Applying the remotely sensed data to identify homogeneous regions of watersheds using a pixel-based classification approach

The identification of ecologically homogeneous zones is crucial and represents one of the key steps in ecosystem planning, management, and restoration. It allows targeted ecological restoration efforts based on the interactions between environmental factors. The approach of this study is pixel-based to identify ecologically homogeneous zones within the green oak forest in Elhassana area, western Algeria. This approach uses remote sensing indicators such as the normalized difference vegetation index (NDVI), leaf area index (LAI), soil-adjusted vegetation index (SAVI), normalized difference moisture index (NDMI), elevation, slope, aspect, relative slope position (RSP), and topographic wetness index (TWI), derived from Landsat 8 and Terra/ASTER satellite imagery to identify homogeneous zones. We use principal component analysis (PCA) to reduce the dimensionality of the data and identify the most important variables. This analysis helps to better understand the structure of the data and determine which variables have the most influence on the unsupervised classification using the iso cluster algorithm. The results of this study allowed us to visualize and map four types of homogeneous zones and characterize their ecological attributes. This information is invaluable for forest planners, enabling sustainable environmental management and the development of an ecological restoration plan for the green oak forest.

Spatial distribution dependency of soil organic carbon content to important environmental variables

Rice is the primary source of nutrition food of more than half of the world’s population, and it is hugely important in the global economic growth, food security, water use, and climate change. The need for satellite systems to monitor rice crops and assist in rice crop management is gaining in popularity. The European Space Agency’s (ESA) launched Sentinel-2 A + B twin platform’s which enhanced the temporal, spatial, and spectral resolution, opening the way for their widely use in crop monitoring. Aside from the technical features of the Sentinel-2 A and B constellation, the easily accessible type of information they generate as well as the appropriate support software have been significant improvements for rice crop monitoring. In this study, the spectral reflectance has been analysed to find how far their potential in determining rice growth phases. The highest spectrum in reflectance was observed in the near infrared (NIR) region (842 nm). Because of the structure of mesophyll cells tissues and the inner backscatter of air spaces, moisture content, and air–water abstraction layers within the leaves, the reflectance in the NIR region seems to be much larger than in the visible band. The multi-temporal vegetation index namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) have derived from ten Sentinel-2 images cover the entire rice season. These indices have been tested to determine the rice growth phases over the rice season. The spatial distribution of each tested indices is displayed in the map output. The maps are then analysed and compared to determine the potential of each index in determining rice growth phases. It was discovered in this study that there was a quadratic correlation between all of the tested indices and rice age. The Normalized Difference Vegetation Index (NDVI) is the most accurate vegetation index for estimating rice growth phases, followed by SAVI and NDMI.

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

Soil organic matter (SOM) is a key soil property used to predict the impacts of land-use changes as well as to indicate soil health status. The vegetative indices (VI) derived from remote-sensing data, such as soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) are important indices reflecting crop growth and biomass. They can be related to soil organic carbon changes in large-scale environments. However, there is little information in the literature about the relationship between VI and soil carbon changes over the transition of different farming practices from degraded grassland to an agroforestry system. This study aimed to determine the relationship between soil C stocks and VI over the transition of degraded grassland (DGL) to agroforestry (AgrfS) in the Brazilian Cerrado. Soil organic stocks, NDVI, SAVI and NDMI were measured from 2011 to 2015, when the area passed from a low-productivity grassland, followed by a crop-pasture intercropping system and an agroforestry system. The transitional phases from degraded grassland to agroforestry system promoted surprisingly large gains in soil carbon stocks, ranging from 41.7 Mg C ha−1 in DGL to 68.4 Mg C ha−1 in AgrfS. Similar to soil carbon stocks, the mean VI values increased over the years from DGL to AgrfS, indicating the importance of vegetation indices to predict soil carbon stocks changes over the restoration of degraded grasslands.

Can palm date plantations and oasification be used as a proxy to fight sustainably against desertification and sand encroachment in hot drylands?

Leaf area index (LAI) has been associated with vegetation productivity and evapotranspiration in mathematical models. At a regional level LAI can be estimated with enough accuracy through spectral vegetation indices (SVIs), derived from remote sensing imagery. However, there are few studies showing LAI–SVI relationships in subtropical regions. The aim of this work was to examine the relationship between LAI and SVIs in a subtropical rural watershed (in Piracicaba, State of Sa˜o Paulo, Brazil), for different land covers, and to use the best relationship to generate a LAI map for the watershed. LAI was measured with a LAI-2000 instrument in 32 plots on the field in areas of sugar cane, pasture, corn, eucalypt, and riparian forest. The SVIs studied were Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI), calculated from Landsat-7 ETM+ data. The results showed LAI values ranging from 0.47 to 4.48. LAI–SVI relationships were similar for all vegetation types, and the potential model gave the best fit. It was observed that LAI–NDVI correlation (r 2=0.72) was not statistically different from LAI–SR correlation (r 2=0.70). The worst correlation was obtained by LAI–SAVI (r 2=0.56). A map was generated for the study area using the LAI–NDVI relationship. This was the first LAI map for the region.

Time series of Leaf Area Index (LAI) is of utmost importance for various disciplines of bio- and geosciences where satellite remote sensing makes LAI estimation possible for large areas. Remote sensing LAI, validated against in situ LAI (LAIinsitu), is used as base for calculating LAI for large areas e.g. on catchment scale. Various vegetation indices (NDVI, SAVI, both with and without substituting the red band with red-edge band) were applied for better estimates of LAIrapideye. SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) present same correlation between remote sensing based predicted LAI (LAIrapideye) against LAIinsitu in winter wheat fields. Both NDVI and SAVI with red-edge band showed improved correlation of remote sensing based VI and in situ measurements. Prior to vegetation indices calculation, radiometric normalization was applied to the time series of RapidEye data. To test the impact of radiometric normalization for calculating vegetation indices on a time series of satellite images, pre- and post-radiometric normalization LAIrapideye was compared. More precise and high resolution estimation of LAI for large areas is of vital importance for improving evapotranspiration and soil moisture.

Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India

Remote sensing of urban heat islands(UHIs) has traditionally used the Normalized Difference Vegetation Index(NDVI) as the indicator of vegetation abundance to estimate the relationship land surface temperature(LST) and vegetation.This study investigates the applicability of Normalized Difference Moisture Index(NDMI) as an alternative indicator.This paper compares the Normalized Difference Vegetation Index(NDVI) and Normalized Difference Moisture Index(NDMI) as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the Land Surface Temperature(LST),NDMI,and the NDVI.Landsat Thematic Mapper(TM) and Enhanced Thematic Mapper Plus(ETM+) data were used to estimate the LST through the single window algorithm from three different months for the Zhujiang Delta area.Maps of NDVI and NDMI for three different data were generated from band 3,band 4 and band 5 of TM/ETM+ imageries,respectively.The relationships between the LST,NDMI,and the NDVI were analyzed supported by Geographic Information System(GIS).Our analysis indicates that there is stronger linear relationship between LST and NDMI for all three months,whereas the relationship between LST and NDVI is much weaker and varies by different months.With the change of seasons from summer to autumn,the linear correlation relationship between LST and NDMI was gradually lowered.This result suggests that NDMI provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the three months for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

PDF HTML阅读 XML下载 导出引用 引用提醒 山区夏季地表温度的影响因素——以泰山为例 DOI: 10.5846/stxb201310312626 作者: 作者单位: 南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学建筑与城市规划学院,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学建筑与城市规划学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目（31170444）；中央高校基本科研业务费专项资助 Analysis of factors affecting mountainous land surface temperature in the summer：a case study over Mount Tai Author: Affiliation: Nanjing University,International Institute for Earth System Sciences,,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以泰山为例，应用夏季的Landsat 5的TM6为基本数据源，基于单窗算法定量反演了泰山地表面温度（LST），在此基础上首先探讨了LST与地形因子的关系，然后比较了归一化水汽指数（NDMI）和归一化植被指数（NDVI）在表达山区LST上的效力，最后利用逐步回归分析法，构建出LST与地形因子、NDMI的回归方程，应用偏相关系数，得出各个因子对LST的影响程度。结果表明：1）在地形因子中，影响LST的主要因素是海拔，随海拔升高呈自然对数形式降低，相比而言，坡度、坡向以及太阳入射能量的影响则很小；2）在没有水体时，NDVI与NDMI都能有效地表达山区的LST，LST与NDVI间是二次项负相关关系，与NDMI间是线性负相关关系，在表达LST上NDMI比NDVI更有效；3）综合分析表明，地表水汽特征是其表面温度最主要的影响因素，其次是海拔。研究结果将为山区地表温度空间分异性特征及形成机制的研究提供科学的参考。 Abstract:Most of the previous studies related to land surface temperature (LST) are mainly focused on the investigation of urban heat island; however, little has been done on the mountainous area that are usually far away from cities. In this study, using the Landsat 5 Thematic Mapper (TM) at Mount Tai, firstly, the LST was retrieved based on the Mono-window Algorithm; and then the impacts on the LST from several factors including the topography, normalized difference moisture index (NDMI) and normalized difference vegetation index (NDVI) were analyzed though correlation analysis; accordingly, the regression equation between LST and topographic factors as well as NDMI was developed by stepwise regression analysis, the variable coefficients in the regression equation were interpreted using nonstandard regression coefficient, and then the impact of each factor on LST was quantized by partial correlation coefficient. The results show that: 1) In summer, elevation significantly affects the LST and has a significantly negative natural logarithm correlation rather than a negative linear correlation with it. However, the influence of slope, aspect and incident solar energy is not very significant, LST has a weak correlation with each of them; 2) NDVI and NDMI effectively express LST in mountainous areas if water is absent on surface. LST and NDVI have a negative quadratic correlation. In addition, with the increase of NDVI, the LSTs over areas covered by dense vegetation (NDVI > 0.5) will appear a "saturation" phenomenon. Meanwhile, LST and NDMI have a simple but stable negative linear correlation. When compared with NDVI, NDMI is more effective and more applicable at a large scale for the expression of LST;3) The comprehensive analysis shows that land surface moisture characteristic is the main factor affecting the LST, and then followed by the elevation. In comparison with the impacts of these two primary factors, those from the other factors are relatively insignificant. These results will provide important information on the examination of the spatial pattern and mechanism of LST across mountainous areas. 参考文献 相似文献 引证文献

The Czech Republic has experienced collectivization, transforming individual land ownership into state ownership through agricultural land consolidation after World War II. Land consolidation has led to the expansion of agricultural soil blocks as well as the disappearance of vital landscape features such as shrubs, hedges, lone trees, and wetlands, all of which serve a variety of ecological functions. The disappearance of the landscape elements causes some important problems, such as soil degradation and erosion. The Czech Ministry of Agriculture has enforced a regulation that was developed by the European Union to improve agriculture by enforcing cross-compliance requirements, which encompass the observance of Good Agricultural and Environmental Condition (GAEC) standards. The regulation disincentivizes single-crop cultivation on areas larger than 30 hectares since 2020 to reduce the negative effects of land consolidations on agriculture and soil quality. In this context, the study examines the relationship between land surface temperature (LST) and moisture of agricultural plants and soil blocks in the South Bohemia, Czech Republic, using Landsat-8 remote sensing data from 2018 to 2021. The mono-window algorithm (MWA) was used to obtain LST, and the precision of Landsat LST data was evaluated using MODIS daily LST data. The NRMSE ≤ 0.15 and RMSE ≤ 3.5 C were found with a strong positive relationship r ≥ 0.65 between datasets. Additionally, the density of vegetation and the moisture index of soil and plants were calculated using spectral remote sensing indices such as the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and soil moisture index (SMI). The statistical relationships among those spectral indices with LST were examined. The analyzes showed that the implementation of GAEC 7d improved the relationship between NDVI and NDMI, with the correlation increasing from r = 0.80 in 2019 to 0.92 in 2021. Meanwhile, the inverse relationship between NDMI and LST changed from − 0.71 to -0.67. These changes in correlation suggest that plant water retention in the region increased following the regulation, and the soil structure may have also improved.

Vegetation cover is indispensable in the process of inhibiting environmental degradation. In the Northeast of Brazil, especially in the Araripe Nacional Forest (FLONA), this problem is related to the removal of vegetation for industrial and domestic use, in addition to the expansion of livestock. Thus, the objective of this work was to evaluate the environmental degradation in the area of FLONA from orbital products via remote sensing with the aid of mathematical modeling. For this, two orbital images of the orbit 65, point 217 were used for processing and obtaining the variables: (1st) July 7, 2003 from TM/Landsat-5 and (2nd) July 15, 2018 from OLI/Landsat-8. In mathematical modeling, the multiple linear regression (MRL) model was applied to the orbital products: land surface temperature (LST), normalized burn ratio (NBR), Normalized Difference Moisture Index, Normalized Difference Water Index (NDWI) to estimate the Soil Adjusted Vegetation Index (SAVI) and hence to predict the Normalized Difference Vegetation Index (NDVI). All the processing to obtain the results was carried out in the software R version 3.4-1. O NDVI pointed out a significant increase of 72.05% in dense vegetation, from 158.33 to 272.40 km2. However, vegetation is more likely to suffer from stress due to the increase in LST at 5 °C, which increased from 17.5 to 25.0 °C, reaching its highest value of 42 °C in July 2011. The MRL results indicated that the models have an excellent predictive capacity in the estimation of degradation, with R2 value greater than 92% of the explained variance. In addition, the MAE and root mean square error were less than 0.03 for both models. The models pointed out that SAVI, NBR and NDWI are responsible for the variability of NDVI in environmental degradation of FLONA. Highlight for the theoretical-conceptual model that can be applied to any semi-arid and highly-sensitive region to changes in the rainfall pattern.