Analysis Of Vegetation Dynamics Research Articles

Quantitative Analysis of Vegetation Dynamics and Driving Factors in the Shendong Mining Area under the Background of Coal Mining

Quantitative Analysis of Vegetation Dynamics and Driving Factors in the Shendong Mining Area under the Background of Coal Mining

A Comprehensive Analysis of Vegetation Dynamics and Their Response to Climate Change in the Loess Plateau: Insight from Long-Term kernel Normalized Difference Vegetation Index Data

The Loess Plateau (LP) is a typical climate-sensitive and ecologically delicate area in China. Clarifying the vegetation–climate interaction in the LP over 40+ years, particularly pre- and post-Grain to Green Program (GTGP) implementation, is crucial for addressing potential climate threats and achieving regional ecological sustainability. Utilizing the kernel Normalized Difference Vegetation Index (kNDVI) and key climatic variables (precipitation (PRE), air temperature (TEM), and solar radiation (SR)) between 1982 and 2022, we performed an extensive examination of vegetation patterns and their reaction to changes in climate using various statistical methods. Our findings highlight a considerable and widespread greening on the LP from 1982 to 2022, evidenced by a kNDVI slope of 0.0020 yr−1 (p < 0.001) and a 90.9% significantly increased greened area. The GTGP expedited this greening process, with the kNDVI slope increasing from 0.0009 yr−1 to 0.0036 yr−1 and the significantly greened area expanding from 39.1% to 84.0%. Over the past 40 years, the LP experienced significant warming (p < 0.001), slight humidification, and a marginal decrease in SR. Post-GTGP implementation, the warming rate decelerated, while PRE and SR growth rates slightly accelerated. Since the hurst index exceeded 0.5, most of the vegetated area of the LP is expected to be greening, warming, and humidification in the future. In the long term, 75% of the LP vegetated area significantly benefited from the increase in PRE, especially in relatively dry environments. In the LP, 61% of vegetated areas showed a positive correlation between kNDVI and TEM, while 4.9% exhibited a significant negative correlation, mainly in arid zones. SR promoted vegetation growth in 23% of the vegetated area, mostly in the eastern LP. The GTGP enhanced the sensitivity of vegetation to PRE, increasing the area corresponding to a significant positive correlation from 15.3% to 59.9%. Overall, PRE has emerged as the dominant climate driver for the vegetation dynamics of the LP, followed by TEM and SR. These insights contribute to a comprehensive understanding of the climate-impact-related vegetation response mechanisms, providing guidance for efforts toward regional sustainable ecological development amid the changing climate.

Vegetation Dynamics of North African Steppe (Case Study of the Moulouya Plateau In Morocco)

Abstract The North African steppe areas represent a heritage of great economic and ecological importance. These areas, which were once prosperous, are currently experiencing significant degradation and a decrease in productivity due to several factors. The objective of this study was to evaluate the dynamics of the steppe vegetation, identify the responsible factors, and present perspectives for their management and restoration. The methodological approach adopted for the characterization of climatic and socioeconomic conditions and the analysis of vegetation dynamics combined classical geomatics methods with a data mining method by mobilizing several sources and on important temporal horizons. This study was optimized by using the Google Earth Engine platform. Results showed that steppe areas are characterized by their plant richness and great potential for resilience despite their low vegetation cover. The analysis of the dynamics of change has highlighted a regressive trend in steppe vegetation during the study period (1995–2020). This study has been able to highlight the extent of the dynamics of the steppes in the study area during the last 26 years and to identify the human activity as the main trigger for the transformation that steppe areas are currently undergoing. Such results improve our knowledge of these areas and open perspectives for their management.

Analysis of Vegetation Dynamics and Driving Mechanisms on the Qinghai-Tibet Plateau in the Context of Climate Change

The Qinghai-Tibet Plateau (TP) is susceptible to climate change and human activities, which brought about drastic alterations in vegetation on the plateau. However, the trends and driving mechanisms of vegetation changes remain unclear. Therefore, the normalized difference vegetation index (NDVI) was used to analyze the spatiotemporal distribution of vegetation and the consistency of dynamic trends in the TP from 2000 to 2020 in this study. The independent contributions and interactive factors of natural and human activities on vegetation changes were investigated through the Geodetector model. The drivers of vegetation under different dry–wet zones and precipitation gradients were quantitatively separated, and the internal mechanisms of vegetation changes were discussed from multiple perspectives. The results showed that from 2000 to 2020, the NDVI had an overall increasing trend, with an increasing rate of 0.0027 a−1, and the spatial pattern was different, increasing gradually from the northwest to the southeast. Consistent improvement occurred in the central and southeastern parts of the TP, while the western and northern parts consistently deteriorated. The annual mean precipitation had the greatest explanatory power for vegetation changes (0.781). The explanatory power of the integrated effects between two factors was greater than that of individual factors. The integrated effects between annual mean precipitation and other driving factors had the strongest explanatory power on vegetation variations. The driving mechanisms of vegetation dynamics varied among different dry–wet zones, and the vegetation growth was more sensitive to the response of precipitation in arid and semi-arid climate zones. This study enhances our understanding of the intrinsic mechanisms of vegetation changes on the plateau, which can provide a reference for ecological conservation, and has implications for further prediction and assessment of vegetation ecosystem stability.

Spatio-Temporal Variability Analysis of Vegetation Dynamics in China from 2000 to 2022 Based on Leaf Area Index: A Multi-Temporal Image Classification Perspective

Leaf Area Index (LAI) is one of the most important biophysical parameters of vegetation, and its dynamic changes can be used as a reflective indicator and differentiation basis of vegetation function. In this study, a VCA–MLC (Vertex Component Analysis–Maximum Likelihood Classification) algorithm is proposed from the perspective of multi-temporal satellite LAI image classification to monitor and quantify the spatial and temporal variability of vegetation dynamics in China since 2000. The algorithm extracts the vegetation endmembers from 46 multi-temporal images of MODIS LAI in 2011 without the aid of other a priori knowledge and uses the maximum likelihood classification method to select the categories that satisfy the requirements of the number of missing periods, absolute distance, and relative distance for the rest pixels to be classified, ultimately dividing the vegetation area of China into 10 vegetation zones called China Vegetation Functional Zones (CVFZ). CVFZ outperforms MCD12Q1 and CLCD land cover datasets in the overall differentiation of vegetation functions and can be used synergistically with other land cover datasets. In this study, CVFZ is used to cut the constant vegetation-type pixels of MCD12Q1 during 2001–2022. The results of the LAI mean time series decomposition of each subregion using the STL (Seasonal-Trend Decomposition based on Loess) method show that the rate of vegetation greening ranges from 9.02 × 10−4 m2m−2yr−1 in shrubland subregions to 2.34 × 10−2 m2m−2yr−1 in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The greening speed of grasslands and croplands in different zones varies widely. In contrast, the average greening speed of shrublands is the slowest. In addition, CVFZ detected grasslands with one or two phenological cycles, broadleaf croplands with one or two phenological cycles, and shrublands with no apparent or one phenological cycle.

Long term trend analysis of vegetation dynamics over Rajasthan using satellite derived enhanced vegetation index

Enhanced Vegetation Index (EVI) time series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the years 2001 to 2021 were used to understand the vegetation dynamics in the Rajasthan during last 21 years. Savitzky–Golay filtering technique was used for reconstruction of high quality data. The non- parametric Mann-Kendall test was used to evaluate the EVI time series trend at 30m pixel resolution. The results showed that: (1) The EVI has great variation in spatial distribution, decreasing trend from the southern and south-eastern parts to the western parts of the state, (2) EVI increased in 93.3% pixels and decreased in 6.6% pixels, indicating that the vegetation is improving rather than degrading. The findings of this research provide a scientific understanding of how vegetation is changing over Rajasthan and could help in the implementation of sustainable ecosystem management.

Analysis of Vegetation Dynamics of Tree Species inside the Forest of Institute of Forestry, Hetauda

A vegetation analysis of tree species was undertaken in the 92.69 hectares (ha) of the forest connected to the Institute of Forestry (IOF), Hetauda Campus. The study separated the forest into two strata, and a total of 10 circular sample plots (each plot measuring 500 m2) were constructed using systematic sampling with a sampling intensity of 0.5%. Enumeration of tree species was carried out within the sample plots. A total of 139 individual trees representing 16 (10 in stratum 1 and 12 in stratum 2) different tree species were recorded (60 in stratum 1 and 79 in stratum 2). Diversity indices were used for calculating vegetation parameters. According to the Importance Value Index, Shorea robusta was dominant in both strata, followed by other heterogenous species. The Shannon Wiener’s index and Simpson’s Diversity Index were higher in stratum 2, but the dominance index was lower than in stratum 1. Stratum 2 had a marginally higher measure of both evenness and richness than stratum 1. The study investigated the vegetation structure of the riverine and tropical moist deciduous forest in the study area. Increasing human interference had no significant effect on diversity and number of species among the strata. This study provides the baseline data necessary to characterize the phytodiversity of the forest area of the Institute of Forestry, Hetauda Campus for conservation and sustainable management.

A photographic transect method for field measurement of vegetation cover along an ecotone gradient in a desert environment

Vegetation cover is essential to the ecologic and biogeochemical functioning of drylands. These systems are marked by spatiotemporal variation in vegetation cover and biomass. Regular monitoring of vegetation cover in drylands is critical for their conservation and management. This study examines the effectiveness of photo-based grid point intercept field survey for quantifying vegetation characteristics in drylands. The field data provides ground truth data for space borne remote sensing to analyze changes in vegetation cover. The study integrates rapid field photo sampling, GPS, and GIS to maximize raw data collection in the field and allow for subsequent sampling in the laboratory setting. Paired T-tests and simple linear regression were employed to assess sample size. Quantification of vegetation cover using minimum distance (MD), spectral angle mapper (SAM), and support vector machine (SVM) automated classification were assessed using visual sampling as a reference. Fifty sampling points are sufficient for quantifying vegetation cover of steppe, but are less adequate for desert steppe. The assessment of automated classifications of photo plots revealed that SAM outperformed both MD and SVM. Photo-based Grid-point Intercept provides a cost- and time-effective technique for data collection to support satellite and air platform remote sensing analysis of vegetation dynamics.

Geotechnology Applied to Analysis of Vegetation Dynamics and Occurrence of Forest Fires on Indigenous Lands in Cerrado-Amazonia Ecotone

The Cerrado-Amazonia Ecotone is one of the largest ecosystems in Brazil and is internationally considered a biodiversity hotspot. The occurrence of fires is common in these areas, directly affecting biomass losses and the reduction of vegetative vigor of forest typologies. Information obtained through remote sensing and geoprocessing can assist in the evaluation of vegetation behavior and its relation to the occurrence of forest fires. In this context, the objective of the present study was to analyze temporal vegetation dynamics, as well as their relationship with rainfall and fire occurrence on Indigenous lands, located in the Cerrado-Amazonia Ecotone of Mato Grosso state, Brazil. Normalized Difference Vegetation Index (NDVI) images of the MOD13Q1 MODIS product and burnt area of the MCD45A1 MODIS product, and rainfall images from the Tropical Rainfall Measuring Mission (TRMM) sensor were used. The period analyzed was from 2007 to 2016. After pre-processing the NDVI, TRMM and burnt area images, correlation analyses were performed between the rainfall, vegetation index and burnt area images, considering different lags (−3 to 3), to obtain the best response time for the variables. The analyses of inter-annual vegetation index trends were carried out following Mann–Kendall monotonic trend and seasonal trend analysis methodologies. Significant correlations were observed between NDVI and rainfall (R = 0.84), in grass regions and between NDVI and burnt area (R = −0.74). The Mann–Kendall monotonic trend indicates vegetation index stability with positive variations in grass regions. The analysis of seasonal trends identified different vegetation responses, with this biome presenting a diverse phytophysiognomy and seasonal vegetation with different phases for amplitudes. This variation is evidenced by the various phytophysiognomies and their responses in relation to biomass gains and losses. The correlation and regression of the NDVI and rainfall in the vegetation type of grass areas show that the burnt area tends to increase with the reduction of NDVI. Finally, no defined pattern of vegetation cycles or phases was observed in terms of seasonality and the proposed methodology can be adapted to other world biomes.

The spectralrao-monitoring Python package: A RAO's Q diversity index-based application for land-cover/land-use change detection in multifunctional agricultural areas

• The Rao's Q diversity index is used to detect LCLU changes in multifunctional agricultural areas. • We developed and provide a Rao’s Q diversity index-based open-source Python application: spectralrao-monitoring . • We tested this application with Landsat 8 satellite data. • Overall accuracies of our three tested methodological approaches range from 0.88 to 0.92. • The study area is part of the largest Portuguese multifunctional farmstead. Monitoring multifunctional agricultural areas is paramount to ensure their cost-effective management. The remote sensing-based detection of land-cover/land-use (LCLU) changes and analysis of vegetation dynamics constitute a relevant indicator to support robust monitoring schemes, allowing the control of agri-environmental conditions and enforcing related measures and policies. The Rao's Q diversity index (RaoQ) is frequently used to measure functional diversity in ecology, thanks to the textural analysis of the environment. This paper aims to develop and provide an open-source Python application whose workflow may constitute a RaoQ-based LCLU change monitoring tool for multifunctional agricultural areas. Here, a use case is presented for detecting and mapping LCLU changes leveraging the free and open access Landsat 8 (L8) satellite data. The workflow is organized in four main stages: (1) data processing; (2) Normalized Difference Vegetation Index (NDVI) calculation; (3) RaoQ calculation; and (4) detection and mapping of LCLU changes through thresholding of RaoQ. Three methodological approaches were developed (RaoC – “classic” RaoQ; RaoMD – “multidimensional” RaoQ, and “classic + multidimensional” RaoQ) with overall accuracies ranging from 0.88 to 0.92. An example of an agri-environmental monitoring decision-support framework based on spectralrao-monitoring is presented. The application is easily reproducible, and the code is fully available and utilizable with other sensors at different resolutions to support monitoring other types of agricultural areas.

Integrated Fire Management as a Renewing Agent of Native Vegetation and Inhibitor of Invasive Plants in Vereda Habitats: Diagnosis by Remotely Piloted Aircraft Systems

The Cerrado biome is being gradually reduced. Remote sensing has been widely used to investigate spatio-temporal changes in the landscape, which are frequently limited to mapping with orbital sensors, while the Remotely Piloted Aircraft System (RPAS) proved to be advantageous in terms of spatial resolution and the application of advanced digital processing techniques. In this study, we investigated a vereda (humid area) of a conservation unit in the state of Mato Grosso, Brazil. Object-Based Image Analysis (OBIA) was applied to images obtained by RPAS to distinguish the phytophysiognomies of plant strata from the vereda and to diagnose the recovery of native and invasive vegetation after prescribed burning. The study was carried out in the following five stages: biomass collection; quality analysis of the land cover; phytosociological survey; collection of control points using a GNSS receiver (type L1/L2); and the capture of aerial images with an RGB camera coupled to a DJI Phantom 4 Pro, which was performed through overflights in three different periods. Object–Based Image Analysis was subsequently performed using the Nearest Neighbor classifier combined with Feature Space Optimization, obtaining classifications with accuracy and Kappa indexes greater than 80% and 0.80, respectively. The results of image processing allowed us to infer that fire acted as a renewing agent for native vegetation and as an inhibiting agent for invasive vegetation. The classification analyses combined with the phytosociological analysis allowed us to infer that the vereda is in the process of maturation. Therefore, the study demonstrated the potential of data obtained by RPAS for the diagnosis and analysis of vegetation dynamics in small wetlands submitted to Integrated Fire Management (IFM).

Assessment of vegetation cover on abandoned agricultural forest-steppe lands using multivariate analysis of their spectral response

It is crucial for all the regions including the forest-steppe zone that the vegetation cover on abandoned agricultural lands is thoroughly studied. This paper reviews some challenges of abandoned agricultural lands, which can be used as the basis for the real-time analysis of vegetation dynamics. The study aims to assess the capacity of automatic identification of forest stands established on abandoned agricultural lands through multivariate analysis of their spectral response. Based on the analysis of spectral reflective characteristics of abandoned agricultural lands from Landsat OLI data, it was found out that the reflective features of abandoned areas with deciduous, coniferous, and mixed forest stands differ significantly in the infrared band. It has been proposed to recognize abandoned agricultural lands with deciduous and coniferous forest stands through discriminant analysis of their reflective characteristics. It has been proved that reflectance in the red and infrared bands can be used to automatically detect abandoned agricultural lands with deciduous and coniferous stands. It is more challenging to identify abandoned areas with mixed forest stands.

Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine

Quantitative evaluation and driving mechanism analysis of vegetation dynamics are essential for promoting regional sustainable development. In the past 20 years, the ecological environment in Altay Prefecture has changed significantly due to global warming. Meanwhile, with increasing human activities, the spatiotemporal pattern and driving forces of vegetation variation in the area are uncertain and difficult to accurately assess. Hence, we quantified the vegetation growth by using the Normalized Difference Vegetation Index (NDVI) on the Google Earth Engine (GEE). Then, the spatiotemporal patterns of vegetation from 2000 to 2019 were analyzed at the pixel scale. Finally, significance threshold segmentation was performed using meteorological data based on the correlation analysis results, and the contributions of climate change and human activities to vegetation variation were quantified. The results demonstrated that the vegetation coverage in Altay Prefecture is mainly concentrated in the north. The vegetation areas representing significant restoration and degradation from 2000 to 2019 accounted for 24.08% and 1.24% of Altay Prefecture, respectively. Moreover, spatial correlation analysis showed that the areas with significant correlations between NDVI and temperature, precipitation and sunlight hours accounted for 3.3%, 6.9% and 20.3% of Altay Prefecture, respectively. In the significant restoration area, 18.94% was dominated by multiple factors, while 3.4% was dominated by human activities, and 1.74% was dominated by climate change. Within the significant degradation area, abnormal degradation and climate change controlled 1.07% and 0.17%, respectively. This study revealed the dynamic changes of vegetation and their driving mechanisms in Altay Prefecture, and can provide scientific support for further research on life community mechanism theory and key remediation technology of mountain-water-forest-farmland-lake-grass in Altay Prefecture.

Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data

The Qinling-Daba Mountains region (Qinba) is an important geographical transitional zone across the north and south of China. To comprehensively understand the ecological transition in the Qinba over past two decades, this study assessed and predicted the spatiotemporal variations of vegetation comparatively, using 250-m time series MODIS NDVI and EVI products. From 2000 to 2019, remarkable increases were observed both in annual and seasonal NDVI and EVI (P < 0.05), and the increasing rate of NDVI was higher than that of EVI for all temporal scales, except summer. Compared to NDVI, larger areas of no significant change were obtained in annual, autumn, and winter EVI, primarily distributed in the high-altitude regions of western Qinba. All assessed vegetation types increased significantly during past two decades except alpine vegetation and marsh, however, the seasons in which the significant increase in NDVI and EVI occurred varied for different vegetation types. Hurst exponent analysis suggested that inconsistent characteristics of vegetation dynamic trends were stronger in the future across Qinba. The area proportion of unfavorable trends, identified mainly covered with cultivated vegetation and scrub, was much larger than that of favorable trends, especially detected by EVI. Areas likely to experience vegetation variation of unfavorable and undetermined trends deserve high focus.

Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine

Vegetation is essential in maintaining terrestrial ecosystem functions, and understanding why and how vegetation evolves is necessary for regional ecological management. On the Tibetan Plateau, the effects of various factors and their interactions in directing vegetation change remain unclear. Supported by the Google Earth Engine (GEE) platform, we quantified spatiotemporal vegetation variation in Gannan Prefecture on the northeastern Tibetan Plateau for 2000–2018 using trend analysis and spatial autocorrelation and explored its driving factors with a geographic detector method. The normalized difference vegetation index (NDVI) illustrated fluctuating growth from 2000 to 2018, and the overall growth rate was 0.002/year. The spatial distributions of NDVI were variable, and areas with NDVI greater than 0.8 accounted for more than 69% of Gannan Prefecture area. From 2000 to 2018, the global Moran’s index of vegetation NDVI was greater than 0.52 and showed a fluctuating upward trend, indicating that the vegetation NDVI tended to be distributed with a high spatial concentration. The local Moran’s index of NDVI was mainly characterized by “High-High” and “Low-Low” clustering types, and the former increased significantly, but the latter decreased. The annual mean temperature, soil type, and elevation were the dominant factors driving vegetation NDVI change in Gannan Prefecture, explaining more than 15% of the variability. Furthermore, the influence of the interaction between any two factors on NDVI was an almost nonlinear enhancement. These results could help us improve our understanding of the underlying mechanism of NDVI changes and provide a scientific basis for ecological protection in alpine regions.

Deriving Planform Morphology and Vegetation Coverage From Remote Sensing to Support River Management Applications

With the increasing availability of big geospatial data (e.g., multi-spectral satellite imagery) and access to platforms that support multi-temporal analyses (e.g., cloud-based computing, Geographical Information Systems, GIS), the use of remotely sensed information for monitoring riverine hydro-morpho-biodynamics is growing. Opportunities to map, quantify and detect changes in the wider riverscape (i.e., water, sediment and vegetation) at an unprecedented spatiotemporal resolution can support flood risk and river management applications. Focusing on a reach of the Po River (Italy), satellite imagery from Landsat 5, 7, and 8 for the period 1988–2018 were analyzed in Google Earth Engine (GEE) to investigate changes in river planform morphology and vegetation dynamics associated with transient hydrology. An improved understanding of these correlations can help in managing sediment transport and riparian vegetation to reduce flood risk, where biogeomorphic processes are commonly overlooked in flood risk mapping. In the study, two established indices were analyzed: the Modified Normalized Difference Water Index (MNDWI) for monitoring changes in the wetted river planform morphology, inferring information about sediment dynamics, and the Normalized Difference Vegetation Index (NDVI) for evaluating changes in vegetation coverage. Results suggest that planform changes are highly localized with most parts of the reach remaining stable. Using the wetted channel occurrence as a measure of planform stability, almost two-thirds of the wetted channel extent (total area = 86.4 km2) had an occurrence frequency &amp;gt;90% (indicating stability). A loss of planform complexity coincided with the position of former secondary channels, or zones where the active river channel had narrowed. Time series analysis of vegetation dynamics showed that NDVI maxima were recorded in May/June and coincided with the first peak in the hydrological regime (occurring in late spring and associated with snowmelt). Seasonal variation in vegetation coverage is potentially important for local hydrodynamics, influencing flood risk. We suggest that remotely sensed information can provide river scientists with new insights to support the management of highly anthropized watercourses.

Analysis of vegetation dynamics using the normalized difference vegetation index (NDVI) at the archipelago of Fernando de Noronha, Pernambuco, Brazil

Island environments have specific biotic and abiotic characteristics, as fragility, limitation of natural resources, geographic isolation, and fragmentation are determining factors that directly affect these areas. Thus, it is relevant to understand the natural evolution of the landscape in the islands, considering the anthropic actions and climate changes in the transformation of vegetation cover, as a means of time series and study of satellite images. This paper aims to analyze the dynamics of the landscape (changes in vegetation cover) of the Fernando de Noronha Archipelago concerning urban development, and other anthropic activities that occurred between 1999 and 2018, through remote sensing images, to establish comparisons with the Island Management Plans that were elaborated in the years of 2005 and 2017. Also, this study intends to raise elements to assist in the spatial management of the Archipelago and to establish Public Conservation Policies for Fernando de Noronha and other island areas. Images from Landsat 7 and Landsat 8 were obtained for scenes from 1999 and 2017, respectively. These images were preprocessed and analyzed in Quantum GIS 2.18 software. And applied the NDVI calculation. It was also used the database found in the sustainable management plan of the archipelago provided by the state government of Pernambuco. With these data, it was possible to diagnose a vegetative growth on the island of about 45.36% in 17 years corroborating with the changes found in the data coming from the island's management plan. However, there are no changes in the phytosociological diversity of the island, this cause is pointed out to the invading and ruderals species of the island that are established and propagate.

Analysis of vegetation dynamics using remote sensing and GIS: a case study of Madhya Pradesh, India

This paper is aimed to study the correct growth cycle of two staple foods (i.e., wheat and rice), of Madhya Pradesh state of India through an approach of vegetation dynamics which focuses on analysing various parameters, such as rainfall, soil temperature, soil moisture, and crop growth trend over time using temporal data based on regression analysis to generate crop maps. This paper analyses various stages of growth for two major staple crops in Madhya Pradesh (M.P.) region. Wheat and rice farming in M.P. is facing issues related to climate change, drought, fertilizer application, low selling prices, and other. Nature of growth and effect of various parameters like rainfall, temperature, was studied and a proper graph analysis was done for a better understanding of plant growth, and generation of various phenological maps. In multiple regression analysis, R2 value of 0.24 and 0.45 was found highest for HH and VV polarization for wheat and rice, respectively. This means that multi-linear regression for HH and VV explains 24% and 45% for wheat and rice, respectively. Analysis and simulation of crop phenology was done by using the trend analysis approach with the help of the various graphs values that explains the different stages of crop growth and the effect of changing temperature and rainfall over the growth of the crops, Use of ArcGIS 10.5 software simplifies the study process and helped in developing a better understanding.

Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade

Terrestrial protected areas (PAs) play an essential role in maintaining biodiversity and ecological processes worldwide, and the monitoring of PAs is a useful tool in assessing the effectiveness of PA management. Advanced remote sensing technologies have been increasingly used for mapping and monitoring the dynamics of PAs. We review the advances in remote sensing-based approaches for monitoring terrestrial PAs in the last decade and identify four types of studies in this field: land use &amp; land cover and vegetation community classification, vegetation structure quantification, natural disturbance monitoring, and land use &amp; land cover and vegetation dynamic analysis. We systematically discuss the satellite data and methods used for monitoring PAs for the four research objectives. Moreover, we summarize the approaches used in the different types of studies. The following suggestions are provided for future studies: (1) development of remote sensing frameworks for local PA monitoring worldwide; (2) comprehensive utilization of multisource remote sensing data; (3) improving methods to investigate the details of PA dynamics; (4) discovering the driving forces and providing measures for PA management. Overall, the integration of remote sensing data and advanced processing methods can support PA management and decision-making procedures.

Patch organization and resilience of dryland wetlands

Dryland wetlands are ecosystems of high ecological importance as they serve as habitat sanctuaries for aquatic and terrestrial biota in areas with very few resources; therefore, the study of such environments is of major importance for the conservation of biodiversity in arid and semi-arid areas. The vegetation organization in these ecosystems is driven by the water regime as the main driver, but local processes like seed banks and soil resources redistribution also play a crucial role in determining the spatial distribution of the vegetation. Assessment of vegetation dynamics and long-term resilience requires the use of realistic models that can integrate the water regime and that can continuously simulate vegetation extent and conditions under flood-drought cycles. Here we study the influence of the water regime as the main driver of the vegetation. We apply a vegetation-modelling framework to compare the performance of a simplified model at the cell scale and a model integrated at a patch scale. Our results show that aggregating the analysis of vegetation dynamics at the patch scale allows for the incorporation of the effects of both local drivers (acting within the patch) as well as the global drivers (acting over the patch as a whole). The water regime acts as a global driver for the vegetation and indirectly affects the local drivers. Our patch scale model successfully captures wetland vegetation dynamics using the water regime as the main driver for representing changes in the vegetation and assessment of the wetland resilience under flood-drought periods.