Land cover mapping applications with MODIS: a literature review

García-Mora, Tziztiki J.; Mas, Jean-François; Hinkley, Everett

doi:10.1080/17538947.2011.565080

Cited by 64 publications

(21 citation statements)

References 5 publications

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“…Since its launch in 2000, MODIS data products have been used by the land remote sensing community for a number of research applications including vegetation monitoring, crop yield estimation, and burn scar identification [25]. One of the most widely used data products from this sensor is the Normalized Difference Vegetation Index (NDVI), a normalized ratio of reflectances in the red and near infrared portions of the electromagnetic spectrum ((NIR -red)/(NIR + red)), which is sensitive to chlorophyll content and can be used as an indicator of the amount of actively photosynthesizing, green vegetation [26,27].…”

Section: High Temporal Resolution Data: Modismentioning

confidence: 99%

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

2013

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Abstract:Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1) a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview) as the dependent variable, and (2) ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s) tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems.

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Section: High Temporal Resolution Data: Modismentioning

confidence: 99%

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

2013

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“…During the last decades, EO-programmes as LANDSAT (Wulder et al 2019) or MODIS (García-Mora et al 2012) deliver data, which have enabled the implementation of large scale monitoring systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

Löw

Tatjana

2020

Preprint

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Background Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or an altering natural environment. It is decisive to trace the intra- to trans-annual dynamics of these forest ecosystems. National to local forest communities request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. Methods We developed a remote sensing based time series analysis (TSA) framework that comprises data access, data management, image pre-processing, and an advanced but flexible TSA. The data basis is a dense time series of multispectral Sentinel-2 images with a spatial resolution of 10 metres. We use a dynamic Savitzky-Golay-filtering approach to reconstruct robust but sensitive phenology courses. Deviations from the latter are further used to derive spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. Finally, we use zonal statistics on different spatial scales to provide aggregated information on the extent of forest disturbances between 2018 and 2019.Results and Conclusion The outcomes are a) individual phenology models and deduced phenology metrics for each 10 metres by 10 metres forest pixel in Austria and b) forest disturbance maps useful to investigate the occurrence, development and extent of bark beetle infestation. The phenology modelling results provide area-wide consistent data, also useful for downstream analyses (e.g. forest type classification). Results of the forest disturbance detection demonstrate that the TSA is capable to systematically delineate disturbed forest areas. Information derived from such a forest monitoring tool is highly relevant for various stakeholders in the forestry sector, either for forest management purposes or for decision-making processes on different levels.

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“…However, accuracy and uncertainty assessments of these datasets in relationship to each other are challenging and limited in scope (Congalton and Mead 1986;Congalton 1991;Thomlinson et al 1999;Foody 2002;Nelson et al 2005;Ozdogan and Gutman 2008;Machwitz et al 2010;Pervez and Brown 2010;Wardlow and Egbert 2010;Lechner et al 2012). It is important to compare and contrast these existing irrigated land datasets to understand them better so that users can select the most suitable dataset or combination of datasets for specific research purposes and applications (Newton et al 2009;Selkowitz and Stehman 2011;Thomas et al 2011;Garc ıa-Mora et al 2012;Zheng et al 2012). Evaluations of multiresolution, time series datasets of land change, including irrigated land, are important to landscape change analysis (Foody 2002;Wulder et al 2004;Csillag and Boots 2005;Boots and Csillag 2006;White 2006;Kuzera and Pontius 2008;Goetz et al 2009;Barbier et al 2010;Lhermitte et al 2011;Rozenstein and Karnieli 2011).…”

Section: Introductionmentioning

confidence: 99%

Case Study Comparing Multiple Irrigated Land Datasets in Arizona and Colorado, USA

Shi

Auch

Vogelmann

et al. 2018

J American Water Resour Assoc

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While there are currently a number of irrigated land datasets available for the western United States (U.S.), there is uncertainty regarding in how they relate to each other. To help understand the characteristics of available irrigated datasets, we compared (1) the Cropland Data Layer (CDL), (2) Moderate Resolution Imaging Spectroradiometer Irrigated Agriculture Dataset (IAD), (3) Digitized Irrigated Land (DIL), and (4) Consumptive Use for Irrigation (CUI) data in Arizona and Colorado, U.S. These datasets were derived from multiple sources at various spatial resolutions and temporal scales. We found spatial and temporal trends among all of them. The datasets showed decreases in irrigated land area in Arizona during the 2000–2010 time period. The change ranges and ratios were similar in all Arizona datasets. Irrigated land in Colorado decreased in DIL and CUI but increased in IAD and CDL. The agreement within the same type of dataset during different time periods was from 60% to 80% (R2 from 0.35 to 0.72) in Arizona and from 50% to 80% (R2 from 0.23 to 0.68) in Colorado. DIL had the highest agreement (80%) in both states. The agreement among different datasets acquired at approximately the same time frame ranged from 51% to 63% (R2 from 0.14 to 0.31) in Arizona and from 47% to 69% (R2 from 0.32 to 0.40) in Colorado. The results from this study support a greater understanding of the multiresolution and multitemporal nature of these datasets for various applications.

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Land cover mapping applications with MODIS: a literature review

Cited by 64 publications

References 5 publications

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

Case Study Comparing Multiple Irrigated Land Datasets in Arizona and Colorado, USA

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