Mapping Total Vegetation Cover Across Western Rangelands With Moderate-Resolution Imaging Spectroradiometer Data

Abstract. Millions of dollars have been spent on brush management, or removal of unwanted woody vegetation, as a conservation practice to control the presence of woody species. Land managers need an inexpensive means of monitoring the effects of brush management conservation methods for decreasing degradation in rangeland systems. In this study, free, publically available, high-resolution (1 m) imagery from the National Agricultural Imagery Program (NAIP) and moderate-resolution (30 m) Landsat-5 Thematic Mapper (TM) imagery were combined to produce a large-scale technique for mapping woody cover. High-resolution imagerybased estimates of woody cover were found to be reasonable (RMSE ¼ 3.8%, MAE ¼ 2.9%) surrogates for ground-based woody cover. An equation for TM-derived woody cover was developed. TM scenes of woody cover (TM WC ) were produced and validated using NAIP and ground-based data. Results showed that the developed relation produced viable (RMSE ¼ 8.5%, MAE ¼ 6.4%) maps of woody cover that could be used to successfully track the occurrence of brush removal, as well as monitor the presence or lack of subsequent reemergence. This work provides land managers with an operational means of determining where to allocate resources to implement brush management, as well as a cost-effective method of monitoring the effects of their efforts.

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“…[63][64][65] In this study, a strong (R 2 ¼ 0.92) linear relationship was found between TM MSAVI and NAIP WC (Fig. 7).…”

Section: Resultssupporting

confidence: 57%

Development of an integrated multiplatform approach for assessing brush management conservation efforts in semiarid rangelands

Collins

Kautz

Tiller

et al. 2015

J. Appl. Remote Sens

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“…The Land Surface Water Index (LSWI; Equation (2)) given its sensitivity to water or equivalent water thickness and leaf moisture has been successfully applied for mapping inundation, forest characteristics, and agricultural landscapes [39]. The Soil-Adjusted Total Vegetation Index (SATVI; Equation (3)) has demonstrated utility in mapping senescent biomass, ground residue, plant litter, and surface conditions while compensating for varying soil brightness and background artifacts [40].…”

Section: Palsar-2mentioning

confidence: 99%

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

et al. 2017

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Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km 2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R 2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs.

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“…The Normalized Difference Till Index (NDTI; Equation (3)) was used for its sensitivity to residue and crop management practices [34]. The Soil-Adjusted Total Vegetation Index (SATVI; Equation (4)) has demonstrated utility in mapping senescent biomass, ground residue, plant litter, and surface conditions while compensating for varying soil brightness and background artifacts [35].…”

Section: Landsat-olimentioning

confidence: 99%

Regional Mapping of Plantation Extent Using Multisensor Imagery

et al. 2016

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Industrial forest plantations are expanding rapidly across Monsoon Asia and monitoring extent is critical for understanding environmental and socioeconomic impacts. In this study, new, multisensor imagery were evaluated and integrated to extract the strengths of each sensor for mapping plantation extent at regional scales. Two distinctly different landscapes with multiple plantation types were chosen to consider scalability and transferability. These were Tanintharyi, Myanmar and West Kalimantan, Indonesia. Landsat-8 Operational Land Imager (OLI), Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2), and Sentinel-1A images were fused within a Classification and Regression Tree (CART) framework using random forest and high-resolution surveys. Multi-criteria evaluations showed both L-and C-band gamma nought γ˝backscatter decibel (dB), Landsat reflectance ρ λ , and texture indices were useful for distinguishing oil palm and rubber plantations from other land types. The classification approach identified 750,822 ha or 23% of the Taninathryi, Myanmar, and 216,086 ha or 25% of western West Kalimantan as plantation with very high cross validation accuracy. The mapping approach was scalable and transferred well across the different geographies and plantation types. As archives for Sentinel-1, Landsat-8, and PALSAR-2 continue to grow, mapping plantation extent and dynamics at moderate resolution over large regions should be feasible.

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Mapping Total Vegetation Cover Across Western Rangelands With Moderate-Resolution Imaging Spectroradiometer Data

Cited by 36 publications

References 23 publications

Development of an integrated multiplatform approach for assessing brush management conservation efforts in semiarid rangelands

Development of an integrated multiplatform approach for assessing brush management conservation efforts in semiarid rangelands

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

Regional Mapping of Plantation Extent Using Multisensor Imagery

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