to larger TC1 and smaller NDSI values in bright soil, and smaller NDVI and TC1 values in dark soil. The RIBS is capable of distinguishing bright soil from ISA without masking vegetation and water body. The spectral separability bright soil and ISA were perfect, with a Jeffries–Matusita distance of 1.916. And the PIDS was the only soil index that could discriminate dark soil from other land covers including ISA. The soil areas in China were classified using a simple threshold method based on MODIS images. An overall accuracy of 94.00% was obtained, with the kappa index of 0.8789. This study provided valuable insights into developing indices for characterizing land surfaces from different perspectives.     

Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor

Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R² of 0.67 and a RMSE-CV of 2.2 t ha^?1) when compared to when the red-edge band was included as a co-variate (R² of 0.73 and a RMSE-CV of 2.04 t ha^?1). Also, similar results were obseved when all WorldView-2 vegetation indices (without the red-edge computed ones), producing slightly low accuracies (R² of about 0.67 and a RMSE-CV of 2.20 t ha^?1), when compared to those obtained using all indices and RE-computed indices(R² of 0.76 and a RMSE-CV of 1.88 t ha^?1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint.