Mapping floristic communities in Southern Africa savannas, Mozambique

Aurélio de Jesus Rodrigues Pais Natasha Sofia Ribeiro Rubens Manoel dos Santos About the authors

Abstract

The present study was carried out in Limpopo National Park (LNP) with the objective to map floristic communities that occur inside the park. Three (3) Landsat 8 satellite images were obtained by Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) tools and were pre-processed and classified, culminating in six (6) types of land use and cover. The floristic survey consisted of stratified sampling in three (3) main LNP landscapes types, namely landscape of limestone soils, landscape of rhyolite shallow soils and landscape of deep sandy soils. The final map presents 13 floristic communities identified by the names of greatest value of ecological importance (IVI) species. The community of Terminalia sericea / Combretum apiculatum / Guibourtia conjugata / Colophospermum mopane presented a larger occurrence area. Colophospermum mopane was the most representative species among the mapped communities, mostly occurring in association with other species. The overall accuracy was 74% and the Kappa index was 68%, thus giving a good rating. The mapping also showed that human occupied areas are larger than some floristic communities areas, so we suggest that these smaller communities, should be given priority actions for their conservation, especially those without human occupation.

Key words:
floristic community; land cover; Limpopo National Park; mapping

Resumo

O presente estudo foi realizado no Parque Nacional do Limpopo (PNL), com o objetivo de mapear comunidades florísticas que ocorrem no parque. Foram obtidas três (3) imagens do satélite Landsat 8 com os sensores Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), para as quais foi feito o pré-processamento e a classificação, culminando em seis (6) tipos de uso e cobertura de terra. O levantamento florístico consistiu numa amostragem estratificada por paisagem, nomeadamende paisagem de solos calcários, paisagem de solos rasos de riolito e paisagem de solos arenosos profundos. O mapa final apresenta 13 comunidades florísticas identificadas pelos nomes das espécies de maior valor de importância ecológica (VI). A comunidade de Terminalia sericea / Combretum apiculatum / Guibourtia conjugata / Colophospermum mopane apresentou maior área de ocorrência. Colophospermum mopane foi a espécie mais representativa entre as comunidades mapeadas, ocorrendo em maior parte associada com outras espécies. A exatidão global foi de 74% e o índice Kappa foi 68%, atribuindo assim um nível de boa classificação. O mapeamento também demonstrou que áreas de ocupação humana são maiores que as áreas de cobertura de algumas comunidades florísticas, deste modo sugerimos que estas comunidades menores sejam priorizadas na conservação, especialmente aquelas que não apresentam ocupação humana.

Palavras-chave:
comunidade florística; cobertura de terra; Parque Nacional de Limpopo; mapeamento

Introduction

Over the last century, a significant loss of forest cover has occurred at the global level due to anthropogenic activities such as agriculture, urbanization, mining and logging, jeopardizing biodiversity conservation (Geist & Lambin 2002Geist HJ & Lambin EF (2002) Proximate causes and underlying driving forces of tropical deforestation. Bioscience 52: 143-150.; Díaz et al. 2006Díaz S, Fargione J, Chapin III FS & Tilman D (2006) Biodiversity loss threatens human well-being. PLoS biology 4: 277.; Bunting et al. 2010Bunting P, Lucas RM, Jones K & Bean AR (2010) Characterisation and mapping of forest communities by clustering individual tree crowns. Remote Sensing of Environment 114: 2536-2547.). Forest vegetation provides habitat for several living beings and plays a key role in affecting global climate (Xiao et al. 2004Xiao XM, Zhang Q, Braswell B, Urbanskib S, Bolesa S, Wofsyb S, Moore III B & Ojima D (2004) Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment 91: 256-270.; Huang et al. 2008Huang C, Song K, Kim S, Townshend JR, Davis P, Masek JG & Goward SN (2008) Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sensing of Environment 112: 970-985.; Lucas et al. 2008Lucas R, Bunting P, Paterson M & Chisholm L (2008) Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment 112: 2088-2103.). Furthermore, biodiversity ecosystem services which are closely linked to well-being and human society subsistence are also threatened (Rikimaru et al. 2002Rikimaru A, Roy PS & Miyatake S (2002) Tropical forest cover density mapping. Tropical Ecology 43: 39-47.; Ploton et al. 2012Ploton P, Pélissier R, Proisy C, Flavenot T, Barbier N, Rai SN & Couteron P (2012) Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecological Applications 22: 993-1003.). For instance, forest resources are well known for being an integral component of a society’ economy and the environment (Chazdon 2008Chazdon RL (2008) Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320: 1458-1460.; Bullock et al. 2011Bullock JM, Aronson J, Newton AC, Pywell RF & Rey-Benayas JM (2011) Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends in ecology & evolution 26: 541-549.). These provide several products and ecosystem services such as food, fiber, wood and nutrient cycling (Isbell et al. 2011Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB & Weigelt A (2011) High plant diversity is needed to maintain ecosystem services. Nature 477: 199.; Ploton et al. 2012Ploton P, Pélissier R, Proisy C, Flavenot T, Barbier N, Rai SN & Couteron P (2012) Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecological Applications 22: 993-1003.), and thus play a vital role in maintaining ecological balance (Roy & Joshi 2002Roy PS & Joshi PK (2002) Forest cover assessment in north-east India - the potential of temporal wide swath satellite sensor data (IRS-1C WiFS). International Journal of Remote Sensing 23: 4881-4896.; Rai 2012Rai RK, Scarborough H, Subedi N & Lamichhane B (2012) Invasive plants-Do they devastate or diversify rural livelihoods? Rural farmers’ perception of three invasive plants in Nepal. Journal for nature conservation 20: 170-176.). Growing awareness that forests provide multiple benefits to humankind has created a global concern for their protection and conservation (Roy & Joshi 2002Roy PS & Joshi PK (2002) Forest cover assessment in north-east India - the potential of temporal wide swath satellite sensor data (IRS-1C WiFS). International Journal of Remote Sensing 23: 4881-4896.; Balvanera et al. 2006Balvanera P, Pfisterer AB, Buchmann N, He JS, Nakashizuka T, Raffaelli D & Schmid B (2006) Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecology letters 10: 1146-1156.; Bullock et al. 2011Bullock JM, Aronson J, Newton AC, Pywell RF & Rey-Benayas JM (2011) Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends in ecology & evolution 26: 541-549.; Isbell et al. 2011Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB & Weigelt A (2011) High plant diversity is needed to maintain ecosystem services. Nature 477: 199.).

Vegetation mapping and classification are important tools to protect, conserve, and manage natural resources. Thus, mapping and delimiting conservation areas are fundamental for conserving and restoring natural ecosystems (Myers et al. 2000Myers NRA, Mittermeier CG, Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403: 853-858.; Redford et al. 2003Redford KH, Coppolillo P, Sanderson EW, Da Fonseca GA, Dinerstein E, Groves C & Olson D (2003) Mapping the conservation landscape. Conservation biology 17: 116-131.; Huang et al. 2008Huang C, Song K, Kim S, Townshend JR, Davis P, Masek JG & Goward SN (2008) Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sensing of Environment 112: 970-985.; Lucas et al. 2008Lucas R, Bunting P, Paterson M & Chisholm L (2008) Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment 112: 2088-2103.). Mapping in conservation units currently presents several steps, such as the use of remote sensing and floristic surveying (Adam et al. 2010Adam E, Mutanga O & Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management 18: 281-296.). This information is mainly applied to landscape planning and biodiversity conservation (Rapinel et al. 2018Rapinel S, Rossignol N, Hubert-Moy L, Bouzillé JB & Bonis A (2018) Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty. Applied vegetation science 21: 678-693.; Adam et al. 2010Adam E, Mutanga O & Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management 18: 281-296.; Hasmadi et al. 2010Hasmadi MI, Zaki MH, Adnan IA, Pakhriazad HZ & Fadlli MA (2010) Determining and mapping of vegetation using GIS and phytosociological approach in Mount Tahan, Malaysia. Journal of Agricultural Science 2: 80.). It has also generally been used to find spatial plant distribution, disturbances, identify animal species habitat, among other factors (Wagner et al. 2000Wagner HH, Wildi O & Ewald KC (2000) Additive partitioning of plant species diversity in an agricultural mosaic landscape. Landscape Ecology 15: 219-227.; Moreno & Halffter 2001Moreno CE & Halffter G (2001) Spatial and temporal analysis of a, b, and g diversities of bats in a fragmented landscape. Biodiversity and Conservation 10: 367-382.).

Remote sensing is a technique which captures data from an object without any physical contact through its spectral response and electromagnetic radiation, and thus is a good alternative to map areas (Beeri et al. 2007Beeri O, Phillips R, Hendrickson J, Frank AB & Kronberg S (2007) Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment 110: 216-225.; Xie et al. 2008Xie Y, Sha Z & Yu M (2008) Remote sensing imagery in vegetation mapping: a review. Journal of plant ecology 1: 9-23.). Vegetation physiognomy maps can be generated from satellite sensor data (i.e. acquired by SPOT or LandSat sensors) by applying supervised or unsupervised pixel classification techniques (Wang & Boesch 2007Wang Z & Boesch R (2007) Color and texture-based image segmentation for improved forest delineation. Transactions on Geoscience and Remote Sensing 45: 3055-3062.; Reddy et al. 2016Reddy CS, Jha CS, Dadhwal VK, Krishna PH, Pasha SV, Satish KV, Dutta K, Saranya KRL, Rakesh F, Rajashekar G & Diwakar PG (2016) Quantification and monitoring of deforestation in India over eight decades (1930-2013). Biodiversity and Conservation 25: 93-116.; Hansen et al. 2013Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SAA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini1 L, Justice CO & Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342: 850-853.). In addition, more detail can be added to physiognomy maps by adding floristic composition information, such as the importance value index of species (Rapinel et al. 2018Rapinel S, Rossignol N, Hubert-Moy L, Bouzillé JB & Bonis A (2018) Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty. Applied vegetation science 21: 678-693.). These data can be obtained from phytosociological surveys and analysis (Hasmadi et al. 2010Hasmadi MI, Zaki MH, Adnan IA, Pakhriazad HZ & Fadlli MA (2010) Determining and mapping of vegetation using GIS and phytosociological approach in Mount Tahan, Malaysia. Journal of Agricultural Science 2: 80.; Kurtz et al. 2018Kurtz BC, Magalhães AM, Seabra VDS, Richter M & Caris EAP (2018) Integrating remote sensing and phytosociology of the Atlantic Forest to map a small continental island in southeastern Brazil: subsidies to protect the habitat of critically endangered species. Rodriguésia 69: 2081-2092.). Vegetation classification by using remote sensing systems has been an indispensable tool in the process of creating an updated vegetation map, as well as a basic requirement for monitoring landscapes (Foody & Cutler 2003Foody GM & Cutler MEJ (2003) Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. Journal of Biogeography 30: 1053-1066.; Giri et al. 2011Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J & Duke N (2011) Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography 20: 154-159.).

In this work remote sensing mapping methods combined with floristic surveys were conducted regarding Mopane formations in a Protected Area in Limpopo National Park (LNP). Uncontrolled fires in LNP related to shifting cultivation practices, hunting small animals, and firewood (among others) are the main threats to biodiversity and habitats, as well as changing floristic landscape patterns of the region (Grossman & Holden 2002Grossman D & Holden P (2002) Parque Nacional do Limpopo, management and development plan. Unpublished report to the Peace Parks Foundation. Peace Parks Foundation, Stellenbosch. 127p.; Milgroom & Spierenburg 2008Milgroom J & Spierenburg M (2008) Induced volition: resettlement from the Limpopo National Park, Mozambique. Journal of Contemporary African Studies 26: 435-448.; Witter 2013Witter R (2013) Elephant-induced displacement and the power of choice: moral narratives about resettlement in Mozambique’s Limpopo National Park. Conservation and Society 11: 406-419.; Everatt et al. 2014Everatt KT, Andresen L & Somers MJ (2014) Trophic scaling and occupancy analysis reveals a lion population limited by top-down anthropogenic pressure in the Limpopo National Park, Mozambique. PloS one 9: 99389.). Despite this, mapping vegetation and human occupation is very important because maps can be used to plan and manage landscapes.

Thus, the present study aims to map floristic communities in LNP for the year 2014, with the following hypotheses: (i) all floristic communities are larger than the human occupation areas in the park; and (ii) Colophospermum mopane is distributed in all floristic communities, showing evidence of wide niche amplitude of this species in the ecosystem.

Material and Methods

Study area

The Mopane ecoregion extends for about 555,000 km2, thus forming a generalized type of vegetation covering areas in Angola, Botswana, Namibia, Zimbabwe, Zambia, Mozambique, Malawi and South Africa (Timberlake et al. 2010Timberlake J, Chidumayo E & Sawadogo L (2010) Distribution and characteristics of African dry forests and woodlands. The dry forest and woodlands of Africa: managing for products and services. Earthscan, New York. Pp. 11-42.; Makhado et al. 2012Makhado RA, Martin JP, Wessels DCJ, Saidi AT & Masehela KK (2012) Use of mopane woodland resources and associated woodland management challenges in rural areas of South Africa. Ethnobotany Research and Applications 10: 369-379.). Limpopo National Park (LNP) is one of the largest natural parks in Mozambique, extending around ​​10,000 km2, and is also considered the largest national conservation area of the Mopane ecosystem (Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.; Marzoli 2007Marzoli A (2007) Inventário Florestal Nacional: avaliação integrada das florestas de Moçambique. Direção Nacional de Terra e Florestas, Ministério da Agricultura, Maputo. 92p.). Few floristic surveys have been carried out in the park partly due to difficulties of access (Lunstrum 2008Lunstrum E (2008) Mozambique, neoliberal land reform, and the Limpopo National Park. Geographical Review 98: 339-355.; MITUR 2003MITUR - Ministério do turismo (2003) Plano de maneio e desenvolvimento do Parque Nacional do Limpopo. Moçambique. DINAC, Maputo. 33p.; Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.; Everatt et al. 2014Everatt KT, Andresen L & Somers MJ (2014) Trophic scaling and occupancy analysis reveals a lion population limited by top-down anthropogenic pressure in the Limpopo National Park, Mozambique. PloS one 9: 99389.).

LNP is part of the Greater Limpopo Transfrontier Park (PTGL), which joins Kruger National Park (PNK) in South Africa, and Gonarezhou National Park (PNG) in Zimbabwe. The Mozambican part of the cross-border Park is located in north of the Gaza Province, southern region of Mozambique (MITUR 2003MITUR - Ministério do turismo (2003) Plano de maneio e desenvolvimento do Parque Nacional do Limpopo. Moçambique. DINAC, Maputo. 33p.; Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.). The park is located in a semi-arid climate, BSh and BWh according to the Koppen classification. The maximum average diurnal temperature increases from South to the North, with an absolute maximum above 40 °C in November to February. The average annual precipitation varies from 360 mm in the northern part to more than 500 mm west of the park (Rubel & Kottek 2010Rubel F & Kottek M (2010) Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorologische Zeitschrift 19: 135-141.).

Three main landscapes cover the park, namely: (i) Landscape of deep sandy soils: also conventionally designated by Nwambia sandveld, covers approximately 458,641 ha (41.1%) of the LNP area, extending from the northwest border with the Kruger National Park (KNP) in a downward direction towards the confluence of the Limpopo and Olifants rivers. It is characterized by sandy substrates, including deep reddish soils and interior dunes with a pH ranging from 4.3 to 5.5 (Van Rooyen et al. 1981Van Rooyen N, Theron GK & Grobbelaar N (1981) A floristic description and structural classification of the plant communities of the Punda Maria-Pafuri-Wambiya area in the Kruger National Park, Rep. of S.A. Journal of South African Botany 47: 213-246.). There is also vegetation with few trees and shrubs mostly ranging between 2 and 4 m high (Gertenbach 1983Gertenbach WPD (1983) Landscapes of the Kruger National Park. Koedoe 26: 9-121.; Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.). (ii) Landscape of limestone soils: also conventionally designated by Mopane on calcrete, covers 415,890 ha (38.8%) of the LNP area and is distributed on both sides of the Shingwedzi River Valley, particularly in steep slopes and limestone ravines (Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.). Soils are shallow, mainly consisting of limestone, with about 10% of the soil surface covered by rocks and pH ranging from 7.9 to 8.4 (Van Rooyen et al. 1981Van Rooyen N, Theron GK & Grobbelaar N (1981) A floristic description and structural classification of the plant communities of the Punda Maria-Pafuri-Wambiya area in the Kruger National Park, Rep. of S.A. Journal of South African Botany 47: 213-246.). (iii) Landscape of rhyolite shallow soils: also conventionally designated by Lebombo north, occurs in the West of LNP along the Shingwedzi River covering about 39,878 ha (3.5%) of the park (Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.). The surface layer is extremely stony with shallow soils derived from rhyolite, with about 80% being covered by stones and rocks, and in many cases it is not considered as a soil type (Van Rooyen et al. 1981Van Rooyen N, Theron GK & Grobbelaar N (1981) A floristic description and structural classification of the plant communities of the Punda Maria-Pafuri-Wambiya area in the Kruger National Park, Rep. of S.A. Journal of South African Botany 47: 213-246.; Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.).

One motivation to study the chosen area was the dependence on natural resources as a subsistence means for the population living within the LNP being extremely high, which translates into increasing pressure on the ecosystem and its long-term sustainability (MITUR 2003MITUR - Ministério do turismo (2003) Plano de maneio e desenvolvimento do Parque Nacional do Limpopo. Moçambique. DINAC, Maputo. 33p.; Stalmans et al. 2004Stalmans M, Gertenbach WPD & Carvalho-Serfontein F (2004) Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique. Koedoe 47: 61-81.).

Acquiring satellite imagery and pre-processing

The satellite images were provided from Landsat 8 satellite (OLI and TIRS sensors) free of charge from the United States Geological Survey (USGS) website (USGS 2013USGS (2013). LANDSAT-8. United States Geological Survey. Department of the Interior. Available at <https://www.usgs.gov/land-resources/nli/landsat/landsat-8> Access on 15 January 2015.
https://www.usgs.gov/land-resources/nli/...
). Considering that LNP is spatially located in several images of the satellite platform, three (3) images were acquired to compose the mosaic (Tab. 1). Image pre-processing consisted of: (i) radiometric corrections using satellite band metadata such as sun elevation (degree) and image data acquisition; and (ii) band combinations (Bands 4, 3 and 2; red, green and blue, respectively), both using Envi 5.0 and ArcMap 10.3 software (Lillesand & Kiefer 2000Lillesand TM & Kiefer RW (2000) Remote sensing and image interpretation. 4th ed. John Wiley Sons, New York. 724p.; Du et al. 2002Du Y, Teillet PM & Cihlar J (2002) Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sensing of Environment 82: 123-134.; Cohen et al. 2003Cohen WB, Maiersperger TK, Yang Z, Gower ST, Turner DP, Ritts WD, Berterretche Me & Running SW (2003) Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sensing of Environment 88: 233-255.; Song & Woodcock 2003Song C & Woodcock CE (2003) Monitoring forest succession with multitemporal Landsat images: factors of uncertainty. IEEE Transactions on Geoscience and Remote Sensing 41: 2557-2567.; Coppin et al. 2004Coppin P, Lambin E, Jonckheere I & Muys B (2002) Digital change detection methods in natural ecosystem monitoring: A review. Analysis of Multi-temporal Remote Sensing Images 3-36.). The last software was only used to cut the satellite images mosaic to obtain the park’s feature using the clip raster function in the Arcmap 10.3 (ESRI 2011ESRI (2011) ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. Available at <https://desktop.arcgis.com/>. Access on 02 February 2015.
https://desktop.arcgis.com/...
). The Marzoli (2007Marzoli A (2007) Inventário Florestal Nacional: avaliação integrada das florestas de Moçambique. Direção Nacional de Terra e Florestas, Ministério da Agricultura, Maputo. 92p.) shapefile was used for this park edge cutting.

Table 1
Satellite images metadata details used to compose a mosaic in Limpopo National Park, Mozambique, Southern Africa.

A visual analysis of regions of interest (ROIs) was also performed, in which the units of provisional land use and land cover were obtained. Determining the potential LNP land uses was based on the FAO (2010)FAO - Food and Agriculture Organization (2010) Global Forest resource assessment. Country report, Mozambique, Rome. 6p. classification. Thus, six (6) types of land use and land cover were identified, namely: Shrubland (Sh), Grassland (Gr), Open forest (OF), Deciduous forest (DF), Evergreen forest (EF) and Human settlement (HS). The latter combines dwellings, infrastructure and cultivation fields.

Sampling and classificationof satellite images

After elaborating the preliminary map, 69 confirmation points were allocated ranging from 11 to 12 sample points per elaborated preliminary class in order to compare with the ground reality, and thus enable greater precision for the final map. The supervised classification was used through the Maximum Likelihood classifier in the Envi 5.0 software, assuming a description of the types of forest cover and current land use found on the confirmation points (Langley et al. 2001Langley SK, Cheshire HM & Humes KS (2001) A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland. Journal of Arid Environments 49: 401-411.; Tso & Olsen 2005Tso B & Olsen RC (2005) Combining spectral and spatial information into hidden Markov models for unsupervised image classification. International Journal of Remote Sensing 26: 2113-2133.; Jensen 2005Jensen JR (2005) Introductory digital image processing: a remote sensing perspective. 3rd ed. Prentice-Hall, Upper Saddle River. 526p.; Saba et al. 2010Saba T, Rehman A & Sulong G (2010) An intelligent approach to image denoising. Journal of Theoretical and Applied Information Technology 17: 32-36.). Thus, combinations were made between FAO (2010)FAO - Food and Agriculture Organization (2010) Global Forest resource assessment. Country report, Mozambique, Rome. 6p. vegetation physiognomy classification and reality found in the field to compile the final map.

Validation and accuracy assessment

An error matrix was constructed for map validation, which represents the distribution of correctly and erroneously classified pixels. This matrix is ​​used to evaluate the result of a classification in order to verify the quality of data contained in a classification (Stuart et al. 2006Stuart N, Barratt T & Place C (2006) Classifying the Neotropical savannas of Belize using remote sensing and ground survey. Journal of Biogeography 33: 476-490.; Lu & Weng 2007Lu D & Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing 28: 823-870.; Lillesand et al. 2008Lillesand TM , Kiefer RW & Chipman JW (2008) Remote sensing and image interpretation. John Wiley and Sons, New York. 756p.). In this matrix, columns are usually taken as correct (reference data) and lines are used to show what has been classified on the map or in generated classification of remote sensing data (Congalton et al. 1998Congalton RG, Balogh M, Bell C, Green K, Milliken JA & Ottman R (1998) Mapping and monitoring agricultural crops and other land cover in the Lower Colorado River Basin. Photogrammetric Engineering and Remote Sensing 64: 1107-1114.; Lillesand et al. 2008Lillesand TM , Kiefer RW & Chipman JW (2008) Remote sensing and image interpretation. John Wiley and Sons, New York. 756p.). In order to evaluate the thematic accuracy, Landis & Koch (1977Landis JR & Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 159-174.) proposed: (i) Kappa index (K), (ii) Overall accuracy index (OA), (iii) omission error (OE), and (iv) Commission error (CE). The relationship between these data sets is usually summarized in an error matrix or a contingency table (Lillesand & Kieffer 1994Lillesand TM & Kiefer RW (1994) Remote sensing and image interpretation. 2ª ed. John Wiley & Sons, Chichester. 750p.; Saba et al. 2010Saba T, Rehman A & Sulong G (2010) An intelligent approach to image denoising. Journal of Theoretical and Applied Information Technology 17: 32-36.).

Floristic characterization

The floristic survey consisted in stratified sampling by landscape type. Twelve sample points representing the three (3) main landscapes types present in LNP were allocated, making up four (4) points for each landscape. Data collection was based on the Point-Quadrant Method, in which the following parameters were measured within each plot for the nearest tree/shrub in each of the four quarters surrounding the central recording: (i) base diameter at ground level for individuals ≤ 2 m high; (ii) diameter at breast height for individuals > 2 m high; and iii) the tallest tree/shrub. All were identified according to APG IV (APG 2016APG IV - Angiosperm Phylogeny Group (2016) An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Botanical Journal of the Linnean Society 181: 1-20.). Each sampling point had six (6) transepts of 160 m each consisting of pairs, thus forming three (3) pairs and equidistantly spaced apart by 200 m (Trollope & Potgieter 1986Trollope WSW & Potgieter ALF (1986) Estimating grass fuel loads with a disc pasture meter in the Kruger National Park. Journal of the Grassland Society of Southern Africa 3: 148-152; Durigan 2003Durigan G (2003) Métodos para análise de vegetação arbórea. In: Cullen Junior L, Rudran R & Valladares-Pádua C (orgs.) Métodos de estudos em biologia da conservação e manejo da vida silvestre. Fundação Boticário de Proteção à Natureza, UFPR, Curitiba. Pp. 455-480.; Freitas & Magalhães 2012Freitas WK & Magalhães LMS (2012) Métodos e parâmetros para estudo da vegetação com ênfase no estrato arbóreo. Floresta e Ambiente 19: 520-540.). Each transect presented four (4) plots, thus making eight (8) plots per pair and 24 plots at the sampling point (Fig. 1), totaling 96 plots sampled for the three (3) landscape types.

Figure 1
Distribution of plots for floristic survey in Limpopo National Park, Mozambique, Southern Africa.

Vegetation data were quantitatively analyzed in terms of relative density, relative frequency, and relative dominance, from which the importance value index (IVI) was calculated. The species names which compose the mapped communities correspond to those that presented the highest ecological importance value (IVI). Thus, the community consists of high IVI species which differed significantly (Tukey 5%) from other species. In the case where two or more species is representing the community, there did not differ significantly in the IVI.

The sampling was simultaneously performed to the mapped land cover classes (Phytophysiognomies) as a way of giving details about the floristic composition, so the final community map had both data sets incorporated (Phytophysiognomies and their corresponding composition). This was performed in Arcmap 10.3 through overlapping geographical coordinate plots (where the floristic characterization was done) with mapped physiognomy polygon class area.

Results and Discussion

The elaborated final map in World Geodetic System (WGS) 84 datum presents 13 floristic community classes (grouped by preliminary land use and land cover classes and species composition) (Fig. 2).

Figure 2
Floristic communities classes mapped by World Geodetic System (WGS) 84 datum in Limpopo National Park, Mozambique, Southern Africa.

Among the resulting classes, Open Forest physiognomy community of Terminalia sericea Burch. ex DC./Combretum apiculatum Sond./Guibourtia conjugata (Bolle) J.Léonard/Colophospermum mopane (J.Kirk ex Benth.) J. Léonard presented the largest coverage area with about 2,458 km2, making up 24.37% of LNP area. Species coverage results corroborate studies done by Martini et al. (2016Martini F, Cunliffe R, Farcomeni A, De Sanctis M, D’Ammando G & Attorre F (2016) Classification and mapping of the woody vegetation of Gonarezhou National Park, Zimbabwe. Koedoe 58: 1-10.) in Gonarezou Park, Zimbabwe (adjacent area to LNP), where they found greater representativeness of Colophospermum mopane in the mapped classes. Bila & Mabjaia (2012Bila JM & Mabjaia N (2012) Crescimento e fitossociologia de uma floresta com Colophospermum mopane, em Mabalane, Província de Gaza, Moçambique. Pesquisa Florestal Brasileira 32: 421.) found the same result around the study area with 86.68 % of importance value index (IVI). Although C. mopane presents wide distribution in the park, this does not occur in a restricted way; it occurs associated with other species such as Combretum apiculatum, Terminalia sericea, and Guiboutia conjugata, among others (O’connor 1998O’Connor TG (1998) Impact of sustained drought on a semi-arid Colophospermum mopane savanna. African Journal of Range & Forage Science 15: 83-91.; Sebego et al. 2008Sebego RJ, Arnberg W, Lunden B & Ringrose S (2008) Mapping of Colophospermum mopane using Landsat TM in eastern Botswana. South African Geographical Journal 90: 41-53.; Gandiwa & Kativu 2009Gandiwa E & Kativu S (2009) Influence of fire frequency on Colophospermum mopane and Combretum apiculatum woodland structure and composition in northern Gonarezhou National Park, Zimbabwe. Koedoe 51: 1-13.; Ribeiro et al. 2010Ribeiro A, Romeiras MM, Tavares J & Faria MT (2010) Ethnobotanical survey in Canhane village, district of Massingir, Mozambique: medicinal plants and traditional knowledge. Journal of ethnobiology and ethnomedicine 6: 33.). On the other hand, Acacia xantophloea/Faderbia alba and Androstrachys jonsonii did not occur in association with C. mopane, which may be due to the soil type that these species occur in (O’connor 1998O’Connor TG (1998) Impact of sustained drought on a semi-arid Colophospermum mopane savanna. African Journal of Range & Forage Science 15: 83-91.; Gandiwa & Kativu 2009Gandiwa E & Kativu S (2009) Influence of fire frequency on Colophospermum mopane and Combretum apiculatum woodland structure and composition in northern Gonarezhou National Park, Zimbabwe. Koedoe 51: 1-13.; Gandiwa et al. 2011Gandiwa E, Chikorowondo G, Zisadza-Gandiwa P & Muvengwi J (2011) Structure and composition of Androstachys johnsonii woodland across various strata in Gonarezhou National Park, southeast Zimbabwe. Tropical Conservation Science 4: 218-229.).

The greater representativeness of C. mopane in the mapped communities is due to this species possessing physical characteristics and physiological mechanisms to tolerate water stress and high temperature conditions, demonstrating wide niche amplitude of this species in the ecosystem (Hempson et al. 2007Hempson GP, February EC & Verboom GA (2007) Determinants of savanna vegetation structure: insights from Colophospermum mopane. Austral Ecology 32: 429-435.; Makhado et al. 2014Makhado RA, Mapaure I, Potgieter MJ, Luus-Powell WJ, Saidi AT (2014) Factors influencing the adaptation and distribution of Colophospermum mopane in southern Africa’s mopane savannas - a review. Bothalia 44: 1-9.). Despite disturbances caused by fire and herbivory activities by megafauna, this species can survive due to its high capacity for regrowth and production of inhibitory chemical substances against herbivores. On the other hand, its wood calcium oxalate crystals contribute to higher density as well as fire resistance (Mlambo & Mapaure 2006Mlambo D & Mapaure I (2006) Post-fire resprouting of Colophospermum mopane saplings in a southern African savanna. Journal of Tropical Ecology 231-234.; WhiteCross et al. 2012Whitecross MA, Archibald S & Witkowski ETF (2012) Do freeze events create a demographic bottleneck for Colophospermum mopane? South African Journal of Botany 83: 9-18.; Stevens et al. 2014Stevens N, Swemmer AM, Ezzy L & Erasmus BFN (2014) Investigating potential determinants of the distribution limits of a savanna woody plant: Colophospermum mopane. Journal of Vegetation Science 25: 363-373.).

Human occupation accounts for 4.2% of the park, surpassing some existing floristic community areas (Tab. 2). The Open Forest physiognomy community of Acacia tortilis Hayne/Colophospermum mopane (J.Kirk ex Benth.) J. Léonard/Terminalia sericea Burch. ex DC./Guibourtia conjugata (Bolle) J. Léonard presented the most human settlements in its surroundings. Anthropogenic disturbances can cause a change in the forest structure, species composition and changes in landscape patterns, causing lower resilience to extreme natural phenomena events such as cyclones and fire (Thompson et al. 2002Thompson J, Brokaw N, Zimmerman JK, Waide RB, Ever-ham EM, Lodge DJ, Taylor CM, Garcia-Montiel D & Fluet M (2002) Land use history, environment, and tree composition in a tropical forest. Ecological Applications 12: 1344-1363.; Chazdon 2003Chazdon RL (2003) Tropical forest recovery: legacies of human impact and natural disturbances. Perspectives in Plant Ecology, Evolution and Systematics 6: 51-71.; Echeverría et al. 2007Echeverría C, Newton AC, Lara A, Benayas JMR & Coomes DA (2007) Impacts of forest fragmentation on species composition and forest structure in the temperate landscape of southern Chile. Global Ecology and Biogeography 16: 426-439.). Some studies have demonstrated a negative effect among traditional LNP communities and the local biodiversity coexistence. Everatt et al. (2014Everatt KT, Andresen L & Somers MJ (2014) Trophic scaling and occupancy analysis reveals a lion population limited by top-down anthropogenic pressure in the Limpopo National Park, Mozambique. PloS one 9: 99389.) found declining lion populations due to interaction with domestic animals from disease transmission and dynamics in predator-prey relationship, while other authors have reported an intense conflict between local communities and elephants (Wolmer 2003Wolmer W (2003) Transboundary conservation: the politics of ecological integrity in the Great Limpopo Transfrontier Park. Journal of Southern African Studies 29: 261-278.; Milgroom & Spierenburg 2008Milgroom J & Spierenburg M (2008) Induced volition: resettlement from the Limpopo National Park, Mozambique. Journal of Contemporary African Studies 26: 435-448.; Dunham et al. 2010Dunham KM, Ghiurghi A, Cumbi R & Urbano F (2010) Human-wildlife conflict in Mozambique: a national perspective, with emphasis on wildlife attacks on humans. Oryx 44: 185-193.; Witter 2013Witter R (2013) Elephant-induced displacement and the power of choice: moral narratives about resettlement in Mozambique’s Limpopo National Park. Conservation and Society 11: 406-419.; Cook et al. 2015Cook RM, Henley MD & Parrini F (2015) Elephant movement patterns in relation to human inhabitants in and around the Great Limpopo Transfrontier Park. Koedoe 57: 1-7.).

Table 2
Floristic communities classes by occupied area in Limpopo National Park, Mozambique, Southern Africa.

In contrast, coexistence between traditional communities and the local flora comes from longer periods, even before LNP became a conservation area (MITUR 2003MITUR - Ministério do turismo (2003) Plano de maneio e desenvolvimento do Parque Nacional do Limpopo. Moçambique. DINAC, Maputo. 33p.; Massé 2016Massé F (2016) The political ecology of human-wildlife conflict: producing wilderness, insecurity, and displacement in the Limpopo National Park. Conservation and society 14: 100-111.; Givá & Raitio 2017Givá N & Raitio K (2017) ‘Parks with people’ in Mozambique: community dynamic responses to human-elephant conflict at Limpopo National Park. Journal of Southern African Studies 43: 1199-1214.; Conceição et al. 2017Conceição AW, Tedim F & Ntumi C (2017) Impacto das políticas de conservação da natureza na dinâmica das comunidades locais no Parque Nacional do Limpopo (Moçambique). Argumentos 14: 275-295.). This anthropogenic occupation is accompanied through their habits, customs and cultural values, and it could explain the current landscape dynamics shaped by fire used by the local communities for subsistence purposes (MITUR 2003MITUR - Ministério do turismo (2003) Plano de maneio e desenvolvimento do Parque Nacional do Limpopo. Moçambique. DINAC, Maputo. 33p.; Archibald et al. 2012Archibald S, Staver AC & Levin SA (2012) Evolution of human-driven fire regimes in Africa. Proceedings of the National Academy of Sciences 109: 847-852.; Archibald 2016Archibald S (2016) Managing the human component of fire regimes: lessons from Africa. Philosophical Transactions of the Royal Society B: Biological Sciences 371: 20150346.; Massé 2016Massé F (2016) The political ecology of human-wildlife conflict: producing wilderness, insecurity, and displacement in the Limpopo National Park. Conservation and society 14: 100-111.). In spite of being an anthropogenic disturbance, fire is a very important and essential event for African savanna dynamics, acting on pyrophytic seed germination, as well as facilitating tree and grass coexistence (Bond & Keeley 2005Bond WJ & Keeley JE (2005) Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends in Ecology and Evolution 20: 387-394.; Foster et al. 2017Foster CN, Barton PS, Robinson NM, MacGregor CI & Lindenmayer DB (2017) Effects of a large wildfire on vegetation structure in a variable fire mosaic. Ecological applications 27: 2369-2381.). However, its frequency and intensity should be controlled so as not to damage biodiversity (Bond & Keeley 2005Bond WJ & Keeley JE (2005) Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends in Ecology and Evolution 20: 387-394.; Lloret et al. 2005Lloret F, Estevan H, Vayreda J & Terradas J (2005) Fire regenerative syndromes of forest woody species across fire and climatic gradients. Oecologia 146: 461-468.; Just et al. 2016Just MG, Hohmann MG & Hoffmann WA (2016) Where fire stops: vegetation structure and microclimate influence fire spread along an ecotonal gradient. Plant ecology 217: 631-644.; Foster et al. 2017Foster CN, Barton PS, Robinson NM, MacGregor CI & Lindenmayer DB (2017) Effects of a large wildfire on vegetation structure in a variable fire mosaic. Ecological applications 27: 2369-2381.). Thus, mapping floristic communities demonstrates the spatial dimension of human occupation and resource use. Not only, but also identification of floristic communities, associated with anthropogenic activities, such as fire, allowing park’s managers to prioritize conservation actions (Ribeiro et al. 2019Ribeiro N, Ruecker G, Govender N, Macandza V, Pais A, Machava D, Chauque A, Lisboa SN & Bandeira R (2019) The influence of fire frequency on the structure and botanical composition of savanna ecosystems. Ecology and evolution 9: 8253-8264.).

The digital classification accuracy was explained using Overall accuracy and Kappa index (Tab. 3). Overall accuracy was 74%, which means that the probability of obtained classes from satellite images reflects 74% of the ground reality. However, according to Congalton & Green (2019Congalton RG & Green K (2019) Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed. CRC Press, Boca Raton. Pp.17-35.), overall accuracy is an index which overestimates the classification accuracy, and the Kappa index would be a more appropriate assessment since it incorporates information from poorly ranked pixels, not just overall accuracy (Lillesand & Kiefer 1994Lillesand TM & Kiefer RW (1994) Remote sensing and image interpretation. 2ª ed. John Wiley & Sons, Chichester. 750p.; Breiman 2001Breiman L (2001) Random forests. Machine Learning 45: 5-32.; Rapp et al. 2005Rapp J, Wang D, Capen D, Thompson E & Lautzenheiser T (2005) Evaluating error in using the national vegetation classification system for ecological community mapping in Northern New England, USA. Natural Areas Journal 25: 46-54.). Commission error was higher in the EF class, about 33%, which means that two (2) of the three (3) pixels of this class on the thematic map were correctly classified, and one (1) was wrongly classified; which means they have been included in this class until they belong to them. On the other hand, this error was lower in the OF class because only one (1) of the eight (8) existing pixels is wrongly included in this class. The largest omission error was 36%, and this was in the OF class. This means that seven (7) of the eleven pixels in this class were correctly classified, and the remaining four (4) were wrongly classified (excluded from this class). However, this error was lower on Gr class, where only two (2) of the 14 existing pixels were excluded from this class.

Table 3
Error Matrix representing the distribution of classified pixels (vegetation physiognomy) for validation in Limpopo National Park, Mozambique, Southern Africa.

The Kappa index was 68%, ranging between 60 to 80% of this index, and indicating that the classification is good (Breiman 2001Breiman L (2001) Random forests. Machine Learning 45: 5-32.; Lillesand et al. 2008Lillesand TM , Kiefer RW & Chipman JW (2008) Remote sensing and image interpretation. John Wiley and Sons, New York. 756p.; Congalton & Green 2019Congalton RG & Green K (2019) Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed. CRC Press, Boca Raton. Pp.17-35.). Although this index could be higher, the accuracy depends on several factors such as terrain complexity, spatial and spectral resolutions of the sensor system, classification algorithm itself and the number of sampling points (Landis & Koch 1977Landis JR & Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 159-174.; Crósta 1992Crósta AP (1992) Processamento digital de imagens de sensoriamento remoto. 3ª ed. IG-UNICAMP, São Paulo. 170p.; Moreira 2003Moreira AM (2003) Fundamentos do sensoreamento remoto e metodologias de aplicação. 2a ed. Editora UFV, Viçosa. Pp. 231-247.; Schowengerdt 2007Schowengerdt RA (2007) Remote sensing models and methods for image processing. 3th ed. Elsevier, New York. 522p.).

Conclusion

Colophospermum mopane presents wide distribution in the park which does not occur in a restricted way, but is associated with other species, demonstrating a wide niche amplitude of this species in the ecosystem. The elaborated map presents much information regarding different physiognomies and floristic compositions of the park. Therefore, it is recommended that LNP managers use this information for fire management since the park is frequently devastated by fires, as well as deepening studies on the effect of burning in these floristic communities. On the other hand, the map information shows that areas of human occupation are larger than some floristic communities; these anthropogenic activities must be evaluated so that they do not harm the park’s biodiversity conservation. Thus, it is reasonable to suggest that communities without human presence, especially the smaller communities, should be given priority actions for their conservation.

Acknowledgments

To the German Gesellschaft fuer Internationale Zusammenarbeit (GIZ) institution, for the funds; and Limpopo National Park administration, for having provided the accommodation, operational means and access to study sites.

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Publication Dates

  • Publication in this collection
    18 Jan 2021
  • Date of issue
    2021

History

  • Received
    14 Dec 2018
  • Accepted
    19 Mar 2020
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