Trends in use of remotely sensed data in wetlands assessment and monitoring in Zimbabwe

The paper assesses trends in use of earth observation data wetland conditions monitoring and assessment Zimbabwe from 1980 to 2019. Reviewed literature shows academia research institutions (69.8%), government agencies (14%) international development partners (16.3%) as the main users remotely sensed ecological assessments monitoring. There is more reliance by abovementioned stakeholders on freely available low-cost resolution imagery Landsat (62.9%) Moderate Resolution Imaging Spectroradiometer (14.3%). Other stakeholders, however, are reliant high-resolution like Rapid Eye (5.7%) aerial photography (11.4%). Satellite images management used for land cover change detection (42.1%), vegetation health (21.1%), water quantity (5.3%), quality (13.2%) mapping (18.4%). identified challenges faced different effectively utilise EO include high cost imageries, limited expertise, inadequate equipment software. Since satellite mainly constraints acquisition suitable assess small wetlands that dominate Zimbabwe's landscape, there need promote recently launched Sentinel improve conditions. Cette étude analyse les tendances d’utilisation des données d'observation de la Terre dans surveillance et l'évaluation l’état terres humides au à La littérature passée en revue montre que universités instituts recherche (69,8%), organismes gouvernementaux partenaires internationaux développement (16,3%) sont principaux utilisateurs obtenues par télédétection le cadre l’évaluation écologique humides. Les intervenants mentionnés ci-dessus s’appuient davantage sur basse résolution disponibles gratuitement (62,9%) du spectroradiomètre imageur moyenne (14,3%). Cependant, d'autres haute comme (5,7%) photographie aérienne (11,4%). Dans gestion Zimbabwe, satellites utilisées pour détection changements l'utilisation couverture terrestre (42,1%), végétation (21,1%), quantité d'eau (5,3%), qualité l'eau (13,2%) cartographie (18,4%). défis identifiés auxquels confrontés différents utiliser efficacement d'OT notamment coût élevé résolution, compétences limitées, ainsi équipements logiciels inadéquats. Étant donné constitue principal obstacle l’acquisition satellitaires adéquates évaluer petites qui dominent paysage il est nécessaire promouvoir l’utilisation récemment mises disposition améliorer turn 20th century, world has lost than 50% its original due human activities (Mitsch al., 2015; Mitsch & Gosselink, 1993). Africa, Asia, North America South record about 80% loss (Davidson, 2014). These rates are, debatable some scholars think they might be higher lack a global inventory 2014; Hu 2017). remaining face increasing pressure both indirect direct such farming sites, release untreated effluent discharge into overpumping (Hu 2017; Ramsar Convention Wetlands, 2018; Wójcicki Woskowicz-Ślęzak, 2018). Urgent action prevent further degradation therefore required all nations (Myers 2013). Wetlands important ecosystems support interaction humans, animals plants through provision ecosystem services Turpie 2010; Wood, 2001). provisioning (freshwater, food, herbs, fibre, fuel, thatch grass, genetic resources), regulating (flood erosion control, purification, climate regulation, air maintenance, pest disease pollination), supporting (water nutrient cycling, habitats, soil formation retention, primary productivity) cultural (ecotourism, recreation, religious spiritual values, education, aesthetic values; Chen 2012; Ogawa Male, 1986; Woodward Wui, 2001; Xu 2020). Despite their numerous benefits, remain fragile require sensitive sustainable (Darradi 2006; Davidson, Gadzirayi 2006). Globally, assessed monitored using guidelines convention (Finlayson 2011; offers framework managing globally wise concept which encourages parties adopt national policies (Mitra 2005; 2019). inception convention, approaches have been changing incorporate current environmental regulations wishes involved 2005). This seen incorporating remote sensing. also extends potential Africa responsible sites (Denny, Countries adopted taken achieve wetlands. Wetland Indigenous Knowledge Systems (IKS), sensing, laws applied manage Among employed Earth Observation (EO) least yet it most effective (Ozesmi Bauer, 2002; Thamaga Dube, Factors hindering adoption but not following: costs analysing involve hardware, software, qualified specialists training especially when dealing with large sets (Klerk Buchanan, called involvement various stakeholders. Stakeholders individuals, organisations or oppose project potentially affected Devarani Basu, 2009; Lucrezi Generally, partners, river basin organisations, sub-basin local NGOs, community-based private sector, academic (MacDonald, 2007; Merrey, 2008). In Zimbabwean context, Mbereko al. (2007) report 12 working communities wetlands, whose knowledge application may protection. can grouped following categories: institutions, traditional NGOs leadership. Owing capabilities larger area monitoring, how embracing abate loss. study aims wetlands' between analyses roles contributions management. It within scope this level identify factors Zimbabwe. types reasons select them. findings will ultimately explain extent specific objectives follows: establishing assessment, nature components assessed. Data were obtained online publications reviewed 2019 (Figure 1). Journal articles published ELSEVIER, Taylor Francis, Springer reviewed. Keywords search relevant data, Authors' affiliations stakeholders' categories utilising authors gleaned journal information under methods sections derive type Technical scientific reports Natural Resources Board (1980–2006) Environmental Management Agency (2007–2019) methodologies monitor statutory agencies' searched Midlands State University library portal. described Marambanyika (2016) built partial view reality hindered Although prove an tool, access limited. Literature Choice largely dependent although wide range ranging low resolution. Types (Chikodzi Mapfaka, Dube Masocha, Makore, Nhiwatiwa, 2018), Medium (MODIS; Shoko 2015), PourI'Observation (SPOT; Chikodzi Mufori, Mhlanga 2014), RapidEye (Marambanyika Beckedahl, 2016b; 2016) 2016; Mutisi, Sibanda, constitute 62.9% used, MODIS (14.3%), (5.7%), SPOT (11.4%) 2). Therefore, widespread two free. free hence evidence being low-resolution come images. Low-resolution misclassification (Thamaga 2018) make hard key features, effectiveness Just improving Southern African Development Community (SADC) countries Angola, Botswana, Democratic Republic Congo, Namibia, Tanzania, Zambia (Mayer Lopez, 2011). A imageries utilised. National Oceanic Atmospheric Administration (NOAA; Milzow Ringrose 2003), Advanced Microwave Scanning Radiometer (AMSR; Hiyama Pour l'Observation Talukdar, 2004), (ENVISAT; De Roeck 2008; 2006), (Ringrose (Awadallah Tabet, Bwangoy Grundling 2013; Hansen Kashaigili Majaliwa, Msofe 2019; Munyati, 2000; Pullanikkatil Schneibel 2017), (Leemhuis Naidoo 2019), (Hansen 2013) Worldview (Mutanga As observed generally dependence across SADC region. was noted utilised almost half (46.7%) applications Reliance World view, SPOT, ENVISAT, NOAA, AMSR shown frequency 11%. studies focus detecting parameters critical assessments. For instance, Mutanga (2012) explored possibility estimating biomass densely vegetated normalised difference index (NDVI) computed WorldView-2 imagery, contains red edge band centred at 725 m. results demonstrated utility random forest regression density, sensors other continues influenced associated purchasing (Meier Kinzelbach, 2010). proven uses 3). (LULCC) analysis (Fakarayi Gumindoga Kibena 2016a; Ndhlovu, Tendaupenyu (Chapungu Nhamo, Masocha Mpakairi, Shekede (Dlamini Lupankwa (Mpakairi, LULCC 42.1% usage 4). Fakarayi (2015) TM, quantify changes Driefontein wetland, 1995 2010. objective if tenure result Fast Track Resettlement Programme, commenced March 2000, had wetland. After co-registering images, classified unsupervised K-means, Supervised Angle Mapper visual interpretation came up five classes cover. All show Grassland decreased 71.2%, 55.3% while cultivated increased 97.9%. Woodland expanded 5% covered 85% increase. However, availability cloud captured cloud-free days. Vegetation constitutes 21.1% example, Chapungu Nhamo 8 Thematic 30 m spatial impact plant species richness Mutirikwi sub-catchment, Images years 1987, 1998, 2006 2014 track abundance over years. To units, GIS-based nested nonaligned block sampling design used. identification number calculated Point Centre Quarter Method. analysed Integrated Land Water System (ILWIS) Normalised Difference Index (NDWI) values. then indicated association richness. NDWI values wood grass invasion desert shrubs. successful showing effect richness, indices Climate Extremes (CEI) Greenhouse Response (GCRI). 13.2%. Dlamini cheap readily broadband multi-spectral situ measurements quantifying Lake Chivero, inland lake chlorophyll-a concentrations proxy predicting pollution levels. predominant temporal patterns variability. ILWIS calibrate NDVI historic pollution. supervised classification maximum likelihood algorithm classifier. Field sample chosen based sources included collecting samples extracting chlorophyll-a. Combining sensing showed compared surroundings. means Chivero compromised presence About 18% mapping. conducted Mapfaka (2018) Masvingo district, sought variations calculate Modified (MNDWI) useful delineating waterlogged areas. From MNDWI extracted, distribution behaviour mapped period Climatic (rainfall temperature variations) detect variations. because managed map no significant sizes 1984 2014, occurring those mapped. finer including ones. 5.3% (2018), Runde catchment, surface bodies. They OLI (Operational Imager) extract spectral delineate water. NDVI, MNDWI, Ratio (WRI), Automated Extraction (AWEI) Surface (LSWI). total 23 selected, them, 121 reference bodies digitised Google imagery. Of LSWI scale. even country LSWI. Results highlighted much concentrated central northern parts south-western south-eastern less (44.4%) region (3.7%) flood regimes (7.4%). Examples (De Landmann 2016), (LULCC; Leemhuis (Grundling 2004) features 2003; 2004). Table 1, 57% software these 83% ILWIS. 43% commercial ArcGIS, Environment Visualizing (ENVI) LOWTRAN 7. rely particular. EO-based studies, explore utilisation Geographic Analysis Support (GRASS) GIS (Neteler 2012), Engine (Hardy 2020), Geoscientific Analyses (SAGA) (LaRocque 2020) Information (WOIS; Guzinski, Kass, Huber, Bauer-Gottwein, Jensen Naeimi, Jensen, Doubkova, Makapela 2015). embraces multi-institutional approach far concerned 2016a). Involvement natural resources Section 8(1) EMA Act (20:27) advocates promotion cooperation among public departments, authorities, (Government 2002). Government actively departments. departments (EMA), Authority (ZINWA), Police (ZRP), Rural District Councils (RDCs), Urban Agricultural Extension Services (AGRITEX; Love 2012). regulations. repealed (20:13) provided conservation (Nickerson, 1994; Scoones Cousins, 1994) took over. Currently, provides 113 Researchers learning universities institutes form another group carry out projects health. Studies done invasive (Dube (Mandishona, 2017) (Masocha carried researchers supported NGOs. include, Lupane (Ndlovu Manjeru, 2016b), universities. addition funding work cultivation food security poverty eradication programmes 2016). surrounding Sixty per cent country's communal (Gadzirayi Matiza Crafter, rendering them jurisdiction communities. Communities believe connections ancestors Hence, strategies passed generation believed inspired ancestral spirits area. works closely intervenes EMA. mechanisms locals IKS creates basis local-level decision-making literature, 70% usually region, groups Tab et, (Landmann Meier (Bwangoy 75.8% whereas represent 24.1% Universities perform International Bank Murwira Institute (Gumindoga Birdlife 2015) Waternet (Ndhlovu, 2012) data. together providing funds work. 16% though management, Only EMA, agency, Harare province, lags behind 14%. concluded little highly undermined reason price (Kamusoko free, coarse cannot majority Identification difficult well detected causes burnt mistaken pools focused hydrological impacts urbanisation catchments, hyacinth affects appropriate scales, led shortage absence reliable poor challenged inflation living standards (Munangagwa, 2009). related too many individuals wishing undertake research. Costs processing only implication choice widely documented Di Martino deficiency country. Four (4) revealed seriously Mandishona, why 2007 very gained popularity 2008 increase advancing technological capacities introduction GeoSpatial (ZINGSA) 2018. Moreover, eight one institution now offering sharp contrast situation 21st Century university introduced curriculum. Limited expertise problem where 72 offered 360 universities, 17 integrated teaching Naeimi (2014), (2014) technical capacity challenge undermining implementation conservation, Africa. improvements training, gender-disaggregated Use still improvement noticed 2010 date. indicates 2009, field systems labour intensive expensive, extend coverage. explains purchased agencies. implement detection, inventory. satellite-based moisture regimes, fire incidences, biodiversity applications. recommend fully supports institutional influence processes structures. considered building initiatives enhance scale appreciation derived services. recommended reveal Future should new crop C++, Formula Translation (Fortran), GIS, Engine, Python, R, (WOIS). Lastly, Copernicus omitted imageries. We would thank anonymous reviewers helping manuscript. declare conflict interest. corresponding author upon reasonable request. accessed list below.