Examining spatiotemporal land cover dynamics in relation to rainfall and temperature: 1992-2022. Case of Chizarira National Park, Zimbabwe

doi:10.21203/rs.3.rs-3607752/v1

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Examining spatiotemporal land cover dynamics in relation to rainfall and temperature: 1992-2022. Case of Chizarira National Park, Zimbabwe

https://doi.org/10.21203/rs.3.rs-3607752/v1

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Understanding the spatiotemporal dynamics of land cover is crucial for establishing effective monitoring and evaluation programs to assess ecosystem changes. This study explores the intricate land cover dynamics within Chizarira National Park (CNP), Zimbabwe, spanning the period from 1992 to 2022. It seeks to elucidate the complex interplay between climate variables, land cover changes, and human impacts within the park’s ecosystem. Utilizing Google Earth Engine (GEE), we acquired time series data for Normalized Difference Vegetation Index (NDVI), rainfall, and temperature, enabling us to map changes in five key land cover classes: water, trees, shrubs, grass, and bare land. Quantum Geographic Information System (QGIS) software facilitated further classification and the computation of area changes for each land cover class. Our analysis produced intriguing results. Mean annual Normalized Difference Vegetation Index (NDVI) exhibited fluctuations, reaching its peak in 2017 and lowest in 1995. Notably, we found no significant correlation between rainfall and NDVI (rho = 0.32, p = 0.77) or between temperature and NDVI (rho = -0.16, p = 0.45). These findings indicate that climate variables alone may not be sufficient to explain NDVI variations. Notably, CNP experienced woody encroachment, with shrubs developing into trees, potentially altering ecosystem services and land cover. Additionally, the study identified bare land in areas impacted by illegal anthropogenic activities, underscoring the need for stronger conservation enforcement. This research serves as a critical baseline for understanding CNP's complex ecological system, informing strategies for conservation, and guiding future studies aimed at preserving this unique national park. It emphasizes the importance of monitoring and addressing human-induced changes, exploring fine-scale temporal dynamics, and investigating the impacts of fires and wildlife population density on vegetation cover.

Land cover dynamics

Google Earth Engine

Normalised Difference Vegetation Index

Rainfall

Temperature

Vegetation and land cover dynamics play a crucial role in shaping ecosystem processes and services making them essential aspects of environmental management and conservation efforts (Dzawanda & Ncube, 2020). However, the effects of climate change and anthropogenic activities have profoundly impacted land cover patterns in recent decades, altering the stability and resilience of ecosystems globally. Understanding these dynamics and their causes is critical for developing successful conservation policies and practices for long-term land management.

Chizarira National Park (CNP) in Zimbabwe is characterized by Miombo woodlands. CNP's Miombo woodlands are predominantly deciduous, with dominant tree species experiencing brief leaf loss during the dry season, including Brachystegia spiciformis (Musasa), Julbernardia globiflora (Munhondo), Brachystegia boehmii (Mupfuti), Combretum collinum, Combretum molle (Mugodo), Terminalia sericea (Mususu), Combretum zeyheri, and Combretum apiculatum. While human-induced tree felling is more prevalent near the park boundaries, natural factors such as climatic events like droughts, floods, herbivory (Hanspach & Ha, 2021), frost, and fire (Dawson et al., 2016), along with overlapping anthropogenic activities, drive vegetation changes in CNP. Miombo woodlands are particularly susceptible to changes in rainfall and temperature, making CNP an interesting case study to examine the relationship between spatiotemporal land cover dynamics and climatic variables.

In recent years, there has been growing evidence of woody encroachment in African protected areas (Eastment, 2020). The drivers of woody encroachment in African protected areas remain a topic of ongoing debate (Devine et al., 2017). Climate fluctuations are thought to significantly influence the dynamics of vegetation because of its effect on the biophysical processes of plants such as respiration and photosynthesis (Garai et al., 2022). In CNP, woody vegetation has considerable impacts on tourism by reducing primary and secondary production in the ecosystem by shading and competing with other plants for light, water and food. The dense woody cover in Chizarira National Park (CNP) is negatively impacting game viewing experiences for tourists and hindering research efforts, particularly concerning elusive animals such as lions. Despite CNP's status as a national park, historical trends in vegetation cover in CNP have yet to be thoroughly explored. There has been a lack of initiatives to monitor and detect changes in vegetation since the production of the 1975 map. As a result, there is currently a lack of information regarding the dynamics, composition and structure of the park's vegetation. Given these circumstances, this study aims to assess vegetation dynamics in CNP, focusing on the period from 1992 to the present, in order to obtain a comprehensive understanding of the park's current vegetation status. This assessment will enable informed decisions regarding woodland management strategies for CNP.

The objectives of this study were (a) to determine the variations in mean annual Normalised Difference Vegetation Index (NDVI) in relation to mean annual precipitation (MAP) from 1992 to 2022 in CNP, (b) to assess the changes in mean annual Normalized Difference Vegetation Index (NDVI) in association with mean annual temperature (MAT) from 2000 to 2022 in CNP and (c) to map and determine decadal variations in land cover classes in CNP (1992, 2002, 2012, 2022).

The Normalized Difference Vegetation Index (NDVI) is a widely accepted and credible indicator of ground vegetation cover, derived from satellite data. It is commonly used for detecting vegetation cover dynamics due to its high sensitivity to changes in vegetation. In this study, NDVI will be utilized to represent vegetation cover as it serves as a sensitive indicator of climate influences on vegetation, altering energy balance, hydrological and biological cycles (Islam & Mamun, 2015). NDVI quantifies the quantity of vegetation using satellite data bands, providing an approximation of the vitality and density of vegetation at a pixel based on reflected sunlight from the visible (VIS) (0.4–0.7 m) and near infrared (NIR) (0.7–1.1 m) spectrums detected by the satellite sensors. This metric proves to be a vital tool for acquiring vegetation data and assessing the direct and indirect impacts of environmental change on vegetation at a large temporal and spatial scale.

This study holds substantial significance as it provides a historical context for understanding the current state of the ecosystem's vegetation in Chizarira National Park. By examining vegetation trends over time, this research offers valuable baseline data that can be utilized to establish monitoring and evaluation programs for assessing ecosystem dynamics. Identifying areas that have undergone degradation through historical vegetation analysis enables the prioritization of targeted restoration efforts (Brink et al., 2013). Moreover, by investigating the underlying causes of vegetation changes, this study will assist park managers in developing sustainable and effective management practices. Understanding the drivers behind these changes allows for the implementation of appropriate interventions to mitigate negative impacts and promote ecosystem health and resilience. In addition, this study's findings will provide policymakers with essential baseline information and empower them to make learned decisions and develop evidence-based policies. By incorporating scientific evidence on vegetation dynamics, policymakers can develop strategies that balance conservation objectives with socio-economic considerations, ensuring the long-term sustainability of Chizarira National Park and its surrounding communities.

Study area

Chizarira National Park (CNP) is the fourth largest national park in Zimbabwe which covers 1910 km2 of the Zambezi valley's southern escarpment. It slopes south to the Sengwa basins with an altitude of 760 meters to 1445 meter. CNP is part of the Kavango/Upper Zambezi Trans-Boundary Conservation Area (KAZA-TFCA) where Botswana, Zimbabwe, Angola, Zambia and Namibia converge (KAZA-TFCA, 2022). This park, of which a portion was first designated as a non-hunting reserve in 1958, was later expanded in 1968 and designated as a national park in 1975 (Zimbabwe Parks and Wildlife Authority 2009). On the southern side of Lake Kariba, it is located about 80 kilometres to the southeast of the little town of Binga. The Chirisa Safari Area and the Binga, Gokwe, and Manjolo Communal Lands form the park's southern and western boundaries, respectively. It lies within Binga District in Matabeleland North Province, forms part of the Sebungwe region and found in natural region 4.

Climate

The region has three distinct seasonal conditions which are cool and dry from May to July, hot and dry from August to October; and hot and wet from November to April. During the hot, dry season, temperatures can reach 40°C. In the river valleys during the winter, it is below zero degrees Celsius. A single rainy season lasting from November to April renders about 700 mm of precipitation in a year.

Geology, Landscape Features and Vegetation

The landscape is rough and full of mountains that have been extensively cut by gorges and ravines that can reach depths of 460 meters. The Zambezi valley is 500 meters away from the Chizarira cliff. It supports luvisol bedrock which is made up of rather poor soils typical of miombo woods. The park's main watershed gives rise to several grassy vleis, springs like Manzituba and Muchininga, and streams. Mopane Colophospermum woodland, Combretum/Commiphora thickets, and riparian Acacia/Trichilia woodland/forest are some other vegetation types (International BirdLife, 2023). Figure 1 below depicts location of the study area.

Data acquisition

Google Earth Engine was used to implement the majority of the study's data acquisition and part of analysis. Random Forest classification with the classifier package was used for classification because it is the most effective technique in terms of consistency and accuracy even when applied to the analysis of data with higher noise levels (Tsai et al., 2018). Random forest classifier (RF) achieves more classification accuracy than other methods and it has become common in recent years. It can handle uncompromising data, irregular data, or missing values. This classifier also provides the relative value of various features throughout the classification process, which can be helpful when choosing features. Therefore, land cover studies can be very easily conducted by using an RF classifier (Thakur et al., 2022). Datasets used are LANDSAT/LC08/C02/T1_RT_TOA for 2013 and 2022, and Landsat-5 TM for 1992 and 2002. Landsat 5 was used for 2002 and Landsat 8 for 2013 because the Landsat-7 ETM data had been damaged by stripping (Aldiansyah et al., 2021). Dates were filtered from 01 May to 30 October to avoid rain season to increase the accuracy of detecting real water bodies.

The Moderate Resolution Imaging Spectroradiometer (MODIS) dataset (MYD13Q1 V6.1 product), which offers an NDVI value on a per-pixel basis, was used to extract NDVI. This dataset was utilized since it computes NDVI using bi-directional surface reflectance that has been atmospherically adjusted and masked for water, clouds, heavy aerosols, and cloud shadows. Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) provided the rainfall time series data. This dataset, which covers more than 30 years, creates gridded rainfall time series for trend analysis and seasonal drought monitoring by fusing in-situ station data with 0.05° resolution satellite images. It is possible to regard the employed high-resolution CHIRPS rainfall data to be a trustworthy representation of the locally observed rainfall (Funk et al., 2015). The MOD11A2 V6.1, which gives an average 8-day land surface temperature (LST) on a grid of 1200 x 1200 kilometres was used to derive land surface temperature time series data (Huang et al., 2021). This dataset is available from 2000, therefore LST data is available from this date. The data collected was filtered according to the research area (CNP) and the relevant dates (1992–2022). The mean value for each year was determined by extracting the monthly temperature, NDVI, and precipitation time series (García et al., 2020).

Data processing

In pre-processing, filter Date was first performed to produce an image collection with a cloud cover of less than 1%. Identification of vegetation cover types was done using Google Earth, geometry drawing tools were used to collect training data and the classification obtained from this method comprises of five classes which are water, trees, shrubs, grass and bare land. A large region of interest was selected for collecting training points and the imagery was cut using Chizarira National park boundary after getting the best imagery (Aldiansyah et al., 2021). Further classification of decadal imagery as well as calculating area covered by each land cover class was done through Quantum Geographic Information System (QGIS) version 3.22.1.

Data analysis

In order to discover inter-annual correlation between NDVI, rainfall and temperature, the annual mean values in each year were selected for each variable with NDVI representing vegetation cover. Mean annual NDVI, mean annual temperature (MAT), and mean annual precipitation (MAP) were utilized, each of which provided a single mean value across the 30 years (García et al., 2020). Because time series datasets were small, the Shapiro-Wilk test was used to determine if the data satisfied the criteria of parametric or non-parametric statistical analysis techniques. MAP and MAT were found to be normally distributed (p = 0.20), (p = 0.44) respectively, while mean annual NDVI was not normally distributed (p = 0.004). Consequently, the Mann-Kendall (MK) test was performed to determine whether the NDVI, rainfall, and temperature trends in CNP changed significantly throughout the study period. The MK test is a straightforward, non-parametric technique that can find patterns in time series data even in the presence of missing values, seasonality, and values below the detection threshold (Chapungu & Nhamo, 2016). To test the patterns and response of NDVI trends to rainfall, a Spearman rank correlation (rho) was conducted (Brandt et al., 2017). This method was chosen because it does not require sample data to obey a certain distribution and is less affected by outliers therefore, it can be used to efficiently examine the relationship between NDVI and climate variables time series (Precipitation et al., 2019). Data analysis was done in R studio version 4.2.3.

Variation in mean annual Normalized Difference Vegetation Index

The mean annual NDVI in CNP has increased from 1992 to 2022 reaching its highest value of 0.54 in 2017 and its lowest value of 0.24 in 1995 (Fig. 2). The modified Mann-Kendall test show that NDVI changes were statistically significant (p < 0.001) (Fig. 2).

Variations in mean annual rainfall

The highest mean annual precipitation (MAP) in CNP for the study period (1992 to 2002) was 74.63mm in 1997 while the lowest was 32.9mm in 2019 (Fig. 3). The Modified Man-Kendall test indicated that there were no statistically significant changes in rainfall (p = 0.13) from 1992 to 2022 in CNP as depicted in Fig. 3.

Variations in mean annual temperature

The highest mean annual temperature (MAT) in CNP for the study period (1992 to 2022) was 33.95⁰C in 2004 and the lowest mean was 30.07⁰C in 2016 (Fig. 4). The Modified Mann- Kendall test show that mean annual temperature changes were not statistically significant (p = 0). Figure 4 is a graphical presentation of MAT trend from 2000 to 2022.

The response of mean annual NDVI to mean annual precipitation

Spearman Rank correlation results indicated that there was no significant correlation between mean annual NDVI and mean annual rainfall (rho = 0.32, p = 077). Figure 5 below indicates the graphical presentation of the response of mean annual NDVI to mean annual precipitation

Association between mean annual NDVI and mean annual temperature.

The results from Spearman Rank Correlation test indicated a non-significant correlation between mean annual NDVI and mean annual land surface temperature (rho= -0.16, p = 0.45). Figure 6 illustrates the correlation between mean annual NDVI and mean annual temperature.

Variations in land cover classes

Table 2 below summarises the area (in hectors) covered by each land cover class in each decade and the p-values obtained from analysis of variance test.

Table 2

Area cover covered by each land cover class in each decade.
Year	1992	2002	2012	2022
Water	22643.82	2070.586	1626.26	4236.859
Trees	79086.84	44615.35	55650.55	105351.7
Shrubs	19135.75	38161.49	22906.5	18826.89
Grass	71969.7	46896.73	27903.97	39069.97
Bare land	1947.957	63038.92	86696.8	27298.62

Figure 7 demonstrates changes in the percentage cover for each land cover class after each decade. The results show that tree cover declined from 40–23% during 1992 to 2002 period and then started to increase from there to 2022 where tress cover was 53% of the CNP land. Shrubs also show changes from 10%, 20%, 12% and 10% from 1992 to 2022 respectively. In 1992 bare land covered only 1% of the park and increase to 32% by 2002, 44% by 2012 and the declined to 14% in 2022. Grass cover was also fluctuating as it was 37% in 1992, 24% in 2002, 14% in 2012 and finally 20% in 2022.

Figure 8 depicts land cover maps which shows spatial decadal shifts in the balance between land cover classes. From the visual interpretation from 1992 to 2002, It can be seen that a larger portion of grass land and water at the central of the park was turned into bush land. Some tree cover and water bodies were lost to grass land in the southern part of the park. From 2002 map to 2012, bare land has increased in the southern, central to north eastern parts of the park. This is also accompanied with increase in tree cover and water bodies in the park. From 2012 to 2022, we can notice a decline in water bodies around the park. Bare land also reduced in the central and north eastern parts but remained and increased in the southern part of the park as well as at the park peripheral in the east. There is also a decline in water bodies and a significant increase in tree cover in larger parts of the park during this decade.

The results of this study provide valuable insights into the complex interplay between climate variables, land cover changes, and human impacts within Chizarira National Park (CNP) over the period from 2002 to 2022. Notably, the study revealed that the Normalized Difference Vegetation Index (NDVI) exhibited no significant correlation with mean annual rainfall or mean annual temperature during this timeframe. These findings appear to challenge conventional wisdom, as previous studies, e.g. Ayanlade et al., (2021) and Garai et al., (2022) have established a positive relationship between high rainfall and increased vegetation cover. Furthermore, these studies highlighted how enhanced vegetation cover can contribute to reducing land surface temperatures through processes like evapotranspiration, shade provision, and photosynthesis, all of which effectively absorb solar radiation. Despite these established associations, our study's findings suggest that high rainfall alone may not invariably result in heightened vegetation cover, nor does it necessarily lead to the moderation of land surface temperatures. The observed insignificant correlation between NDVI and temperature underscores the notion that multiple, intricate factors are at play in shaping NDVI variations (Forest et al., 2022).

Additionally, the study identified notable changes in land cover classes over the decades. Between 1992 and 2002, there was an evident increase in shrubland, accompanied by a decline in tree and grass cover. During this period, a reduction in water bodies was also observed, contributing significantly to a substantial increase in bare land from 1–39%. This shift in land cover could be attributed, in part, to the drought conditions experienced in 2002, which exerted considerable influence over the park's ecological dynamics. Such climatic events can drive land cover changes, particularly the expansion of bare land, in response to diminished water resources.

One noteworthy trend was the transformation of shrublands into tree-dominated areas from 2002 onwards. This transition suggests a process of woody encroachment within CNP, whereby shrubs gradually developed into mature trees. Such woody encroachment carries multifaceted ecological implications, including potential alterations in ecosystem services. Increasing woody species cover may have contributed to reduction in water as a result of increased water intake by interception, which immediately decreases stream flow and soil water levels (Eastment, 2020). As woody species proliferate, they can influence various aspects of the park's ecology, such as climate regulation and water resource management. These changes may, in turn, affect tourism experiences, biodiversity, overall ecosystem functioning, as well as primary and secondary production within the park.

While the increase in vegetation cover has contributed to the reduction of bare land by 2022, it is essential to note that substantial portions of bare land persist, primarily in the southern region of the park and near its eastern boundary. These areas are particularly susceptible to illegal anthropogenic activities, highlighting the urgent need for enhanced conservation enforcement measures to safeguard the integrity of CNP's ecosystem.

This study reveals several significant findings regarding the dynamics of land cover and its relationship with climate variables in Chizarira National Park (CNP). Firstly, our analysis demonstrates that the Normalized Difference Vegetation Index (NDVI) does not exhibit a substantial response to variations in rainfall, despite observing an increase in both mean annual NDVI and mean annual rainfall from 1992 to 2022. This suggests that other factors beyond precipitation alone influence NDVI patterns in CNP. A notable trend identified in this study is the ongoing process of woody encroachment within the park. Over time, shrublands have transitioned into tree-dominated areas, resulting in CNP becoming predominantly covered by trees. This phenomenon has led to a decrease in water bodies and an increase in bare land, particularly in the southern part of the park and along its boundary. These land cover changes have implications for the park's ecosystem, necessitating further investigation into their ecological impacts.

To enhance our understanding of CNP's dynamic ecosystem, we recommend conducting future studies at finer temporal scales, recognizing that vegetation and ecosystem dynamics can manifest over relatively short periods. Such investigations should also focus on the delicate balance between grass and woody vegetation within the park. Additionally, it is imperative to assess the influence of overlapping anthropogenic activities on vegetation cover, especially in the park's peripheral areas. Considering the potential impacts of fires and elephant population density on natural environments, further research should explore how these factors influence spatiotemporal vegetation cover in Chizarira National Park. Moreover, the continuation of prescribed burning practices, which can effectively mitigate the density of shrubs and trees, should be considered to prevent excessive woody vegetation encroachment and promote grass growth.

Acknowledgements

Special thanks go to African Lion and Environmental Research Trust (ALERT), Zimbabwe for all the advice, knowledge, training, and guidance in data collection during the industrial attachment period. Special remarks go to Dr N. Monks the Director Research and Conservation Mr Isaac Mudimba, the principal Researcher. I would also like to express my gratitude to Chinhoyi University of Technology, Department of Wildlife Ecology and Conservation staff for their lectures, advice, guidance and motivation.

Author contribution Synthesis of ideas was done by T. Jeche. T. Jeche and T Mwedzi participated in data collection, analysis and writing up.

Funding No finding was received for conducting this study.

Data availability The datasets of the study are available from corresponding authors on reasonable request

Ethics approval We did not need ethical approval in conducting the study.

Competing interests The authors declare no competing interests

Aldiansyah, S., Dwi, M., & Mannesa, M. (2021). MONITORING OF VEGETATION COVER CHANGES IN RELATIONSHIP TO GEOMORPHOLOGICAL FORMS USING GOOGLE EARTH ENGINE IN KENDARI CITY. 21(2).
Ayanlade, A., Jeje, O. D., Nwaezeigwe, J. O., Orimoogunje, O. O. I., & Olokeogun, O. S. (2021). Rainfall seasonality effects on vegetation greenness in different ecological zones. Environmental Challenges, 4(January), 100144. https://doi.org/10.1016/j.envc.2021.100144
Brandt, M., Tappan, G., Diouf, A. A., Beye, G., Mbow, C., & Fensholt, R. (2017). Woody vegetation die off and regeneration in response to rainfall variability in the west african sahel. Remote Sensing, 9(1). https://doi.org/10.3390/rs9010039
Brink, A. B., Bodart, C., Buchanan, G., Clerici, M., Donnay, F., Eshiamwata, G., Gregoire, J. M., Kirui, B. K., Lupi, A., Palumbo, I., & Pekel, J. F. (2013). Vegetation. In Developments in Earth Surface Processes (Vol. 16). https://doi.org/10.1016/B978-0-444-59559-1.00012-8
Chapungu, L., & Nhamo, L. (2016). An Assessment of the Impact of Climate Change on Plant Species Richness Through an Analysis of the Normalised Difference Water Index (NDWI) in Mutirikwi Sub-catchment, Zimbabwe. 5(2).
Dawson, T. P., Pettorelli, N., Wegmann, M., Skidmore, A., Sander, M., Fernandez, M., Lucas, R., Schaepman, M. E., Wang, T., Connor, B. O., Jongman, R. H. G., Kempeneers, P., Sonnenschein, R., Leidner, A. K., He, K. S., Nagendra, H., Paganini, M., Klerk, H. M. De, Asner, G. P., … Penner, J. (2016). Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. 1–10. https://doi.org/10.1002/rse2.15
Devine, A. P., McDonald, R. A., Quaife, T., & Maclean, I. M. D. (2017). Determinants of woody encroachment and cover in African savannas. Oecologia, 183(4), 939–951. https://doi.org/10.1007/s00442-017-3807-6
Dzawanda, B., & Ncube, S. (2020). An assessment of vegetation cover changes and soil erosion hazard in muzvezve sub-catchment area, Zimbabwe. African Geographical Review, 00(00), 1–18. https://doi.org/10.1080/19376812.2020.1856156
Eastment, C. (2020). How has woody vegetation changed in north-east Namibia in response to land use, climate and fire ? February.
Forest, P., Miltiadou, M., & Danezis, C. (2022). Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations — a new environmental record for monitoring extremes. 1–21. https://doi.org/10.1038/sdata.2015.66
Garai, S., Khatun, M., Singh, R., Sharma, J., Pradhan, M., & Ranjan, A. (2022). Assessing correlation between Rainfall, normalized difference Vegetation Index (NDVI) and land surface temperature ( LST ) in Eastern.
García, M., Isla, C., Lehmann, C. E. R., Stevens, N., & Bjorkman, A. D. (2020). Woody plant encroachment intensifies under climate change across tundra and savanna biomes. January, 1–19. https://doi.org/10.1111/geb.13072
Hanspach, J., & Ha, W. (2021). Woody plant species diversity as a predictor of ecosystem services in a social – ecological system of southwestern Ethiopia. 0123456789, 373–391. https://doi.org/10.1007/s10980-020-01170-x
Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1
Islam, M. M., & Mamun, M. M. I. (2015). Variations of NDVI and Its Association with Rainfall and Evapotranspiration over Bangladesh. Rajshahi University Journal of Science and Engineering, 43, 21–28. https://doi.org/10.3329/rujse.v43i0.26160
Precipitation, A., Zone, F., Li, Y., Xie, Z., Qin, Y., & Zheng, Z. (2019). Estimating Relations of Vegetation, Climate Change, and Human Activity : A Case Study in the 400 mm.
Thakur, S., Samant, S. S., & Singh, R. K. (2022). Assessment of decadal land use land cover change using random forest classifier in Google Earth engine for Himachal Pradesh, India. October 2022, 36. https://doi.org/10.1117/12.2636247
Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L., & Shi, L. (2018). Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine. https://doi.org/10.3390/rs10060927

No competing interests reported.

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Examining spatiotemporal land cover dynamics in relation to rainfall and temperature: 1992-2022. Case of Chizarira National Park, Zimbabwe

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Abstract

Figures

Introduction

Methodology

Climate

Geology, Landscape Features and Vegetation

Data analysis

Results

Discussion

Conclusion and Recommendations

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References

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