Changes in Soil Carbon Stocks and Vegetative Indices in the Transitional Phases from Degraded Grassland to an Agroforestry System in the Cerrado Region, Brazil

Globally, tropical deforestation is often followed by the establishment of fire-prone grassland. In Southeast Asia, Imperata cylindrica grassland is the dominant land cover after deforestation. No quantitative data are available on the changes in soil carbon (C) stock upon such land conversion. We aimed to elucidate changes in soil C stock after deforestation followed by the occurrence and persistence of I. cylindrica grassland in the Asian humid tropics. We compared soil C stock between primary forests (n = 20) and grasslands (n = 14) with a wide range of soil textures in East Kalimantan (Indonesian Borneo). We also assessed the temporal change in soil C stock in the grassland sites between 1992 and 2004 by comparing identical soil pits (n = 7). Soil C stock (0–100 cm deep) increased by 23% following transition from primary forest to grassland during about 10 years. Over 12 years at the grassland sites, however, soil C stock did not change in the 0–100 cm depth, but we observed increased blackness of soil, especially coarse-textured soils. The increase in C stock following the transition was largely attributed to the organic matter supply by grass roots, rhizomes, and charred materials from wildfires to the subsurface soils and subsoils (5–100 cm). The unchanged soil C stock (0–100 cm) over 12 years at the grassland sites suggests that the soil C stock level there has nearly reached a new equilibrium state. However, the increased blackness of the soil suggests changes in the quality of soil organic matter by higher subsoil root input and deeper bioturbation by earthworms.

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.

Analysis of spatiotemporal surface water variability and drought conditions using remote sensing indices in the Kagera River Sub-Basin, Tanzania

Soil moisture is one indicator or parameter of field quality. Soil moisture represents the soil water content which is essentially needed by vegetation to grow. Vegetation will experience with stress when the soil moisture is not adequate to grow the plant well. Therefore the soil moisture can be measured indirectly by determining the vegetation index by using remote sensing data. The methods applied in this study to calculate the vegetation index are soil adjusted vegetation index (SAVI) and normalized difference moisture index (NOMI). The results showed that most areas (more than 80%) in Jember have high SAVI and NOMI values which represent high soil moisture and it is good for the plants to grow. This study also measured the soil moisture from 20 soil samples in different locations along Jember regency. It is found the relationship functions of soil moisture and SAVI by the linear function of y = 0,0182 x - 0,3731 with the linearity of 94.4%, while the linear function of soil moisture and NOMI is y = 0,0151 x - 0,3817 with linearity 94.6%. This study resulted a mathematical model to determine soil moisture value based on the SAVI or NOMI values from satellite data. The error of this model is only about 5%, means this model can be considered as accurate model to determine soil moisture from SAVI an NOMI.

Forest ecosystems are among the largest terrestrial carbon reservoirs on our planet earth thus playing a vital role in global carbon cycle. Presently, remote sensing techniques provide proper estimates of forest biomass and quantify carbon stocks. The present study has explored Landsat-8 sensor product and evaluated its application in biomass mapping and estimation. The specific objectives were estimation of above ground biomass and carbon stocks using field data, assessing relationships of Landsat-8 spectral indices and field data and modeling of biomass and carbon stocks based on best linear regression model. Results showed that the highest aboveground biomass and below ground biomass was recorded as 246 t/ha and 64 t/ha whereas the lowest aboveground biomass and below ground biomass was 55 t/ha and 14 t/ha, respectively. Similarly, the highest above ground carbon and below ground carbon (t/ha) were 116 t/ha and 30 t/ha respectively while the lowest above ground carbon and below ground carbon (t/ha) were estimated as 26 t/ha and 6.7 t/ha respectively. Indices computed from Landsat-8 included normalized difference vegetation index, difference vegetation index, soil adjusted vegetation index, perpendicular vegetation index and atmospherically resistant vegetation index. Regarding relationship between aboveground biomass and vegetation indices, the coefficient of correlation (R2) were 0.67, 0.68, 0.65, 0.58 and 0.23 for normalized difference vegetation index, soil adjusted vegetation index, Perpendicular vegetation index, difference vegetation index and atmospherically resistant vegetation index respectively. The stepwise correlation between aboveground biomass (dependent variable) and five indices (Normalized difference vegetation index; soil adjusted vegetation index; Perpendicular vegetation index; difference vegetation index; atmospherically resistant vegetation index). Among five vegetation indices, only soil adjusted vegetation index was selected in stepwise method, satisfying the criteria and the overall model R2 was 0.63 and its adjusted R2 was 0.60. Simple linear regression model between aboveground biomass and single predictor index was better than stepwise regression model with (R2= 0.68) and (Root mean square error = 33.75 t/ha). Thus, soil adjusted vegetation index was considered best for biomass mapping. The study concluded that Landsat-8 product has considerable potential for biomass and carbon stocks estimation and can be expanded to national and regional forest inventories, modeling and future reducing emission from deforestation and forest degradation+ implementation.

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

Abundant and diverse urban bird communities promote ecosystem and human health in cities. However, the estimation of bird community structure requires large amounts of resources. On the other hand, calculating remotely sensed spectral indices is cheap and easy. Such indices are directly related to vegetation cover, built-up cover, and temperature, factors that also affect the presence and abundance of bird species in urban areas. Therefore, spectral indices can be used as proxies of the structure of urban bird communities. We estimated the abundance, taxonomic, functional, and phylogenetic diversity of the bird community at each of 18 50 m radius survey stations in the urban core area of Kavala, Greece. We also calculated eight spectral indices (means and standard deviations, SDs) around survey stations at 50 m, 200 m, and 500 m spatial scales. The land surface temperature SD (LST) was the most important proxy, positively related to bird abundance at the 50 m and 200 m spatial scales. At the same time, the mean green normalized difference vegetation index (GNDVI) was the most important proxy, negatively related to abundance at the 500 m spatial scale. Means and SDs of vegetation indices, such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI2), soil-adjusted vegetation index (SAVI), and atmospherically resistant vegetation index (ARVI), were the most important proxies, positively related to taxonomic and functional diversity at all the spatial scales. The mean and SDs of LST, normalized difference moisture index (NDMI), and normalized difference built-up index (NDBI) variously affected taxonomic and functional diversity. The mean and SDs of LST were the best proxies of phylogenetic diversity at the 50 m and 500 m spatial scales, while the SDs of NDBI and NDMI were the best proxies at the 200 m spatial scale. The results suggest that several spectral indices can be used as reliable proxies of various facets of urban bird diversity. Using such proxies is an easy and efficient way of informing successful urban planning and management.

The development of tourism infrastructure without control can cause changes in the environmental vegetation index and harm a tourism destination. This study aims to analyze changes in the vegetation index on Dodola Island from 2013-2021 to develop tourism infrastructure. The approach used is remote sensing, namely the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) using Landsat 8 Operational Land Imager (OLI). NDVI is an index of the greenery or photosynthetic activity of vegetation. Vegetation actively carrying out photosynthesis will absorb most of the red waves of sunlight and reflect higher near-infrared waves. In addition, SAVI is a soil vegetation index adjusted for soil pixels' effect using a vegetation density function. The results of this study indicate a perfect correlation between the NDVI average value and the SAVI average value of 0.996 so that it can be used as data to analyze changes in the vegetation index value on Dodola Island from 2013-2021. The average NDVI value of Dodola Island in 2017 showed a significant decrease to 0.16 from 0.35 in 2016. Likewise, the average SAVI value of Dodola Island in 2017 showed a reduction of 0.12 from 0 .27 in 2016. Although in 2021, the average NDVI value will increase to 0.32 and the average SAVI value to 0.27, stakeholders need to form institutions and establish policies as a preventive measure for the decline in the vegetation index worse than in 2017. Based on the Decree of the State Minister of the Environment No. 201 of 2004 concerning the characteristics of the damage to mangrove forests, the condition of the mangrove area of ​​Dodola Island in 2017 is categorized as damaged and sparse, so that mangrove rehabilitation is necessary. Based on the results of this study, a community-based mangrove ecotourism approach is recommended as an alternative method of sustainable tourism development on Dodola Island.

Optical remote sensing indices play an important role in vegetation information extraction and have been widely serving ecology, agriculture and forestry, urban monitoring, and other communities. Remote sensing indices are constructed from individual bands depending on special characteristics to enhance the typical spectral features for the identification or distinction of surface land covers. With the development of quantitative remote sensing, there is a rapid increasing requirement for accurate data processing and modeling. It is well known that the geometry-induced variation observed in surface reflectance is not ignorable, but the situation of uncertainty thereby introduced into these indices still needs further detailed understanding. We adopted the ground multi-angle hyperspectrum, spectral response function (SRF) of Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), Operational Land Imager (OLI), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Multi-Spectral Instrument (MSI) optical sensors and simulated their sensor-like spectral reflectance; then, we investigated the potential angle effect uncertainty on optical indices that have been frequently involved in vegetation monitoring and examined the forward/backward effect over both the ground-based level and the actual Landsat TM/ETM+ overlapped region. Our results on the discussed indices and sensors show as following: (1) Identifiable angle effects exist with a more elevated influence than that introduced by band difference among sensors; (2) The absolute difference between forward and backward direction can reach up to −0.03 to 0.1 within bands of the TM/ETM+ overlapped region; (3) The investigation at ground level indicates that there are different variations of angle effect transmitted to each remote sensing index. Regarding cases of crop canopy at various growth phases, most of the discussed indices have more than a 20% relative difference to nadir value except Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) with the magnitude lower than 10%, and less than 16% of Normalized Burn Ratio (NBR). For the case of wax maturity stage, the relative difference to nadir value of Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), Char Soil Index (CSI), NBR, Normalized Difference Moisture Index (NDMI), and SWIR2/NIR exceeded 50%, while the values for NBR and NDMI can reach up to 115.8% and 206.7%, respectively; (4) Various schemes of index construction imply different propagation of angle effect uncertainty. The “difference” indices can partially suppress the directional influence, while the “ratio” indices show high potential to amplify the angle effect. This study reveals that the angle-induced uncertainty of these indices is greater than that induced by the spectrum mismatch among sensors, especially under the case of senescence. In addition, based on this work, indices with a suppressed potential of angle effect are recommended for vegetation monitoring or information retrieval to avoid unexpected effects.

Amazon forest stocks large quantities of carbon both in plant biomass and in soil. Deforestation has accelerated the process of forest fragmentation in the Brazilian Amazon, resulting in changes in carbon stocks in both biomass and soil. Logging, including that under legal forest management, can create edge-like conditions inside the forest. We investigated the relationship between changes in carbon stocks in the soil and the distance to the nearest edge in forest remnants after about 30years of isolation. We assessed the effect of edges using geographically weighted regression (GWR), which considers the non-stationary character of soil carbon stocks and assigns relative weights to the observations according to the distance between them. Data from 265 georeferenced plots distributed over 28ha of forest fragments in the Manaus region were included in these analyses. Soil-carbon stocks were estimated for areas before (1984–1986) and after (2012–2013) isolation of the fragments. The GWR model indicated an apparent relationship between change in carbon stocks and distance from the edge (R2=0.79). The largest changes occurred in plots located closest to the edges. In 202 plots ⩽100m from an edge, soil-carbon stock increased significantly (p=0.01) by a mean of 1.34Mgha−1 over the ∼30-year period. Such changes in soil carbon stocks appear to be associated with higher rates of tree mortality caused by microclimatic changes in these areas. Increased necromass inputs combined with changes in composition and structure of vegetation may result in increased rates of decomposition of organic matter, transferring carbon to the soil compartment and increasing soil carbon stocks. Considering both “hard” edges adjacent to deforestation and “soft” edges in logging areas, the soil-carbon increase we measured implies an absorption of 6×106MgC in Brazilian Amazonia. In hard edges maintained for ∼30years, the soil-carbon increase offsets 8.3% of the carbon losses from “biomass collapse” in the first 100m from a clearing. Soil carbon did not change significantly in 63 forest-interior plots, suggesting that global climate change has not yet had a detectible effect on this forest carbon compartment.

Ex-situ conservation places such as botanical gardens require sufficient soil quality to support introduced species from various phytogeographical zones. The soil quality of the Botanic Garden of Indian Republic (BGIR), Noida, Uttar Pradesh, was evaluated to quantify soil nutrients. The dependency of one nutrient on the other nutrients was investigated using Pearson correlation and Multilinear regression analysis (MLRA). At the 0.05 level of significance, the nutrients Log10S and Log10EC (r = 0.97), N and OC (r = 0.98), Mn and OC (r = 0.97), Mn and N (r = 0.92), Ca and pH (r = − 0.91), Cu and Fe (r = 0.94) were found to be associated. Correspondence Analysis (C.A.) has been performed to find the association of soil elements with the soil type of study site. The spatial indices like NDVI (Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), NPCRI (Normalized Pigment Chlorophyll Index), RDVI (Renormalized Difference Vegetation Index) have shown significant correlation with the Log10S, Mg, Log10Zn, B and Fe respectively (with respective Pearson correlation coefficient r = 0.88, r = − 0.90, r = − 0.93, r = 0.91, r = 0.92 at P < 0.05). ARVI, along with other indices SCI (Soil Composition Index), NDMI (Normalized Difference Moisture Index), and MSAVI (Modified Soil Adjusted Vegetation Index), are also the predictor variables for Log10Zn (r = − 0.89, r = − 0.88 r = 0.92 at P < 0.05 respectively). MAVI2 (Moisture Adjusted Vegetation Index) positively correlates with OC, Mn content (r = 0.91, r = 0.93 respectively). MSAVI is negatively interrelated with Ca (r = − 0.89), SCI is negatively interrelated with Log10 K (r = − 0.98), BSI (Bare Soil Index) is positively associated with pH (r = 0.91), and negatively with Ca (r = − 0.93). At the same time, other indices like SAVI (Soil Adjusted Vegetation Index), SATVI (Soil Adjusted Total Vegetation Index), NDWI (Normalized Difference Water Index), and DVI (Difference Vegetation Index) have failed to explain the presence of soil nutrients based on spectral reflectance. This study is important for understanding the changing nutrient status of soil at the conservation site for successfully establishing plants from different phytogeographical zones.

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.

The use of the National Oceanic and Atmospheric Administration (NOAA) satellites, and the conventional Normalised Difference Vegetation Index (NDVI) model have shown promise as a large scale monitoring tool to understand the vegetation dynamics of the sparsely vegetated Sahelian grasslands. One of the assumptions of the NDVI model is that the soil background is spectrally homogeneous, which is not the case. Twelve sites, within two Système Probatoire d'Observation de Terre (SPOT) satellite imageries, corresponding to NOAA Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) pixel resolution, were assigned representative soil NDVI values for both dry and wet conditions. These soil NDVI values, together with herbaceous above-ground biomass production estimates, were used in a multiple correlation and regression analysis to assess statistically the soil impact on integrated NDVI values, i.e. values supposed only to express the total amount of vegetation in the end of the rainy season. The analysis showed that soil influence varied significantly with different soil types and moisture content, and should therefore not be ignored in satellite based vegetation monitoring.

Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical factors for current and future scenarios. The nine biophysical parameters are precipitation (Pr), maximum temperature (Tmax), minimum temperature (Tmin), soil texture (ST), available water capacity (AWC), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference chlorophyll index (NDCI), and normalized difference moisture index (NDMI) by Random forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Artificial Neural Net (ANN), and Support vector Machine (SVM). The multicollinearity test Boruta feature selection techniques that assessed interdependency and prioritized the factors impacting crop disease. However, climatic change scenarios were created using the most recent Climate Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) 2-4.5 and SSP5-8.5 datasets. The rice crop disease validation was accomplished using 1105 field-based farmer observation recordings. According to the current findings, Purba Bardhaman district experienced a 96.72% spread of rice brown spot disease due to weather conditions. In contrast, rice blast diseases are prevalent in the north-western region of Birbhum district, affecting 72.38% of rice plants due to high temperatures, water deficits, and low soil moisture. Rice tungro disease affects 63.45% of the rice plants in Bankura district due to nitrogen and zinc deficiencies. It was discovered that the link between NDMI and NDVI is robust and positive, with values ranging from 0.8 to 1. According to SHAP analysis, Pr, Tmin, and Tmax are the top three climatic variables impacting all types of disease cases. The study's findings could have a substantial impact on precision crop protection and meeting the United Nations Sustainable Development Goals.

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.