Environmental controls on Landsat‐derived phenoregions across an East African megatransect

Abstract

Semiarid and savanna‐type (SAST) ecosystems in East Africa have unique plant species compositions and characteristics that make quantifying this biome's seasonality and interannual variability difficult. Phenoregion classification offers a way to use seasonality of vegetation growth to help understand the phenological spatial patterns of complex landscapes. Here, we used Normalized Difference Vegetation Index (NDVI) time series from Landsat 8 to map phenoregions in scenes centered around national parks from Mt. Kenya National Park (Kenya) to Limpopo National Park (Mozambique). We then assessed whether landscape‐scale controls on phenology are consistent across the region or whether they vary across this latitudinal gradient. We compared our phenoregion maps to MODIS Land Cover and geology, and we used multinomial logistic regression to determine the role that elevation, slope, aspect, and geology play in driving phenological differences. The sites' phenoregions showed no unique land cover composition, suggesting that MODIS land cover does not capture the subtle variations identified in phenoregion analysis. Multinomial logistic regression showed that geographic trend (x‐ and y‐directions) was a strong predictor in four of the five landscapes and that, depending on the scene, geology, elevation, or aspect was a strong secondary predictor. Using seasonality of the NDVI time series to generate phenoregions provides different and, in some cases, more ecologically relevant information, compared to vegetation maps that use only land cover from a single season or time period to generate ecoregions. With a significant population of humans and animals that live in and depend on SAST ecosystems, it is important to better understand vegetation processes and factors that affect them as climate change becomes an increasingly pertinent issue in dry systems.

Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains

“…Here, we consider NDVI data derived from the LandSat 8 sensor (which provides a ∼30×30 m spatial resolution pixel) taken over Serengeti National Park, Tanzania, Africa. These data were part of a larger study conducted by Desanker, Dahlin, and Finley (2020) that looked at environmental drivers in vegetation phonology change. The data cover an area of 30 km × 30 km and 34 months, and correspond to 64 images of size 1000×1000 collected at 16-day intervals.…”

Exploring the Drivers of Sentinel-2-Derived Crop Phenology: The Joint Role of Climate, Soil, and Land Use

“…As the impacts of climate change intensify, the need to understand the functioning of the agro-ecosystems has stimulated scientific communities to elucidate environmental controls on vegetation dynamics [5]. Phenology variables are indicated as some of the most sensitive data to climate conditions, and therefore represent key indicators of crop growth and development and play an important role in vegetation monitoring [6].…”