Dinder National Park (DNP) constitutes one of the oldest and most significant natural reserves in Africa, established in 1946 and situated within the geographical confines of Sudan. Designated as a UNESCO Biosphere Reserve in 1987, it encompasses an extensive expanse of 10,197 square kilometers ( MPDNP, 2004 ). The park’s topography comprises a variety of habitats, which are indispensable for the diverse array of species it harbors. Positioned north of the 406.4 mm rainfall isohyet, the semi-arid climatic conditions of the park render it particularly vulnerable to the detrimental effects of forest fires. The southeastern region of the park is distinguished by its dense vegetation, attributable to relatively elevated rainfall levels, while other sections exhibit a higher susceptibility to fire outbreaks, as evidenced by satellite data observations.

Dinder National Park is bordered by three states: Sennar, Gedarif, and Blue Nile ( Figure 1 ). Administratively, the park falls under the Dinder locality of Sinnar State. It borders the Gadarif state in the north-eastern direction, and a small portion falls under the Rosieres district of the Blue Nile state ( Abdel Hameed & Eljack, 2003 ). The Park is bordered by the Rahad River at latitude 12˚26'N and longitude 35˚02'E, and then continues in a north-western direction up to latitude 12˚42'N and longitude 34˚48'E at Dinder River. The boundary continues again up to latitude. 12˚32'N and longitude. 34˚32'E along Khor Kennana, and finally the boundary slightly diverts to the southeast to latitude. 11˚55'N and longitude. 34˚44'E, and then gets to the Sudan-Ethiopian border Figure 1 ( MPDNP, 2004 ).

The Dinder and Rahad are the two rivers that flow through the DNP region in the northwest direction, and the banks of the two rivers consist of a group of lakes or back swamps (i.e., Mayas) ( Abdel Hameed et al., 1997 ). The soil in (DNP) consists of two types vertisols and entisols ( Holsworth, 1968 ; Dasmann, 1972 ). The climate of the park is characterized by two seasons: the hot and humid rainy season (May November) and the cool and dry season (December-March) the northeastern part of the park has the least rainfall (400 - 800 mm), which gradually increases with distance towards the southeast of the park (800 - 1000 mm). ( MPDNP, 2004 ). The vegetation in the park consists of three ecosystems: The A. seyal-Balanites aegyptiaca ecosystem, Mayas ecosystem, and the riverine ecosystem) ( Abdel Hameed et al., 1996 ). The Mayas wetlands are the most unique feature and one of its three major ecosystems. “Mayas” is a local name for floodplain wetlands that are found on both sides of the Dinder and Rahad rivers. There are more than 40 mayas that are part of the river Dinder and Rahad ecosystems inside the DNP ( Hassaballah et al., 2016 ).

Remote sensing (RS) is formally defined as “the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation” ( Lillesand & Kiefer, 1979 ). The present study employed remote sensing methodologies to assess the repercussions of fire frequency on tree diversity within DNP. Satellite imagery procured from the MODIS and LANDSAT missions, in conjunction with NDVI time series data from the PROBA-V Mission spanning the years 2010 to 2020, constituted the primary data sources for the remote sensing analytical framework. The spatial data examination was executed utilizing ENVI 4.5, QGIS 3.16, and ArcGIS 10.3 software, with documentation facilitated through Microsoft Office applications.

Field data acquisition (refer to Appendix 1) was augmented by the utilization of a GPS receiver for accurate geospatial sampling, complemented by a Toshiba Satellite C855 laptop and a printer. Preparatory activities before fieldwork entailed a comprehensive review of extant remote sensing data, historical cartographic records, and relevant literature to classify land cover and delineate the predominant habitats within the park, thereby enhancing the contextual comprehension of the study.

The image processing technique that was used to detect burned scars area mapping is based on analysis of the remote sensing data time-series of multispectral satellite imagery from the National Aeronautics and Space Administration (NASA) archive by Moderate Resolution Imaging Spectroradiometer (MODIS) data ( Atta, 2007 ; Elmardi, 2009 ; García et al., 2014 ). Finally, a fire frequency map was prepared by using fire maps from each year from 2010 to 2020.

The Normalized Difference Vegetation Index (NDVI), recognized for its sensitivity to variations in green biomass, chlorophyll concentrations, and canopy water stress ( Liu et al., 2004 ), proved to be instrumental in evaluating vegetation health in relation to differing fire frequencies. A total of 150 samples were randomly obtained, evenly distributed across three categories of fire frequency: low (0 - 1 burn), moderate (2 - 5 burns), and high (6 - 11 burns). Utilizing QGIS, a shapefile of random points was generated employing the Random Points tool, ensuring a strategic distribution of points across the fire frequency map (refer to Figure 2 ). Vegetation samples were collected during the summer period when plant phenological activity reached its zenith.

Differences in species were determined by the total number of species per plot per year in each treatment, A total of 30 samples were selected randomly (10 samples from each fire frequency (categorical), A mathematical measure of species diversity in a given community measured by Shanon index & Simpson index Equation (1) and similarity by Somerson index Equation (2) (refer to Appendix 2).

The Shannon index has been a popular diversity index in the ecological literature, where it is also known as Shannon’s diversity index, Shannon-Wiener index, and (erroneously) Shannon-Weaver index ( Spellerberg & Fedor, 2003 ).

The Simpson index was introduced in 1949 by Edward H. Simpson to measure the degree of concentration when individuals are classified into types ( Simpson, 1949 ).

Sørensen’s similarity coefficient, is a statistic used for comparing the similarity of two samples. It was developed by the botanist Thorvald Sørensen and published in 1948 ( Sorensen, 1948 ).

Biodiversity evaluations incorporated several indices, including the Shannon Index (H), Simpson Index (D), Sorenson Index, Species Richness, and Equitability (evenness), to assess the impacts of varying fire frequencies on tree diversity, as elucidated by Sohier & Dronkers (2024) .

The fire frequency map showed that the riverine areas and Maya areas are the least burned areas due to the humidity of these places ( Figure 3 ). This reduced fire activity is attributed to higher humidity levels near water sources. On the other hand, these conditions promote enhanced vegetation growth, which increases the potential for wildfires over time. Conversely, areas along the park’s borders show low fire frequency, likely due to overexploitation by local inhabitants, leading to insufficient biomass to sustain annual fires.

The most vulnerable areas are in the middle of the park because they contain quantities of weeds. These maps could be used to determine priorities for areas to open fire lines in DNP.

The fire season normally starts in October and is extended until December, through these months the area that burned inside the park are different from year to year. ( Figure 4 ). This could be correlated with the annual precipitation, which

triggered the vegetation availability. this result is agreed with ( Zhang et al., 2016 ) who stated that the precipitation intensity and intermittency play an important role in the dynamics of wood vegetation cover and deep soil moisture. In arid and semiarid regions, as the annual precipitation increased, the rate of woody vegetation cover increased as a power-law function.

The statistical results showed that the maximum area burned in the park in 2011 was 12.8% and the minimum area burned in 2016 was 4.1%, and the results showed management activities were absent ( Figure 5 ).

The wildfire in the DNP burnt without any barrier, and it stops only if it is encountered by a natural obstacle such as a drainage system ( Figure 6 ). A fire usually starts in the north-eastern part of the park immediately after the rainy season because it is closer to the villages that surround the park and some residents of the villages surrounding the park start fires for hunting and collecting honey ( Elmardi, 2009 ), also the north side drier than the south area because of high amount of rainfall.

The graph showed that fire frequency has adverse consequences for tree biodiversity. There was a negative correlation between most of the tree spices and an increase in fire frequency, except Acacia polycantha (kakamout), Terminalia laxiflora (Subagh), Boscia senegalensis (Mukheit) and Anogeissus leiocarpus, there was a positive correlation, and Sterculia setiera and Acacia seyal were good with medium fire ( Figure 7 ).

Species labeled as “Resistant,” such as Acacia seyal, exhibit traits that allow them to withstand or recover from fire. For example, Hassan & Hassan (2013) observed that Acacia seyal remained unaffected by fires of varying intensity across three seasons. These species are characterized by thicker bark, deep root systems, or the ability to resprout post-fire. Notably, 23 out of 27 occurrences of this species were recorded in the “High” fire frequency category, indicating a strong adaptability to fire-prone environments. Eriksson et al. (2003) stated that the Acacia woodland was determined to be the most fire prone as well as fire resilient ecosystem.

Despite having the largest number of total occurrences (892), this species is labeled as having “No Clear Effect” regarding fire sensitivity or resistance. It has a significant presence in the High (301 tree), Medium (196 tree), and Low (395 tree) categories, suggesting it might be moderately adapted to fire. The lack of clear impact suggests this species could recover from fire but is not highly specialized for fire resilience.

These species are marked as good with “Medium Frequency” fire, meaning they are not highly sensitive but can adapt to a moderate level of fire disturbance. This suggests that while they may not thrive in high-fire areas, they can survive and regenerate in environments with periodic fires.

According to Armenteras et al. (2021) there 4 classes of responding pattern of tree species to fire fire-sensitive, fire survivors, fire thrivers, and fire-tolerant, so our classification of the response of the tree can be tailored to that classification. and this could be used in future management planning of the park.

It is obvious that the reactions to disturbances differed greatly among taxonomic groupings, and while several groups profited from disruption, numerous others were negatively damaged, probably due to various disturbance response techniques ( Viljur et al., 2022 ).

Diversity and evenness are two essential components for describing, measuring, and comparing biological diversity in forest communities ( Redowan, 2015 ). Overall, the measured indices the community subjected to low fire frequency is the most diverse compared with communities that witness more frequent fire incidents (medium and high frequency) ( Table 2 ). The Low Category has the highest equitability (E = 0.712), suggesting a more even distribution of species compared to the Medium and High categories. The Medium Category shows the lowest equitability, indicating dominance by fewer species.

Figures from the Shanon Index and Simpson Index showed that at low fire frequencies, there is higher biodiversity if they are compared to medium and high fire frequencies ( Table 1 ).

Table 1. Shanon index & simpson index.

Figures from the Sorenson index showed that between medium and high fire frequency (3.36) there is a higher similarity if they are compared to low and medium (0.6) and low and high fire frequency (0.81) ( Table 2 ).

The results showed that there had been a negative effect on the number of individual tree species when fire frequency had been increased, except for Acacia seyal, Acacia polycantha and Anogeissus leiocarpus, which had a positive and simulated effect.

The Sorenson index ( Table 2 ) showed that there were moderate similarities between the three communities ( Table 2 ), which indicates that the fire with its three frequencies has not made a distinct change in the composition of the park, The moderate similarity between communities suggests that these three fire regimes support somewhat overlapping species compositions. This could be due to shared environmental conditions or the adaptability of certain species to a broader range of fire frequencies. However, the differences in fire intensity and frequency likely result in some unique species adapted to different fire regimes scenarios, reducing complete overlap in community composition. And suggests that maintaining a balance in fire frequency could help conserve species shared across both regimes. This leads to the assumption that local species are adapted to historical disturbance regime, such regimes could be expected to support the highest levels of biodiversity ( Viljur et al., 2022 ).

The images from the study area show how the fire effect vegetation cover on the park.

The Sorensen-Dice coefficient, also known as Sorensen’s similarity coefficient.

Formula

Sorensen’s original formula was intended to be applied to discrete data. Given two sets, X and Y, it is defined as

$\text{DSC}=\frac{2\left|X\cap Y\right|}{\left|X\right|+\left|Y\right|}$

Equation (2) Somerson Index Equation

where “X” and “Y” are the cardinalities of the two sets (i.e., the number of elements in each set). The Sorensen index equals twice the number of elements common to both sets divided by the sum of the number of elements in each set.

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