The relationship between satellite-derived indices and species diversity across African savanna ecosystems

Mapfumo, Ratidzo B.; Murwira, Amon; Masocha, Mhosisi; Andriani, R.

doi:10.1016/j.jag.2016.06.025

Cited by 19 publications

(18 citation statements)

References 57 publications

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“…Similarly, diversity indices also significantly regressed with the NBVI components; however as stated earlier, the R 2 values were much lower (≤0.5, p < 0.05) except for the Chao-1 index ( Table 9). The significant relationship observed between satellite-based indices (NDVVIs and NBVIs) and field measured diversity indices is in line with previous results such as [149] who reported R 2 as high as 0.87 between NDVI and plant richness; [137] who reported R 2 values between 0.32 and 0.72 for NDVI and Shannon's diversity; and [95] who reported R 2 values of 0.51 to 0.83 for first order hyperspectral indices and diversity indices including Shannon-Weiner, Pielou, Simpson, Margalef and Gleason. The scatterplots of observed versus predicted diversity indices are shown in Figure 6.…”

Section: Maximum-differencesupporting

confidence: 91%

“…The potential of the new NDVVI variants to estimate the species richness and diversity of the study area was assessed using non parametric statistics. Vegetation indices such as the normalized difference vegetation index (NDVI) are commonly used to predict species richness and diversity [32,95,[135][136][137], therefore, we compared the capability of the new NDVVI variants and traditional narrow-band vegetation indices (NBVIs) listed in Table 3 for predicting vascular plant species diversity. As the NDVVI is derived from Hyperion wavelengths with different levels of sensitivity to soil TPH, it is expected that the NDVVI values will be higher for control transects than for polluted transects and that consequently, estimated species richness and diversity indices will follow similar patterns.…”

Section: Statistical Analysesmentioning

confidence: 99%

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Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions

2018

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Biodiversity loss remains a global challenge despite international commitment to the United Nations Convention on Biodiversity. Biodiversity monitoring methods are often limited in their geographical coverage or thematic content. Furthermore, remote sensing-based integrated monitoring methods mostly attempt to determine species diversity from habitat heterogeneity somewhat reflected in the spectral diversity of the image used. Up to date, there has been no standardized method for monitoring biodiversity against the backdrop of ecosystem or environmental pressures. This study presents a new method for monitoring the impact of oil pollution an environmental pressure on biodiversity at regional scale and presents a case study in the Niger delta region of Nigeria. It integrates satellite remote sensing and field data to develop a set of spectral metrics for biodiversity monitoring. Using vascular plants of various lifeforms observed on polluted and unpolluted (control) locations, as surrogates for biodiversity, the normalized difference vegetation vigour index (NDVVI) variants were estimated from Hyperion wavelengths sensitive to petroleum hydrocarbons and evaluated for potential use in biodiversity monitoring schemes. The NDVVI ranges from 0 to 1 and stems from the presupposition that increasing chlorophyll absorption in the green vegetation can be used as a predictor to model vascular plant species diversity. The performances of NDVVI variants were compared to traditional narrowband vegetation indices (NBVIs). The results show strong links between vascular plant species diversity and primary productivity of vegetation quantified by the chlorophyll content, vegetation vigour and abundance. An NDVVI-based model gave much more accurate predictions of species diversity than traditional NBVIs (R-squared and prediction square error (PSE) respectively for Shannon's diversity = 0.54 and 0.69 for NDVVIs and 0.14 and 0.9 for NBVIs). We conclude that NDVVI is a superior remote sensing index for monitoring biodiversity indicators in oil-polluted areas than traditional NBVIs.

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Section: Maximum-differencesupporting

confidence: 91%

Section: Statistical Analysesmentioning

confidence: 99%

Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions

2018

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“…The country has assortment variety of soil types ranging from sodic and salliatic soils in the north, ferrialistic soils in the south, paraferrallistic and ortheferrilitic in the east, to regosols and Kalahari sands in the west [ 38 , 39 ]. The vegetation is dominated by dry miombo woodlands in the central and east regions of the country [ 40 ]. Mopane woodlands dominate in the lowveld regions located in the northern and southern areas [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Inclusion of edaphic predictors for enhancement of models to determine distribution of soil-transmitted helminths: the case of Zimbabwe

Midzi¹,

Kavhu²,

Manangazira³

et al. 2018

Parasites Vectors

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BackgroundReliable mapping of soil-transmitted helminth (STH) parasites requires rigorous statistical and machine learning algorithms capable of integrating the combined influence of several determinants to predict distributions. This study tested whether combining edaphic predictors with relevant environmental predictors improves model performance when predicting the distribution of STH, Ascaris lumbricoides and hookworms at a national scale in Zimbabwe.MethodsGeo-referenced parasitological data obtained from a 2010/2011 national survey indicating a confirmed presence or absence of STH among school children aged 10–15 years was used to calibrate ten species distribution models (SDMs). The performance of SDMs calibrated with a set of environmental and edaphic variables was compared to that of SDMs calibrated with environmental variables only. Model performance was evaluated using the true skill statistic and receiver operating characteristic curve.ResultsResults show a significant improvement in model performance for both A. lumbricoides and hookworms for all ten SDMs after edaphic variables were combined with environmental variables in the modelling of the geographical distribution of the two STHs at national scale. Using the top three performing models, a consensus prediction was developed to generate the first continuous maps of the potential distribution of the two STHs in Zimbabwe.ConclusionsThe findings from this study demonstrate significant model improvement if relevant edaphic variables are included in model calibration resulting in more accurate mapping of STH. The results also provide spatially-explicit information to aid targeted control of STHs in Zimbabwe and other countries with STH burden.

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“…As a result, Reference [20] tested 168 metrics of spectral diversity. Among them, however, Normalized Difference Vegetation Index (NDVI) or its variances (NDVI Sill, NDVI Range, CVNDVI) are the most commonly applied, mainly in biodiversity analysis [21,22]. The third type of landscape metrics, which allow one to quantify, among others, the functioning and stability of ecosystems, their biodiversity and degree of fragmentation [23,24].…”

Section: Introductionmentioning

confidence: 99%

The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study

Sowińska-Świerkosz

Michalik-Śnieżek

2020

Sustainability

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Landscape quality (LQ) encompasses diverse characteristic of the natural and cultural environment. The most effective tool to analyze LQ is the use of indicators. The main problem in the assessment of LQ is not the lack of indicators, but its multitude. That is why, the indicators’ categorization is a problematic issue. The paper aims to introduce and test the methodology for selecting the suitable indicators based on the example of two national parks located in the south-east part of Poland. The method composed of the following stages: (1) Selection of spatial units being analyzed; (2) selection of indicators type(s); (3) selection of specific indicators; (4) calculation of indicator set no 1; (5) analysis of the correlation between indicators’ pairs; (6) selection of a final set of indicators; (7) analysis of effectiveness. The latter stage, refers to the statistical analysis of significance between results obtained dependently on the data sources, a spatial unit of analysis and analyzed regions. The results showed that the categorization composed of ten, mainly composite indicators, can be applied to conclude on different levels of LQ of protected areas. The differences between two analyzed data sources, different spatial units, as well as diverse regions, occurred to be statistically insignificant. Generally, the results of the effectiveness analysis showed that a final categorization of LQ indicators is adequate to conclude on the diverse dimensions of LQ of analyzed protected areas.

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The relationship between satellite-derived indices and species diversity across African savanna ecosystems

Cited by 19 publications

References 57 publications

Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions

Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions

Inclusion of edaphic predictors for enhancement of models to determine distribution of soil-transmitted helminths: the case of Zimbabwe

The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study

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