# **_3.2 Outcome Variables_**

Bands from Landsat 7 imagery, accessed and processed using the GEE developer platform (GEE 2017),<sup>10</sup> are used to construct pixel-level averages of NDVI, SAVI, and LSWI for each year and season.<sup>11</sup> At a pixel size of 30m x 30m, the average micro-watershed is covered by 5,951 pixels, well above the 84 pixels that would be available if we used MODIS.<sup>12</sup> With Landsat’s revisit frequency of 16 days, this yields a maximum of 7–8 observations per season, though the actual value may be lower owing to cloud cover and failure of the satellite’s <mark>scan line corrector after May 31, 2003.</mark><sup>13</sup> Before computing these indices, band values are converted to top-of-atmosphere reflectance (Zhu and Woodcock 2012), and a masking routine (Zhu et al. 2015) is used to exclude pixels “contaminated” by clouds, shadows, water, etc.). We assume that missing values are randomly distributed and thus enter the error term rather than biasing our estimates.

We used archived imagery of consistent quality during the pre-intervention period to obtain information for the 12-year period starting in 2005 with the dry season (Oct. 2004-Jan. 2005) and ending in 2016 with the rainy season (June–Sep.). Control areas were defined by drawing a 5-km buffer around critical TBIWRD watersheds (see figure 4). With some 2.8 million pixels having valid data per season, the quantity was too large to process; even aggregating biweekly observations to seasonal averages for the three annual seasons in each of the 12 years covered would result in about 100 million observations.

Summary statistics of outcome variables for control and treatment areas in pre- and post-treatment years (table 2) illustrate three observations. First, for all indicators inter-season variability is high, with peak values in the rainy season, followed by the dry and the pre-rainy season. Variation is highest for the NDVI, followed by the LSWI -the only index with marginally negative values, which indicates wilting of leaves in the pre-rainy season- and the SAVI. Second values of the indices for the project area are marginally above those for control areas throughout. Finally, discerning different trends from the data in the table requires greater disaggregation.

Nonparametric kernel-weighted local polynomial regressions against time (figure 5) point to a positive trend in all outcome indicators for control and treatment areas. They also support the notion of parallel trends in the pre-intervention period and divergence between treatment and control areas thereafter in a way

> 10<sup>GEE is a cloud-based platform used to access and process large collections of satellite imagery. Given the volume of raw imagery and</sup> computational power necessary, without GEE it would have been very difficult, if not impossible, to conduct this type of analysis. Once processed in GEE, the data and variables of interested are exported as individual raster files and a batch process is used to extract pixel-level data. to facilitate merging across data sets Each pixel is assigned a unique identifier based on longitude and latitude.

> 11<sup>Beforehand, band values are converted to top-of-atmosphere reflectance using a masking routine (Zhu and Woodcock 2014) to exclude</sup> “contaminated” pixels. such as clouds, shadows. and water cover, that can result in unreliable values. Also filtered out in the masking process are problematic bands appearing in LS7 imagery due to a malfunctioning scan line corrector in the LS7 satellite, which renders about 22 percent of the data unusable (Irons, Dwyer, and Barsi 2012).

> 12 While the MODIS two-day revisit frequency provides higher temporal resolution, we think the spatial resolution more than compensates for this.

> 13 The masking process filters out the problematic bands in LS7 imagery discussed above in footnote 11 (Irons et al _._ 2012).

10