Maximum Normalized Difference Vegetation Index Values Research Articles

GIS and remote sensing Based Analysis of Land use and Land cover Change in the Upper Anger watershed, Western Ethiopia

ABSTRACT Anthropogenic land use and land cover (LULC) change was the most serious problem in worldwide. This study attempted to investigate spatiotemporal LULC changes in the Upper Anger watershed, Western Ethiopia. In the present study, Landsat TM of 1990, Landsat ETM+ of 2003, and Landsat OLI/TIRS of 2020 were used to analyze the LULC change and Normalized Difference Vegetation Index (NDVI). The supervised classification with maximum likelihood algorithm was employed for classification of the LULC. The result shows that the agricultural land was increased by 707.4 km2, whereas forest land was decreased by 179.9 km2. Consequently, agricultural land and bare land were increased with rate of 23.6 km2/year and 0.5 km2/year, while grassland and forest cover decreased by rate of 18.2 km2/year and 6 km2/year, respectively, from 1990 to 2020. The maximum NDVI value was declined with 0.19 from 1990 to 2020 due to expansion of agricultural land, bare land, and settlement in the study area. About 530 km2 of grassland and 241.5 km2 of forest cover were converted into other land cover classes between 1990 and 2020. Thus, appropriate land use planning and environmental education should be promoted to minimize the rapid loss of forests and grasslands in the study area.

Trends of greening and browning in terrestrial vegetation in China from 2000 to 2020

Terrestrial vegetation condition is altering generally as a result of climate change and anthropogenic activity during the past few decades. To reveal the impact of long-term climate factors and artificial protection on multiple vegetation types, it is crucial to understand the spatial distribution of vegetation greening and browning and the effect of national ecological restoration programs. In this study, we established a persistent vegetation change index (P-value) to characterize greening (restoration) and browning (degradation) in China in 2000–2020. Firstly, we generated annual time-series normalized difference vegetation index (NDVI) data from MODIS product by averaging the monthly maximum NDVI values for each year. Secondly, we calculated the P-value to investigate the continuous change in vegetation state by incremental time interval. Finally, patterns and trends of greening and browning in forests, shrublands, and grasslands were quantified and mapped at pixel and sample point levels. The findings of our study revealed that Chinese wild vegetated lands greened up by ∼3.4 × 104 km2 (25%) and turned brown in ∼1.6 × 104 km2 (11%) between 2000 and 2020. Net greening was detected in all biomes, most conspicuously in several ecological program regions in northern China. The NDVI time-series data in 31% of field plots showed a consistent result, 11% of field plots showed a browning trend, and 58% of field plots showed a stable state. These results indicated a synergistic effect on forests, shrublands, and grasslands, but with regional variations attributed to differences in precipitation abundance, the implementation of positive ecological programs by the government and negative human activities. Additionally, these findings provide valuable insight into large-scale terrestrial vegetation transitions and have practical applications for decision-making and policy development in the assessment and restoration of ecosystems aimed at reducing carbon emissions, mitigating climate change, preserving biodiversity, and conserving water resources in China.

Comparative Assessment and Monitoring Changes in NDVI of Achanakmar Tiger Reserve (ATR) and its Buffer Zone, India

Achanakmar Tiger Reserve (ATR), endowed with rich biological diversity and lush green vegetation in and around, makes it more unique. It is also an integral part of the Achanakmar Amarkantak Biosphere Reserve (AABR) and has been identified as one of the important tiger reserves of the Central Indian landscape due to its connectivity with other protected areas and tiger reserves in neighboring landscapes. Vegetation mapping and monitoring are important to understand changes in ecosystem processes and associated temporal and spatial impacts. Pre- and post-monsoon IRS, LISS III, and AWiFS satellite data from 2000, 2004, 2008, 2010, and 2013 were used for the present study. This paper is an attempt to examine the variation in the normalized difference vegetation index (NDVI) of ATR and its buffer zone on a seasonal and temporal basis. Climate conditions such as temperature, precipitation, relative humidity, etc. play an important role in the growth and development of healthy vegetation. The NDVI value of ATR has shown fluctuation and recorded positive growth over the past 14 years with few exceptions. The post-monsoon season recorded a higher NDVI value as compared to the pre-monsoon months. The maximum NDVI value was recorded in 2004 (+0.539) for the entire ATR and its buffer zone.

Permafrost degradation services for Arctic greening

As an important component of the climate system, permafrost responds significantly to climate change, and its impact on the ecosystem cannot be ignored. In this study, we analyzed the temporal and spatial variation trends of the normalized difference vegetation index (NDVI) in Arctic permafrost regions and revealed the correlation between the active-layer thickness (ALT), soil temperature, and NDVI change. Using the partial correlation method, we assessed the ecological regulation service of permafrost to the ecosystem. The results showed that both the average annual maximum and summer NDVI values in the Arctic region followed a significant increasing trend from 1982 to 2015. The average correlation coefficient (ACC) between Arctic NDVI and ALT was 0.35, followed by the ACC (0.33) between NDVI and soil temperature at 7–28 cm depth, and had a lower ACC (0.31) at 0–7 cm ALT. When the precipitation and snow water equivalent (SWE) remained unchanged, the partial correlation between NDVI and ALT was 0.711, which was a significant positive correlation. It also showed that permafrost degradation was the dominant factor controlling Arctic NDVI increase, whereas precipitation and SWE had little effect. The study revealed the impact of permafrost on NDVI change, deepened our understanding of the importance of permafrost degradation for ecosystem services, and effectively filled the gap that tundra ecosystem services value has been ignored in the global ecological service value assessment.

Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia

Wetland ecosystems are one of the most important areas that provides different ecosystems services as well as habitat for plant and animal species. In spite of multipurpose, wetland ecosystems are under threats. This study attempts to analyze wetland dynamics of Abay Choman and Jimma Geneti watershed in Horo Guduru Wollega Zone, Western Ethiopia using geospatial techniques. The land use land cover (LULC), the Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were investigated using Landsat 5 TM of 1991, Landsat 7ETM+ of 2003, and OLI/TIRS of 2021. In the present study, the LULC was classified using a supervised classification method with maximum likelihood algorithm. The red and infrared bands of Landsat imagery from three different time periods were used to calculate NDVI, while the NDWI was estimated using the green and near infrared (NIR) bands of multispectral Landsat images. Results show that wetland ecosystem in the study area decreased by about 125.2 km2 (8.8%) with the rate of 4.2 km2/year. In contrast, agricultural land increased by 223.4 km2 with the rate of 7.4 km2/year between 1991 and 2021. About 66.7 km2 wetland was converted to cultivated land whereas 29.3 and 24.7 km2 of grassland and shrubs land were converted into cultivated land. As a result, the maximum NDVI and NDWI values were decreased between 1991 and 2021. To minimize the rapid loss of wetland and water bodies in the study area, proper land use planning and environmental education should be promoted.

Estimation of Canopy Leaf Area Index of Tropical Rainforests of Sri Lanka from the Normalized Difference Vegetation Index along a Wide Altitudinal Gradient

The leaf area index (LAI) of a forest is a key determinant of its primary productivity. Estimation of LAI via remote sensing of forest canopies offers an alternative to direct measurement, which is difficult in tropical rainforests (TRFs). Our objectives were to determine the variation of LAI and Normalized Difference Vegetation Index (NDVI) of a range of TRFs of Sri Lanka across a wide altitudinal range (117-2,132 m above sea level) and develop a predictive model to estimate LAI from NDVI and canopy architectural properties. Ten permanent sampling plots (PSPs) of one hectare each were established in forest reserves of Kanneliya (117-174 m), Sinharaja-Pitadeniya (509-618 m), Sinharaja-Enasalwatte (1,042-1,065 m), Rilagala (1,668 m), Hakgala (1,804 m), Piduruthalagala (2,080 m) and Horton Plains (2,132 m). Canopy LAI and its architectural properties (i.e., Mean Leaf Angle (MLA) and leaf angle distribution) were computed by analysis of ‘fish-eye’ images obtained from September 2019 to July 2020, using hemispherical photography and HemiView software. Satellite images for March-April, 2020 were downloaded from Landsat 8 OLI/TIRS C2L1. NDVI (NDVI=λNIR-λRED/λNIR+λRED) was calculated from ENVI software, where λNIR and λRED are reflectances of near-infrared and red wavebands. ENVI software computed the maximum NDVI (NDVIMax) and minimum NDVI (NDVIMin) values among 30 m×30 m pixels within each PSP. Mean NDVI (NDVIMean) was computed by taking the mean of NDVI values of all pixels within a PSP. Canopy LAI ranged from 1.94 (Pidurutalagala) to 3.38 (Pitadeniya). The corresponding ranges for NDVIMax, NDVIMean and NDVIMin were 0.620-0.767, 0.594-0.764 and 0.429-0.747 respectively. Canopy LAI, NDVIMax and NDVIMean showed significant (p<0.05) linear decreasing trends with increasing altitude. For every 1,000 m increase in altitude, LAI, NDVIMax and NDVIMean decreased by 0.396 (Adjusted-R2=0.407, AIC=-19.92), 0.066 (0.698,-66.72) and 0.058 (0.493,-61.27). In contrast, NDVIMin did not show a significant trend with altitude. Second-order polynomial functions showed greater explanatory power than the linear functions, in terms of adjusted-R2and AIC, in fitting the variation of LAI (Adj.-R2=0.492, AIC=-20.77) and NDVIMax with altitude (0.755,-68.15). The estimated maximum LAI and NDVIMax were at 637 and 329 m above sea level respectively. From among a range of multiple linear regression models using different combinations of NDVI, canopy architectural properties and altitude, the following two models were selected for predicting LAI, based on their adj-R2 and AIC values: LAI=1.269+[3.567.NDVIMax]–[1.598.NDVIMin]–[0.000215.Altitude] (Adj[1]R2=0.544, AIC=-21.41); LAI=-0.673+[5.887.NDVIMax]–[1.490.NDVIMin]–[0.000452.MLA] (0.489, -20.27).
 Keywords: Normalized Difference Vegetation Index, Leaf Area Index, Tropical rainforests, Altitude, Canopy architecture

A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin.

In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013–2021) were compared to the phenological profiles for the period before the crisis (1986–2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices.

Mangrove-based Ecotourism Sustainability Analysis using NDVI and AHP Approach

This article aims to analyze the sustainability of mangrove ecotourism using the Normalized Difference Vegetation Index (NDVI) and Analytical Hierarchy Process (AHP) approaches. Based on Landsat 8 OLI satellite imagery calculation using the NDVI technique, there has been a decrease in vegetation value on Dodola Island in 2017. This condition needs to be analyzed scientifically, considering the Dodola Island mangrove area to be preserved. In addition to the interests of tourism infrastructure development. The research method used is a mixed research method through a case study approach in Dodola Island, Morotai Island Regency, North Maluku Province, Indonesia. This study adopts remote sensing techniques and decision support systems to describe the results of sustainable mangrove ecotourism analysis. This study indicates that the calculation results of Landsat 8 OLI spatial data from 2013-to 2021 show a significant decrease in vegetation value in 2017, where the maximum NDVI value is 0.30, and the minimum NDVI value is 0.11. Specifically, the mangrove area also experienced a decrease in vegetation value with a maximum NDVI value is 0.23 and a minimum NDVI value is 0.02. To anticipate environmental damage in mangrove areas, this study recommends mangrove conservation programs, namely rehabilitation, restoration, reclamation, and conservation of mangrove areas. In addition, the results of the priority analysis using the AHP approach show that the rehabilitation program is a program that needs to be prioritized because it follows the existing conditions and capabilities of the Dodola Island managers.

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

Using MODIS-NDVI Time Series to Quantify the Vegetation Responses to River Hydro-Geomorphology in the Wandering River Floodplain in an Arid Region

Vegetation, hydrology and geomorphology are three major elements of the floodplain ecosystem on Earth. Although the normalized difference vegetation index (NDVI) has been used extensively to characterize floodplain vegetation growth, vigour and biomass, methods for quantifying the various distinct responses of floodplain vegetation to hydro-geomorphological changes in different lateral belts in arid regions are still needed. In this study, the Linhe reach was divided into four lateral belts based on their hydro-geomorphological characteristics, and the Moderate Resolution Imaging Spectroradiometer (MODIS)-NDVI time series statistical indicators were used to characterise the distinct changing the patterns of vegetation growth in different belts. The response of floodplain vegetation to river hydro-geomorphology in each belt was analysed. The result showed that the average maximum NDVI value in the regular inundation area was 0.23 and higher than that in the other lateral belts. The correlation between the water persistence time and peak NDVI value in the regular water inundation area was significant (ρ = 0.84), indicating that in contrast to highly frequent or extremely rare water inundation, regular water inundation provides significant benefits to floodplains. Continuous or highly frequent inundation may cause decreased vegetation productivity. Overall, our results suggest that the vegetation greenness response to the river hydro-geomorphology is different from the river to the edge of the floodplain. Thus, a better understanding of the interactions between the floodplain vegetation and river hydro-morphology and river water resource management in arid-region floodplains.

Spatially explicit changes in forest biomass carbon of China over the past 4 decades: Coupling long-term inventory and remote sensing data

Quantification of the spatial pattern of forest carbon (C) sinks in high resolution is helpful to reveal the factors that affect the C cycle and provides valuable information for developing sustainable forest management policies. Here we developed a method using the data of long-term forest inventories (1977–2018) and spatially-explicit remotely sensed information from land-use maps and the Normalized Difference Vegetation Index (NDVI) datasets, to estimate the spatial and temporal variation of forest biomass C in China. At first, we calculated forest biomass C stocks using the refined Continuous Biomass Expansion Factor (CBEF) model with parameters for each forest type based on eight national forest inventories. Secondly, based on multi-temporal land-use remote sensing and national forest inventory datasets, we obtained forest coverage datasets with high resolution (1 km*1 km). Thirdly, we downscaled the forest biomass C density using the calibrated forest coverage maps and the maximum NDVI values derived from GIMMS-NDVI3g imagery. Our results showed that China's forest functioned as a C sink of 3777.73 Tg C, and the C density of forest stands increased from 35.41 Mg C ha−1 during 1977–1981 to 43.95 Mg C ha−1 during 2014–2018. In addition, the validation results for most of the provinces based on published inventory estimates during the eight periods showed that the forest area at the pixel scale was successfully calibrated. From this, we produced the maps with a finer resolution for a series of spatially continuous forest biomass carbon density distribution and carbon sinks. Notably eight major forest projects have accounted for 44%–51% of the forest C stocks added in China from 1977 to 2018. Our research provides new insights for understanding and monitoring the spatiotemporal variations in of forest biomass and key information to support the development of new afforestation policies moving forward.

An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

Immediate disturbances induced by tropical cyclone Fani on the coastal forest landscape of eastern India: A geospatial analysis

An extremely severe tropical cyclone Fani made its landfall on 3rd May, 2019 along the coastal region of Odisha, India causing profound damage to its natural and human resources. This study examines the immediate effects of the cyclone on vegetation dynamics and patterns of canopy gap formation in Balukhand Wildlife Sanctuary of this region. Sentinel 2A data of pre and post-Fani scenarios were used to derive Normalized Difference Vegetation Index (NDVI) and relevant landscape metrics of patch and class levels for this forest from coast (zone A) to inland (zone C). Results showed that even the maximum NDVI values of the post-cyclone scenario were either less than or equal to the mean NDVI values of the pre-cyclone scenario for most of the grids of three zones. Among the twenty three grids, severe damage was identified in seven grids mostly covering western parts of the forest owing to high levels of exploitative human interventions. Species like Casuarina equisetifolia and Eucalyptus globulus had suffered the most. The individual as well as combined interactions of land cover and cyclone scenario were found to have significantly strong effects on patch metric values (p < 0.01) in three-way analysis of variance. Regarding canopy gaps, zone A was characterized by clusters of few large patches (> 1 ha area; > 400 m perimeter) and multiple tiny patches but canopies of zone B and C were primarily perforated through multitudes of tiny, irregular cum amorphous gaps. Zone-wise ecological restoration was recommended to mitigate the statistically significant losses of vegetated patches (p < 0.001) due to the cyclone and accordingly relevant species were suggested for site-specific plantation.

Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed

Winter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (>26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass (p ≤0.01, R2 = 0.56) and with observed percent vegetative ground cover (p ≤0.01, R2 = 0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014–15, 2015–16, and 2017–18) for four Maryland counties. Results from winter 2017–18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit. Project supportThis project was supported by the USGS Land Change Science Program within the Land Resources Mission Area, the USDA Choptank River Conservation Effects Assessment Project (CEAP), the USDA Lower Chesapeake Bay Long Term Agricultural Research (LTAR) Project; the Maryland Department of Agriculture; and the NASA DEVELOP National Program.

Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data

Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%.

Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE &lt; 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

Long Term Vegetation Health Monitoring in Dibru-Saikhowa National Park using Remote Sensing &amp; Gis

Remote sensing and GIS based vegetation monitoring offers lot of potential for ecosystem studies. This study utilized freely available moderate resolution Landsat images to quantify the changes in vegetation dynamics in Dibru-Saikhowa national park, India. A wide range of vegetation indices and temperature indices such as normalized difference vegetation index (NDVI), land surface temperature (LST), vegetation condition index (VCI), temperature condition index (TCI) and vegetation health index (VHI) was utilized for the purpose of the study. Results reveal that the study area has gone through changes in vegetation and temperature pattern affecting the land surface balances. The maximum NDVI value for the year 1996 was recorded between 0.5-0.8 whereas the maximum LST values ranged between 17.240C-34.850C. In 2019, the maximum NDVI values reduced to the range of 0.14-0.6 while LST increased to 18.950C-38.910C. Consequently, the VHI classes showed a negative trend. In 1996, healthy vegetation covered a total area of 14564.6 ha which reduced to 9872.1 ha in 2019. Conversely, the no vegetation class showed a significant positive trend from 951.3 ha to 3015.99. Such alteration in vegetation dynamics in the study area is affecting the local climate and regional ecosystem services and require instant attention of conservationist and policy makers.

Long- Term Estimation of Actual Crop Evapotranspiration Based on Satellite Data Analysis

Abstract Arid regions conditions are overwhelmed with various water scarcities problems, however, one of the reasons of this problems may be due to climate change effect. Moreover, under these conditions, water is a crucial key for any agricultural development processes. Therefore, the aim of this study was to estimate the actual evapotranspiration of different crop pattern under climate change conditions based on satellite data analysis for long time effect (1985 – 2019). The study area is considered active agricultural area , the percentage of bare soil and vegetation cover were (86.9%) and (12.7) in 1985 while in 2019 the percentage of bare soil sharply decrease so it was reached to (34.1%) but the percentage of d vegetation cover was (64.2%). Satellite images have been gathered and analyzed from Landsat TM, Landsat ETM+ and Landsat OLI, through the studied period. However, Normalized Difference Vegetation Index (NDVI) had been obtained for estimating the actual evapotranspiration under the studied area conditions. Planning and management of water use by irrigation agriculture are especially important in an arid and semiarid areas like the study area so the understanding of the Actual Evapotranspiration (AET) prerequisite for better management and conservation of agriculture water, By determine the area of every crop pattern, actual evapotranspiration (AET) was estimated by m3/ha/day and m3/total cultivated area/day. Data analysis indicate that the minimum and maximum NDVI values had been ranged from 0.7 and 0.9 for mango trees from 1985 and 2019, 0.5 and 0.8 for olive trees, 0.4 and 0.8 for forests and 0.7 for annual crops within the studied period from 1985 to 2019. The minimum and maximum actual evapotranspiration under different crop pattern from 1985 to 2019 ranged from 2.6 and 4.9 mm/day, 1.7 and 4 mm/day, 1 to 4.4 mm/day and 2.7 mm/day for the same crop pattern respectively. This study can assist in water management practices for better irrigation management.

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen’s slope, Mann–Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.

A climatological study of the spatio-temporal variability of land surface temperature and vegetation cover of Vadodara district of Gujarat using satellite data

ABSTRACTIn the present study, a comprehensive assessment of the spatio-temporal variation of day-time and night-time land surface temperature (LST) and normalized difference vegetation index (NDVI) of Vadodara district of Gujarat in India from 2001 to 2012 has been carried out using satellite data. A significant cooling trend was observed in the day-time LST, whereas the night-time LST showed a distinct warming trend. The entire geographical extent of Vadodara was classified into different night-time LST classes to quantify the extent of the hot pockets, and it showed a clear-cut warming pattern for all the months of the year with an increase in the geographical areas under higher temperature range. Further analysis of Diurnal Temperature Range (DTR) also revealed a strong impact of the urbanization process, with annual DTR showing a decreasing trend at the rate of 0.29°C year−1. An analysis of the vegetation cover of the district showed that on an average, the NDVI of the district increased during the study period. However, a micro-level examination of NDVI values depicted that the type of vegetation cover had drastically changed. The maximum NDVI values for months from May to December for 2012 were much lower than those of 2001 and 2006, indicating a change in vegetation pattern of the district. An assessment of the area under different NDVI values exhibited that for all the months of the year (except September), the total area with NDVI values of higher range (i.e. +0.5 and above) had substantially decreased from 2001 to 2012. The analysis revealed that for some of the months like February, while in 2001, 45% of district exhibited NDVI values above +0.5, but by 2012, it had decreased to only 18%, showing a drastic change in vegetation type and deterioration of the extent of thick dense vegetation.